20\n14 (28)\nTumor size (cm)\n≤5\n7 (38.9)\n.80\n.87 (.28–2.68)\n>5\n16 (35.6)\nBackground liver\nWithout cirrhosis\n12 (38.7)\n.72\n.83 (.30–2.32)\nWith cirrhosis\n11 (34.4)\nGrade\nI-II\n15 (34.9)\n.70\n1.24 (.42–3.71)\nIII-IV\n8 (40)\nMetastasis\nWithout\n15 (42.9)\n.24\n.53 (.185–1.54)\nWith\n8 (28.6)\nCI: confidence interval, HCC: hepatocellular carcinoma, OR: odds ratio, and ZHX2: zinc fingers and homeoboxes 2.Figure 1\nexpression of zinc fingers and homeoboxes 2 (ZHX2), nuclear factor-YA (NF-YA), and alpha-fetal protein (AFP) mRNA in hepatocellular carcinoma (HCC) tissues. The target mRNA expression was detected by RT-PCR analysis. The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as an internal control. In cases 1, 8, 15, 47, and 49, ZHX2 and NF-YA mRNAs were detected, but AFP mRNA expression was not observed. In cases 5, 12, 46, and 48, ZHX2 and NF-YA mRNAs were not detected, but AFP mRNA expression was observed.NF-YA mRNA expression was detected in 27 (42.9%) of 63 HCC tissues (Figure1). It was significantly negatively correlated with serum AFP concentration (P=.005, OR=.16). NF-YA expression was not significantly associated statistically with age, tumor size, cirrhosis, grading, and metastasis (Table 3).Table 3\nRelationship between NF-YA mRNA expression and clinicopathological parameters in HCC tissues.\nParameter\n+ (%)\nNF-YA mRNAP value\nOR (95% CI)\nTotal\n27 (42.9)\nAFP (μg/L)\n<20\n10 (76.9)\n.005\n.16 (.04–.64)\n>20\n17 (34)\nTumor size (cm)\n≤5\n7 (38.9)\n.69\n1.26 (.41–3.84)\n>5\n20 (44.4)\nBackground liver\nWithout cirrhosis\n15 (48.4)\n.38\n.64 (.23–1.75)\nWith cirrhosis\n12 (37.5)\nGrade\nI-II\n16 (37.2)\n.18\n2.06 (.70–6.05)\nIII-IV\n11 (55)\nMetastasis\nWithout\n18 (51.4)\n.27\n.56 (.20–1.57)\nWith\n9 (32.1)\nCI: confidence interval, HCC: hepatocellular carcinoma, NF-Y: nuclear factor-Y, and OR: odds ratio.AFP mRNA expression was detected in 38 (60.3%) of 63 HCC tissues (Figure1). It was not significantly associated with age, tumor size, cirrhosis, grading, and metastasis. However, there was a significant association between AFP mRNA expression and serum AFP concentration (P=.001, OR=14.14) (Table 4).Table 4\nRelationship between AFP mRNA expression and clinicopathological parameters in HCC tissues.\nParameter\n+ (%)\nAFP mRNAP value\nOR (95% CI)\nTotal\n38 (60.3)\nAFP (μg/L)\n<20\n2 (15.4)\n.001\n14.14 (2.78–72.05)\n>20\n36 (72)\nTumor size (cm)\n≤5\n9 (50)\n.29\n1.81 (.60–5.49)\n>5\n29 (64.4)\nBackground liver\nWithout cirrhosis\n22 (71)\n.09\n.41 (.15–1.16)\nWith cirrhosis\n16 (50)\nGrade\nI-II\n29 (67.4)\n.09\n.40 (.13–1.17)\nIII-IV\n9 (45)\nMetastasis\nWithout\n21 (60)\n.95\n1.03 (.37–2.85)\nWith\n17 (60.7)\nAFP: alpha-fetoprotein, CI: confidence interval, HCC: hepatocellular carcinoma, and OR: odds ratio.ZHX2 mRNA expression rate (26.3%) in HCC tissues with AFP mRNA expression was significantly lower than that (52%) without AFP mRNA expression (P=.04, OR=.33). In 27 HCC tissues with NF-YA expression, ZHX2 mRNA expression rate was 74.1%. In 36 NF-YA negative HCC tissues, only 3 ZHX2-positive tissues (8.3%) were detected. The difference was statistically significant (P=.001, OR=31.43) (Table 5). Furthermore, NF-YA expression rate (31.6%) in HCC tissues with AFP expression was significantly lower than that (60%) without AFP expression (P=.03, OR=.31) (Table 6).Table 5\nRelationship between ZHX2, NF-YA, and AFP mRNA expressions in HCC tissues.\n+ (%)\nZHX2 mRNAP value\nOR (95% CI)\nNF-YA mRNA\n−\n3 (8.3)\n.001\n31.43 (7.28–135.62)\n+\n20 (74.1)\nAFP mRNA\n−\n13 (52)\n.04\n.33 (.11–.96)\n+\n10 (26.3)\nAFP: alpha-fetoprotein, CI: confidence interval, HCC: hepatocellular carcinoma, NF-Y: nuclear factor-Y, OR: odds ratio, and ZHX2: zinc fingers and homeoboxes 2.Table 6\nRelationship between NF-YA and AFP mRNA expression in HCC tissues.\n+ (%)\nNF-YA mRNAP value\nOR (95% CI)\nAFP mRNA\n−\n15 (60)\n.03\n.38 (.11–.88)\n+\n12 (31.6)\nAFP: alpha-fetoprotein, CI: confidence interval, HCC: hepatocellular carcinoma, NF-Y: nuclear factor-Y, and OR: odds ratio.To further confirm the relations between ZHX2, NF-YA, and AFP, we used siRNA to silence the expression of ZHX2 and then detected the change of NF-YA and AFP expression in HepG2 cells. After transfection of siRNA into the cells, the level of ZHX2 mRNA and protein expression decreased significantly by 85% and 83%, respectively, as compared to control siRNA. Treatment with ZHX2 siRNA simultaneously led to 60% and 61% reduction in the NF-YA mRNA and protein levels in the cells, respectively. Downregulation of ZHX2 also induced a 2.4-fold increase in both AFP mRNA and protein levels (Figures2 and 3).Figure 2\nexpression of zinc fingers and homeoboxes 2 (ZHX2), nuclear factor-YA (NF-YA), and alpha-fetal protein (AFP) mRNA in HepG2 cells after siRNA transfection. The target mRNA expression was detected by RT-PCR analysis. The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as an internal control. The amounts of mRNA in the ZHX2 siRNA-treated cells were compared to those of control cells. After siRNA transfection, ZHX2 mRNA expression (389 bp) was decreased by 85%, NF-YA mRNA expression (117 bp) was reduced by 60%, and AFP mRNA expression (240 bp) was increased by 2.4-fold.Figure 3\nExpression of zinc fingers and homeoboxes 2 (ZHX2), nuclear factor-YA (NF-YA), and alpha-fetal protein (AFP) in HepG2 cells after siRNA transfection. The target protein expression was detected by Western blot analysis. The expression ofβ-actin was analyzed as an internal control. The amounts of protein in the ZHX2 siRNA-treated cells were compared to those of control cells. After siRNA transfection, ZHX2 protein expression (92KD) was decreased by 83%, NF-YA protein expression (40 KD and 43 KD) was reduced by 61%, and AFP protein expression (70 KD) was increased by 2.4-fold.\n\n\n## 4. Discussion\n\nZHX2 is a novel transcriptional repressor, which consists of 837 amino acid residues. The protein has two Cys2-His2-type zinc finger motifs and five homeodomains and is localized in the nuclei [15]. We previously found that promoter hypermethylation caused a low mRNA expression of ZHX2 in HCC. In this study, ZHX2 mRNA expression rate in HCC tissues was 36.5% and similar to the previous study (34.4%) [9]. Compared with clinicopathological parameters, ZHX2 mRNA expression was negatively associated with preoperative AFP level in serum. We also detected AFP mRNA expression in HCC tissues by RT-PCR, which was found to be lower in HCC tissues with ZHX2 expression. Taken together, these findings confirm a negative correlation between ZHX2 and AFP expressions in HCC. To investigate the regulation of AFP, we used RNAi technique because it is known to downregulate specific gene at a posttranscriptional level [21]. We found that the transfection of siRNA into HepG2 cells caused a silence of ZHX2 and an increased expression of AFP mRNA and protein. Yamada et al. also demonstrated that the promoter activity of alpha-fetoprotein was repressed by the expression of ZHX2 in HLE hepatoma cells in a dose-dependent manner [22]. They concluded that ZHX2 and ZHX3 were involved in the transcriptional repression of the HCC markers in normal hepatocytes, suggesting that the failure of the ZHX2 and/or ZHX3 expression might be a critical factor in hepatocyte carcinogenesis [22].The interaction of ZHX2 with the serine/threonine-rich AD of NF-YA has been previously confirmed [15]. By RT-PCR analysis, we found that ZHX2 expression was positively correlated with NF-YA expression. Treatment with ZHX2 siRNA led to 60% and 61% reduction in the NF-YA mRNA and protein levels, respectively. These results indicated that ZHX2 was correlated to NF-YA in HCC. It is possible that ZHX2 downregulates gene expression via the interaction with NF-YA in HCC.Furthermore, we analyzed NF-YA and AFP expressions in HCC tissues and found that NF-YA expression was negatively correlated to AFP expression and serum AFP level. Treatment with ZHX2 siRNA could lead to decreased NF-YA and increased AFP at the mRNA and protein levels. Thus, it is possible that ZHX2 regulates AFP transcription via the interaction with NF-YA in HCC. Although there is no evidence to show that NF-YA directly interacts with AFP, NF-YA might regulate AFP expression indirectly through other genes, such as p300 [14] and p53 [23]. Further studies are required to explain the exact mechanism(s) of AFP regulation in HCC.In conclusion, we detected ZHX2, NF-YA, and AFP expressions in human HCC tissues by RT-PCR and found a close correlation between ZHX2 and NF-YA and a negative relation between ZHX2 and AFP. The RNAi of ZHX2 in HepG2 cells further verified these relations. Therefore, AFP can be regulated by ZHX2 in HCC, and this regulation may be via the interaction with NF-YA. ZHX2 may be used as an adjuvant diagnostic tissue marker for AFP-negative HCC.Further study is required to detect the expression levels of ZHX2 and AFP in hepatic cirrhosis and dysplastic nodules, in order to understand the expression difference in both lesions and establish the possibility of ZHX2 as an earlier screening marker for patients with cirrhosis and/or preneoplastic nodule.\n\n\n\n---\n\n*Source: 101083-2013-02-26.xml*"},"year":{"kind":"string","value":"2013"}}},{"rowIdx":99,"cells":{"text":{"kind":"string","value":"# Impacts of Different Physical Parameterization Configurations on Widespread Heavy Rain Forecast over the Northern Area of Vietnam in WRF-ARW Model\n\n**Authors:** Tien Du Duc; Cuong Hoang Duc; Lars Robert Hole; Lam Hoang; Huyen Luong Thi Thanh; Hung Mai Khanh\n\n**Journal:** Advances in Meteorology\n (2019)\n\n\n**Publisher:** Hindawi\n\n**License:** http://creativecommons.org/licenses/by/4.0/\n\n**DOI:** 10.1155/2019/1010858\n\n\n---\n\n## Abstract\n\nThis study investigates the impacts of different physical parameterization schemes in the Weather Research and Forecasting model with the ARW dynamical core (WRF-ARW model) on the forecasts of heavy rainfall over the northern part of Vietnam (Bac Bo area). Various physical model configurations generated from different typical cumulus, shortwave radiation, and boundary layer and from simple to complex cloud microphysics schemes are examined and verified for the cases of extreme heavy rainfall during 2012–2016. It is found that the most skilled forecasts come from the Kain–Fritsch (KF) scheme. However, relating to the different causes of the heavy rainfall events, the forecast cycles using the Betts–Miller–Janjic (BMJ) scheme show better skills for tropical cyclones or slowly moving surface low-pressure system situations compared to KF scheme experiments. Most of the sensitivities to KF scheme experiments are related to boundary layer schemes. Both configurations using KF or BMJ schemes show that more complex cloud microphysics schemes can also improve the heavy rain forecast with the WRF-ARW model for the Bac Bo area of Vietnam.\n\n\n---\n\n## Body\n\n## 1. Introduction\n\nElongated to 15 degrees (from 8 to 23 degrees north), Vietnam weather is affected by a variety of extratropical and tropical systems such as cold surges and cold fronts, subtropical troughs and monsoon, tropical disturbances, and typhoons [1–3, 4]. An approximate number of 28–30 heavy rain events occurring over the whole Vietnam region every year were determined from local surface stations. According to Nguyen et al. [4], the rainy season in the Vietnam area usually lasts from May to October with the peak rainy months starting in the north and then moving to the south with time because of the movement of the subtropical ridge and Intertropical Convergence Zone (ITCZ). In the northern part of Vietnam, the peak rainy months can be seen during the June–September period, particularly in July and August, which is partly related to the activity of the ITCZ [5].Apart from single weather patterns causing heavy rain for Vietnam, it is common to observe a mix of different patterns simultaneously. A good example for this is the historical rain and flood over Central Vietnam in the Hue city in 1999 due to the combination of cold surges and tropical depression-type disturbances [6]. In 2008, extreme heavy rain was witnessed in Hanoi as a result of the dramatic intensification of the easterly disturbance through the midlatitude-tropical interactions [7]. Likewise, a surface low pressure which coincided with the subtropical upper-level trough was the main factor causing historical rainfall over northeastern Vietnam in Quang Ninh Province in 2015 [8].In the northern part of Vietnam (hereinafter referred to as Bac Bo area)—the focus area of this study—tropical cyclones or surface low-pressure patterns are one among several key factors leading to heavy rain for different regions in Vietnam. ITCZ plays the vital role in facilitating the development of low-level vortexes which are likely to develop into tropical disturbances and typhoons in the summer time, particularly in July and August. As a result, rain may fall over the Bac Bo area with excessive amount, and rain duration directly depends upon the lifetime of ITCZ and the disturbances itself. The other causing reasons are related to the effects of cold surge from the north (Siberian area) or the combinations of different patterns (a cold surge with a tropical cyclone, a cold surge with a trough, a surface low-pressure system with a trough, etc.). Table1 provides the observed precipitation from SYNOP stations in the Bac Bo area with the annual rainfall mostly ranging from 1500 to 2000 mm and a high number of daily heavy rain occurrences (over 50 mm/24 h and 100 mm/24 h).Table 1\nStation information over the northern part of Vietnam, annual rainfall (over the period 1998–2018) in mm, and the number of days with daily accumulated rainfall over 50 mm/24 h (#50 mm) and over 100 mm/24 h (#100 m) in average. The number of samples is ∼7300 each station.\nStation name\nLong.\nLat.\nAnnual rain\n#50 mm\n#100 mm\nMuong Te\n102.83\n22.37\n2423\n11\n2\nSin Ho\n103.23\n22.37\n2787\n13\n2\nTam Duong\n103.48\n22.42\n2355\n9\n2\nMuong La\n104.03\n21.52\n1412\n5\n1\nThan Uyen\n103.88\n21.95\n1920\n8\n1\nQuynh Nhai\n103.57\n21.85\n1678\n7\n1\nMu Cang Chai\n104.05\n21.87\n1741\n5\n1\nTuan Giao\n103.42\n21.58\n1586\n5\n1\nPha Din\n103.5\n21.57\n1840\n6\n1\nVan Chan\n104.52\n21.58\n1481\n6\n1\nSong Ma\n103.75\n21.5\n1118\n2\n0\nCo Noi\n104.15\n21.13\n1308\n4\n0\nYen Chau\n104.3\n21.05\n1229\n3\n0\nBac Yen\n104.42\n21.23\n1519\n4\n0\nPhu Yen\n104.63\n21.27\n1496\n5\n1\nMinh Dai\n105.05\n21.17\n1695\n6\n1\nMoc Chau\n104.68\n20.83\n1581\n5\n1\nMai Chau\n105.05\n20.65\n1698\n7\n1\nPho Rang\n104.47\n22.23\n1648\n7\n1\nBac Ha\n104.28\n22.53\n1611\n4\n0\nHoang Su Phi\n104.68\n22.75\n1739\n5\n1\nBac Me\n105.37\n22.73\n1753\n6\n0\nBao Lac\n105.67\n22.95\n1179\n3\n0\nBac Quang\n104.87\n22.5\n4295\n24\n9\nLuc Yen\n104.78\n22.1\n1917\n8\n1\nHam Yen\n105.03\n22.07\n1858\n8\n1\nChiem Hoa\n105.27\n22.15\n1631\n6\n1\nCho Ra\n105.73\n22.45\n1417\n5\n0\nNguyen Binh\n105.9\n22.65\n1686\n6\n1\nNgan Son\n105.98\n22.43\n1853\n8\n1\nTrung Khanh\n106.57\n22.83\n1821\n7\n2\nDinh Hoa\n105.63\n21.92\n1749\n8\n2\nBac Son\n106.32\n21.9\n1693\n7\n2\nHuu Lung\n106.35\n21.5\n1572\n7\n1\nDinh Lap\n107.1\n21.53\n1865\n7\n2\nQuang Ha\n107.75\n21.45\n2883\n16\n5\nPhu Ho\n105.23\n21.45\n1480\n6\n1\nTam Dao\n105.65\n21.47\n2585\n13\n4\nHiep Hoa\n105.97\n21.35\n1601\n6\n1\nBac Ninh\n106.08\n21.18\n1608\n7\n2\nLuc Ngan\n106.55\n21.38\n1406\n6\n1\nSon Dong\n106.85\n21.33\n1686\n8\n2\nBa Vi\n105.42\n21.15\n1942\n9\n2\nHa Dong\n105.75\n20.97\n1635\n7\n1\nChi Linh\n106.38\n21.08\n1536\n7\n1\nUong Bi\n106.75\n21.03\n1824\n9\n2\nKim Boi\n105.53\n20.33\n2115\n9\n2\nChi Ne\n105.78\n20.48\n1841\n8\n2\nLac Son\n105.45\n20.45\n2040\n9\n2\nCuc Phuong\n105.72\n20.25\n1918\n9\n2\nYen Dinh\n105.67\n19.98\n1509\n7\n1\nSam Son\n105.9\n19.75\n1759\n9\n3\nDo Luong\n105.3\n18.89\n1871\n9\n2\nLai Chau\n103.15\n22.07\n2178\n10\n2\nSa Pa\n103.82\n22.35\n2581\n10\n2\nLao Cai\n103.97\n22.5\n1704\n7\n1\nHa Giang\n104.97\n22.82\n2372\n12\n2\nSon La\n103.9\n21.33\n1456\n4\n1\nThat Khe\n106.47\n22.25\n1482\n6\n1\nCao Bang\n106.25\n22.67\n1439\n6\n1\nBac Giang\n106.22\n21.3\n1518\n7\n1\nHon Ngu\n105.77\n18.8\n2007\n11\n4\nBac Can\n105.83\n22.15\n1421\n5\n1\nDien Bien Phu\n103\n21.37\n1536\n5\n1\nTuyen Quang\n105.22\n21.82\n1654\n8\n2\nViet Tri\n105.42\n21.3\n1601\n8\n2\nVinh Yen\n105.6\n21.32\n1489\n6\n1\nYen Bai\n104.87\n21.7\n1768\n8\n1\nSon Tay\n105.5\n21.13\n1612\n7\n1\nHoa Binh\n105.33\n20.82\n1828\n8\n2\nHuong Son\n105.43\n18.52\n2135\n10\n4\nHa Noi\n105.8\n21.03\n1647\n8\n2\nPhu Ly\n105.92\n20.55\n1731\n8\n2\nHung Yen\n106.05\n20.65\n1381\n6\n1\nNam Dinh\n106.15\n20.39\n1564\n7\n1\nNinh Binh\n105.97\n20.23\n1662\n8\n2\nPhu Lien\n106.63\n20.8\n1572\n7\n1\nHai Duong\n106.3\n20.93\n1561\n7\n1\nHon Dau\n106.8\n20.67\n1501\n8\n1\nVan Ly\n106.3\n20.12\n1620\n9\n2\nLang Son\n106.77\n21.83\n2874\n12\n2\nThai Nguyen\n105.83\n21.6\n1266\n5\n1\nNho Quan\n105.73\n20.32\n1728\n8\n2\nBai Chay\n107.07\n20.97\n1700\n8\n2\nCo To\n107.77\n20.98\n1933\n10\n3\nThai Binh\n106.35\n20.45\n1930\n10\n3\nCua Ong\n107.35\n21.02\n1591\n8\n1\nTien Yen\n107.4\n21.33\n2189\n12\n3\nMong Cai\n107.97\n21.52\n2168\n10\n3\nBach Long Vi\n107.72\n20.13\n2670\n15\n5\nHuong Khe\n105.72\n18.18\n1216\n6\n1\nThanh Hoa\n105.78\n19.75\n2516\n12\n5\nHoi Xuan\n105.12\n20.37\n1701\n8\n3\nTuong Duong\n104.43\n19.28\n1720\n6\n1\nVinh\n105.7\n18.67\n1305\n5\n1\nHa Tinh\n105.9\n18.35\n1896\n10\n4\nKy Anh\n106.28\n18.07\n2507\n13\n5\nBai Thuong\n105.38\n19.9\n1830\n7\n2\nNhu Xuan\n105.57\n19.63\n1758\n8\n3\nTinh Gia\n105.78\n19.45\n1777\n9\n3\nQuy Chau\n105.12\n19.57\n1661\n7\n1\nQuy Hop\n105.15\n19.32\n1579\n7\n2\nTay Hieu\n105.4\n19.32\n1517\n7\n2\nQuynh Luu\n105.63\n19.17\n1559\n8\n2\nCon Cuong\n104.88\n19.05\n1647\n7\n2In Vietnam National Center for Hydro-Meteorological Forecasting (NCHMF), several global model (NWP) products are mostly applied in operational forecast to predict the occurrence of heavy rain. These include the models from National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), and Germany’s National Meteorological Service (DWD). In addition, some regional NWP products including the High Resolution Regional Model (HRM) (the HRM’s information can be found at DWD’s internet linkhttps://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/hrm/HRM_users_guide.pdf) [9], the Consortium for Small-Scale Modeling (COSMO) [10] models from DWD, and the Weather Research and Forecasting (WRF-ARW) model [11] from the National Center for Atmospheric Research (NCAR) are also a useful reference source in the operational heavy rainfall forecast of Vietnam [12]. Despite the predictability of these models in some certain cases, they may fail to predict extreme events because of several reasons. Lorenz [13] pointed out the three main factors causing uncertainties in NWP are the initial conditions, the imperfection of the models, and the chaos of the atmosphere. While the initial condition problem for NWP can be reduced by data assimilation methods, the imperfection of models, which relates to many subgrid processes, can be alleviated by using proper physical parameterizations. For regional weather forecasting centers with limited capabilities in data assimilation and resource computation to provide the cloud resolved resolution forecast, choosing correct physical parameterization schemes still plays the most important role in downscaling the processes in regional NWP models [14].To illustrate the dependence of heavy rainfall forecast on physical parameterizations, the typical heavy rainfall event relating to the activities of ITCZ over the South China Sea—the East Sea of Vietnam—from 27 to 30 August 2014 in the Bac Bo area was simulated by the WRF-ARW model (see mean sea level pressure analysis of the GFS model in Figure1(a)). The 3-day accumulated rainfall was mostly from 100 mm to 150 mm, and some stations recorded more than 200 mm such as Kim Boi which recorded 229 mm (Hoa Binh Province; marked with a square in Figure 1(b)), Dinh Lap 245 mm (Lang Son Province, the northeast area; marked with a star in Figure 1(b)), and Tam Dao 341 mm (Vinh Phuc Province, center of the domain; marked with a circle in Figure 1(b)).Figure 1\nExample of impact of different physical parameterization schemes on heavy rain forecast with the WRF-ARW model (5 km horizontal resolution) over the Bac Bo area (the northern part of Vietnam), issued at 00UTC 27/08/2014. (a) Analysis of mean sea level pressure from the GFS at 00UTC 27/08/2014. (b) 72 h accumulated precipitation from synoptic observation from 00UTC 27/08/2014 to 00UTC 30/08/2014. 72 h accumulated precipitation forecast from the WRF-ARW model with the configuration of KF cumulus parameterization and shortwave radiation and cloud microphysics schemes: (c) KF-Lin-Duh-MYJ; (d) KF-WSM3-Duh-MYJ; (e) KF-WSM5-God-MYJ; (f) KF-WSM5-God-YSU. 72 h accumulated precipitation forecast from the WRF-ARW model with the configuration of BMJ cumulus parameterization: (g) BMJ-Lin-Duh-MYJ; (h) BMJ-WSM3-Duh-MYJ; (i) BMJ-WSM5-God-MYJ; (j) BMJ-WSM5-God-YSU. More explanation of model configurations from (c) to (j) experiments can be found in Table2.\n(a)\n(b)\n(c)\n(d)\n(e)\n(f)\n(g)\n(h)\n(i)\n(j)Figure1 illustrates 72 h accumulated rainfall forecasts from the WRF-ARW model at 5 km horizontal resolution, issued at 00UTC 27/08/2014 with different physical parameterization configurations by combining the Betts–Miller–Janjic (BMJ) or Kain–Fritsch (KF) cumulus schemes, the Lin or WRF single-moment three- or five-class (WSM3/WSM5) cloud microphysics schemes, the Dudhia or Goddard shortwave radiation schemes, and the Yonsei University (YSU) or Mellor–Yamada–Janjic (MYJ) boundary layer schemes.With the KF cumulus parameterization scheme in Figures1(c)–1(f), the results show that extreme rainfall over the northeast area (Lang Son Province) can be predictive with the amount of rainfall over >150 mm but was forecasted overestimation for the mountainous regions over the northwest area (Hoang Lien Son mountain ranges). The extreme rainfall over the center of the domain (Vinh Phuc Province) was not simulated well using the KF scheme for this situation.For the experiments using the BMJ scheme in Figures1(g)–1(j), the extreme rainfall over the Lang Son Province is underestimated, but cases using Lin or WSM3 for microphysics, Dudhia for shortwave radiation scheme, and the MYJ surface boundary layer (Figure 1(g) and 1(h)) can reduce the overestimation related to Hoang Lien Son mountain ranges in KF scheme experiments. The extreme heavy rainfall over the center of the domain (in Vinh Phuc Province) can be quite well forecasted when using WSM5 with Goddard Hoang Lien Son mountain ranges and the MYJ surface boundary layer (Figure 1(i)). The same configuration in Figure 1(i) but using another surface boundary layer (YSU) scheme provided totally different results (Figure 1(j)) for the southern area of the domain (overestimation).Many studies have been carried out for validating the effects of physical parameterization schemes in the WRF-ARW model. Zeyaeyan et. al. [15] evaluated the effect of various physic schemes in the WRF-ARW model on the simulation of summer rainfall over the northwest of Iran (NWI). The result shows that cumulus schemes are the most sensitive and microphysics schemes are the least sensitive. The comparison between 15 km and 5 km resolution simulations does not show obvious advantages in downscaling. These investigations showed the best results for both the 5 and 15 km resolutions with model configurations from the newer Tiedtke cumulus scheme, MYJ scheme, and WSM3/Kessler microphysics scheme. Tan [16] processed sensitivity tests with microphysics parameterization of the WRF-ARW model in the concept of quantitative extreme precipitation forecasting for hydrological inputs. About 19 bulk microphysics parameterization schemes were evaluated for a storm situation in California in 1997. The most important finding was that the extreme and short-interval simulated precipitation of the WRF-ARW model, which is very important for hydrological forecast input, can be improved by the choice of microphysics schemes. Nasrollahi et al. [17] showed that different features from hurricane (track, intensity, and precipitation) in the WRF-ARW model can be improved with suitable selections of microphysics and cumulus schemes. These results showed that the best simulated precipitation can be achieved by using BMJ cumulus parameterization combined with the WSM5 microphysics scheme, but the hurricane’s track was best estimated by using the Lin or Kessler microphysics option with BMJ cumulus parameterization. Other validations of parameterization of physical processes on the tropical cyclone of Pattanayak et al. [18] with WRF for the NMM dynamical core showed the important role of cumulus parameterization in track forecasts, thereby contributing to rainfall induced by landfall of tropical cyclones.In the other aspects related to using cumulus parameterization schemes at high-resolution simulations, Gilliland et al. [19] compared simulations using different cumulus parameterization schemes in summer-time convective activities. Compared with the model simulations at below 5 km horizontal resolution, which resolve the convection, the study showed that depending on the strength of the synoptic-scale forcing, the use of a cumulus parameterization scheme can still be warrant for representing the effects of subgrid-scale convective processes (the Kain–Fritsch scheme in this study).Thus, the application of regional models to certain regions such as Vietnam will be influenced significantly by local factors (topography and microclimate mode) as well as the effects of physical combinations. Among regional models which are applied in Vietnam, the WRF-ARW model is the most useful tool because of the capabilities of providing different configuration physical/dynamical models for implementation to various scientific communities for both research and operation. For this reason, this study will focus on the impact of some physical parameterization configurations of the WRF-ARW model combining cumulus, cloud microphysics, and shortwave radiation parameterization schemes on heavy rainfall forecast. Two typical cumulus parameterizations (adjustment and mass-flux approaches) will be investigated with the combination of simple to complex cloud microphysics schemes. The verification of dependence of the typical synoptic situations/weather patterns causing heavy rainfall for the Bac Bo area on parameterization schemes is also carried out. For the real-time adaptation forecasting verification purposes, the lateral boundary condition will be used from the Global Forecast System (GFS) of NCEP.The remainder of this paper is organized as follows: In Section2, experimental design and validation methods will be presented. Section 3 discusses the impacts of different physical parameterization configurations on heavy rainfall over the Bac Bo area, and conclusions are given in Section 4.\n\n\n## 2. Experiments\n\n### 2.1. Model Description\n\nThis study used the recently released version of the Weather Research and Forecasting model with the ARW dynamical core (WRF-ARW model; version 3.9.1.1) with multinested grids and two-way interactive options. The WRF model has been integrated many advances in model physics/numerical aspects and data assimilation by scientists and developers from the expansive research community, therefore becoming a very flexible and useful tool for both researchers and operational forecasters (https://www.mmm.ucar.edu/weather-research-and-forecasting-model).For the purpose of investigating the impact of physical parameterization schemes, similarly to the study of Kieu et. al. [20, 21], a set of combination of physical parameterizations has been generated based on (a) the modified KF and BMJ cumulus parameterization schemes; (b) the Goddard and Dudhia schemes for the shortwave radiation; (d) the YSU and MYJ planetary boundary schemes; and (e) the Lin, WSM3, WSM5, and WSM6 schemes for the cloud microphysics. There are a maximum of 32 different configuration forecasts for each heavy rainfall case listed in Table 2. The other options are the Monin–Obukhov surface layer scheme and the Rapid Radiative Transfer Model scheme for longwave radiation. Note that, with the MYJ scheme, the surface layer option will be switched to Janjic’s Eta–Monin–Obukhov scheme which is based on similar theory with viscous sublayers over both solid surfaces and water points. Skamarock et al. [22] provided the detailed description of the WRF-ARW model, and various references for physical parameterizations of the WRF-ARW model can be found from various listed references [11, 23–29, 30].Table 2\nDetails of physical parameterization configurations in different experiments.\nAbbreviation\nMicrophysics\nShortwave radiation\nBoundary layer\nBetts–Miller–Janjic (BMJ) cumulus parameterization\nBMJ-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nBMJ-Lin-Duh-YSU\nLin\nDuhia\nYSU\nBMJ-Lin-God-MYJ\nLin\nGoddard\nMYJ\nBMJ-Lin-God-YSU\nLin\nGoddard\nYSU\nBMJ-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nBMJ-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nBMJ-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nBMJ-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nBMJ-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nBMJ-WSM5-God-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nBMJ-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nBMJ-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nBMJ-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nBMJ-WSM6-God-YSU\nWSM6\nGoddard\nYSU\nKain–Fritsch (KF) cumulus parameterization\nKF-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nKF-Lin-Duh-YSU\nLin\nDuhia\nYSU\nKF-Lin-God-MYJ\nLin\nGoddard\nMYJ\nKF-Lin-God-YSU\nLin\nGoddard\nYSU\nKF-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nKF-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nKF-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nKF-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nKF-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nKF-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nKF-WSM5-God-MYJ\nWSM5\nGoddard\nMYJ\nKF-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nKF-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nKF-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nKF-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nKF-WSM6-God-YSU\nWSM6\nGoddard\nYSUThe WRF-ARW model is configured with two nested grid domains consisting of 199 × 199 grid points in the (x, y) dimensions with horizontal resolutions of 15 km (denoted as d01 domain) and 5 km (denoted as d02 domain). All domains share 41 similar vertical σ levels with the model top at 50 hPa. The higher resolution domain covers the northern part of Vietnam with a time step of 15 seconds. All validations will be carried out with forecasts from the d02 domain. Figure 2 shows the terrain used in d01 and d02 domains.Figure 2\nSYNOP station distribution over Vietnam and closed countries (black dots) and terrain of computing domains for the WRF-ARW model. The blue contour is for the outer domain (d01, 15 km), and dark green shading is for the inner domain (d02, 5 km), illustrated by the Diana meteorological visualization software of the Norwegian Meteorological Institute.\n\n\n### 2.2. Boundary Conditions\n\nThe GFS model of NCEP used to provide boundary conditions for the WRF-ARW model in this study has a 0.5-degree horizontal resolution and be prepared every three hours from 1000 hPa to 1 hPa. The GFS data for this study were downloaded from the Research Data Archive at the National Center for Atmospheric Research via website linkhttps://rda.ucar.edu/datasets/ds335.0. More information on GFS data can be found at https://www.nco.ncep.noaa.gov/pmb/products/gfs/.\n\n\n### 2.3. Observation Data\n\nThe number of observation stations in Vietnam increased from 89 in 1988 to 186 in 2017, with 4 or 8 observations per day (black dots are 8 observations/day for stations in Vietnam and nearby countries in Figure2), but only 24 stations are reported to WMO. The difference in location and topography results in the significant change from one climate to another; therefore, Vietnam has been divided into several climate zones. The highest station density is in the Red River Delta area (the southern part of northern Vietnam) with approximately 1 station per 25 km × 25 km area. The coarsest station density is in the Central Highlands area (latitude between ∼11 N and 16 N) with approximately 1 station per 55 km × 55 km area. On average, the current surface observation network density of Vietnam is about 1 station per 35 km × 35 km for flat regions and 1 station per 50 km × 50 km for mountainous complex regions. In this paper, in order to verify model forecast for the Bac Bo area, we used observation data from the northern SYNOP stations for the period from 2012 to 2016 listed in Table 1.In this study, 72 cases of typical widespread heavy rains which occurred in the northern part of Vietnam in the period 2012–2016 were selected. For each case, the forecasting cycles are chosen so that the 72-hour forecast range can cover the maximum duration of heavy rain episodes.With respect to causes of heavy rainfall events, there are four main categories: (i) activities of ITCZs or troughs: type I, (ii) affected by tropical cyclones or surface low-pressure system (staying at least more than 2 days over the Bac Bo area): type II, (iii) related to the cold surge from the north: type III, and (iv) the complex combinations from different patterns: type IV. The list of forecasted events is given in Table3 with the station name, the maximum value of daily rainfall, and the type of each heavy rainfall case. The sample number of type I, type II, type III, and type IV has 37, 21, 5, and 9 forecast cycles, respectively.Table 3\nList of forecast cycles related to heavy rain cases from 2012 to 2016, the station with maximum daily accumulated rainfall up to 72 h for each cycle, and types of main synoptic situations.\nForecast cycle (year month day hour)\nMaximum 24 h accumulation observation (mm)\nStation with maximum observation\nTypes of rain events\n2012 05 18 00\n37\nBac Yen\nType I\n2012 05 19 00\n44\nLac Son\nType I\n2012 05 20 00\n114\nBac Quang\nType I\n2012 05 21 00\n195\nBac Quang\nType I\n2012 05 22 00\n131\nHa Dong\nType I\n2012 05 23 00\n186\nQuang Ha\nType I\n2012 07 20 00\n41\nBac Quang\nType I\n2012 07 21 00\n24.6\nLai Chau\nType II\n2012 07 22 00\n116\nVinh Yen\nType II\n2012 07 23 00\n75\nVinh\nType II\n2012 07 25 00\n229\nTuyen Quang\nType II\n2012 07 26 00\n104\nHam Yen\nType II\n2012 07 27 00\n112\nBac Quang\nType I\n2012 08 03 00\n58.1\nCon Cuong\nType I\n2012 08 04 00\n34.1\nTinh Gia\nType I\n2012 08 05 00\n70\nLang Son\nType I\n2012 08 06 00\n153\nMuong La\nType I\n2012 08 07 00\n163\nQuang Ha\nType I\n2012 08 13 00\n63.6\nMuong La\nType II\n2012 08 14 00\n64.5\nNho Quan\nType II\n2012 08 15 00\n74\nDo Luong\nType II\n2012 08 16 00\n76\nTam Dao\nType II\n2012 08 31 00\n49.1\nSin Ho\nType IV\n2012 09 01 00\n67\nNhu Xuan\nType IV\n2012 09 02 00\n124\nQuynh Luu\nType IV\n2012 09 03 00\n84\nMoc Chau\nType IV\n2012 09 04 00\n157\nHuong Khe\nType IV\n2012 09 16 00\n151.4\nMuong Te\nType IV\n2012 09 17 00\n104.6\nTam Duong\nType III\n2013 06 19 00\n109\nBac Quang\nType II\n2013 06 20 00\n72.2\nHuong Khe\nType II\n2013 06 21 00\n59\nNam Dinh\nType II\n2013 06 22 00\n218\nHa Tinh\nType II\n2014 08 25 00\n91\nLao Cai\nType II\n2014 08 26 00\n89\nVan Ly\nType II\n2014 08 27 00\n157\nHon Ngu\nType II\n2015 05 18 00\n90\nHoi Xuan\nType III\n2015 05 19 00\n58\nSin Ho\nType III\n2015 05 20 00\n106\nThat Khe\nType III\n2015 05 21 00\n72\nTuyen Quang\nType III\n2015 06 21 00\n124\nLuc Yen\nType II\n2015 06 22 00\n72.7\nDo Luong\nType II\n2015 07 01 00\n84.5\nCao Bang\nType I\n2015 07 02 00\n123.0\nVan Chan\nType I\n2015 07 03 00\n74.0\nHuong Khe\nType I\n2015 07 21 00\n54.5\nHoa Binh\nType I\n2015 07 22 00\n19\nSin Ho\nType I\n2015 07 23 00\n92\nSin Ho\nType I\n2015 07 24 00\n180\nQuynh Nhai\nType I\n2015 07 25 00\n181\nCua Ong\nType I\n2015 07 26 00\n432\nCua Ong\nType I\n2015 07 27 00\n347\nQuang Ha\nType I\n2015 07 28 00\n224\nMong Cai\nType I\n2015 07 29 00\n247\nCua Ong\nType I\n2015 07 30 00\n145\nQuang Ha\nType I\n2015 07 31 00\n239\nQuang Ha\nType I\n2015 08 01 00\n157\nPhu Lien\nType I\n2015 09 18 00\n73\nHai Duong\nType IV\n2015 09 19 00\n78.0\nDinh Hoa\nType IV\n2016 05 20 00\n118\nTinh Gia\nType I\n2016 05 21 00\n107.0\nHa Nam\nType I\n2016 05 22 00\n92\nSa Pa\nType I\n2016 05 23 00\n190.4\nYen Bai\nType I\n2016 07 24 00\n60\nPhu Lien\nType II\n2016 07 25 00\n17\nThai Binh\nType II\n2016 07 26 00\n46\nDinh Hoa\nType II\n2016 07 30 00\n18.1\nMu Cang Chai\nType II\n2016 08 01 00\n19\nSin Ho\nType I\n2016 08 02 00\n150\nNinh Binh\nType I\n2016 08 10 00\n81\nQuynh Nhai\nType I\n2016 08 11 00\n117\nThanh Hoa\nType I\n2016 08 12 00\n87\nTay Hieu\nType I\nType I: activities of trough or ITCZ; type II: affected by tropical cyclone or low-pressure system; type III: related to cold surge from the north; type IV: combinations of different patterns.\n\n\n### 2.4. Validation Methods\n\nBy finding the nearest grids to each station position (listed in Table1), the daily accumulated rainfall for these heavy rainfall cases from WRF-ARW model forecasts can be assigned. The verification scores used in this study are frequency bias (BIAS), probability of detection (POD), false alarm ratio (FAR), threat score (TS), and equitable threat score (ETS). If we denote H for the hit rate of occurred rainfalls (at a given threshold) for both forecast and observation, M for the missed rate of occurred rainfall forecast, and F for the false alarm rate of the forecast, the BIAS, POD, FAR, and TS are calculated by the following equations:(1)BIAS=H+FH+M,perfect value=1,<1=underestimation,>1=overestimation,POD=HH+M,perfect value=1,FAR=FH+F,perfect value=0,TS=HH+M+F,perfect value=1,no skill=0.If we set Hitsrandom = (H + F) (H + M)/T, where T is the sum of H, M, F, and the number of nonoccurred rainfalls for both forecast and observation, the ETS is calculated by(2)ETS=H−HitsrandomH+M+F−Hitsrandom,perfect value=1,no skill<=0.Other meanings of these scores can be found in Wilks’ study [31]. The verification will be carried out for the 5 km domain and for 24 h accumulated rainfall at 24 h, 48 h, and 72 h forecast ranges. Other analysis charts include the histogram of precipitation occurrences at given thresholds (>25 mm/24 h, >50 mm/24 h, and >100 mm/24 h) at the observation stations.\n\n\n## 2.1. Model Description\n\nThis study used the recently released version of the Weather Research and Forecasting model with the ARW dynamical core (WRF-ARW model; version 3.9.1.1) with multinested grids and two-way interactive options. The WRF model has been integrated many advances in model physics/numerical aspects and data assimilation by scientists and developers from the expansive research community, therefore becoming a very flexible and useful tool for both researchers and operational forecasters (https://www.mmm.ucar.edu/weather-research-and-forecasting-model).For the purpose of investigating the impact of physical parameterization schemes, similarly to the study of Kieu et. al. [20, 21], a set of combination of physical parameterizations has been generated based on (a) the modified KF and BMJ cumulus parameterization schemes; (b) the Goddard and Dudhia schemes for the shortwave radiation; (d) the YSU and MYJ planetary boundary schemes; and (e) the Lin, WSM3, WSM5, and WSM6 schemes for the cloud microphysics. There are a maximum of 32 different configuration forecasts for each heavy rainfall case listed in Table 2. The other options are the Monin–Obukhov surface layer scheme and the Rapid Radiative Transfer Model scheme for longwave radiation. Note that, with the MYJ scheme, the surface layer option will be switched to Janjic’s Eta–Monin–Obukhov scheme which is based on similar theory with viscous sublayers over both solid surfaces and water points. Skamarock et al. [22] provided the detailed description of the WRF-ARW model, and various references for physical parameterizations of the WRF-ARW model can be found from various listed references [11, 23–29, 30].Table 2\nDetails of physical parameterization configurations in different experiments.\nAbbreviation\nMicrophysics\nShortwave radiation\nBoundary layer\nBetts–Miller–Janjic (BMJ) cumulus parameterization\nBMJ-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nBMJ-Lin-Duh-YSU\nLin\nDuhia\nYSU\nBMJ-Lin-God-MYJ\nLin\nGoddard\nMYJ\nBMJ-Lin-God-YSU\nLin\nGoddard\nYSU\nBMJ-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nBMJ-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nBMJ-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nBMJ-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nBMJ-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nBMJ-WSM5-God-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nBMJ-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nBMJ-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nBMJ-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nBMJ-WSM6-God-YSU\nWSM6\nGoddard\nYSU\nKain–Fritsch (KF) cumulus parameterization\nKF-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nKF-Lin-Duh-YSU\nLin\nDuhia\nYSU\nKF-Lin-God-MYJ\nLin\nGoddard\nMYJ\nKF-Lin-God-YSU\nLin\nGoddard\nYSU\nKF-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nKF-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nKF-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nKF-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nKF-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nKF-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nKF-WSM5-God-MYJ\nWSM5\nGoddard\nMYJ\nKF-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nKF-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nKF-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nKF-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nKF-WSM6-God-YSU\nWSM6\nGoddard\nYSUThe WRF-ARW model is configured with two nested grid domains consisting of 199 × 199 grid points in the (x, y) dimensions with horizontal resolutions of 15 km (denoted as d01 domain) and 5 km (denoted as d02 domain). All domains share 41 similar vertical σ levels with the model top at 50 hPa. The higher resolution domain covers the northern part of Vietnam with a time step of 15 seconds. All validations will be carried out with forecasts from the d02 domain. Figure 2 shows the terrain used in d01 and d02 domains.Figure 2\nSYNOP station distribution over Vietnam and closed countries (black dots) and terrain of computing domains for the WRF-ARW model. The blue contour is for the outer domain (d01, 15 km), and dark green shading is for the inner domain (d02, 5 km), illustrated by the Diana meteorological visualization software of the Norwegian Meteorological Institute.\n\n\n## 2.2. Boundary Conditions\n\nThe GFS model of NCEP used to provide boundary conditions for the WRF-ARW model in this study has a 0.5-degree horizontal resolution and be prepared every three hours from 1000 hPa to 1 hPa. The GFS data for this study were downloaded from the Research Data Archive at the National Center for Atmospheric Research via website linkhttps://rda.ucar.edu/datasets/ds335.0. More information on GFS data can be found at https://www.nco.ncep.noaa.gov/pmb/products/gfs/.\n\n\n## 2.3. Observation Data\n\nThe number of observation stations in Vietnam increased from 89 in 1988 to 186 in 2017, with 4 or 8 observations per day (black dots are 8 observations/day for stations in Vietnam and nearby countries in Figure2), but only 24 stations are reported to WMO. The difference in location and topography results in the significant change from one climate to another; therefore, Vietnam has been divided into several climate zones. The highest station density is in the Red River Delta area (the southern part of northern Vietnam) with approximately 1 station per 25 km × 25 km area. The coarsest station density is in the Central Highlands area (latitude between ∼11 N and 16 N) with approximately 1 station per 55 km × 55 km area. On average, the current surface observation network density of Vietnam is about 1 station per 35 km × 35 km for flat regions and 1 station per 50 km × 50 km for mountainous complex regions. In this paper, in order to verify model forecast for the Bac Bo area, we used observation data from the northern SYNOP stations for the period from 2012 to 2016 listed in Table 1.In this study, 72 cases of typical widespread heavy rains which occurred in the northern part of Vietnam in the period 2012–2016 were selected. For each case, the forecasting cycles are chosen so that the 72-hour forecast range can cover the maximum duration of heavy rain episodes.With respect to causes of heavy rainfall events, there are four main categories: (i) activities of ITCZs or troughs: type I, (ii) affected by tropical cyclones or surface low-pressure system (staying at least more than 2 days over the Bac Bo area): type II, (iii) related to the cold surge from the north: type III, and (iv) the complex combinations from different patterns: type IV. The list of forecasted events is given in Table3 with the station name, the maximum value of daily rainfall, and the type of each heavy rainfall case. The sample number of type I, type II, type III, and type IV has 37, 21, 5, and 9 forecast cycles, respectively.Table 3\nList of forecast cycles related to heavy rain cases from 2012 to 2016, the station with maximum daily accumulated rainfall up to 72 h for each cycle, and types of main synoptic situations.\nForecast cycle (year month day hour)\nMaximum 24 h accumulation observation (mm)\nStation with maximum observation\nTypes of rain events\n2012 05 18 00\n37\nBac Yen\nType I\n2012 05 19 00\n44\nLac Son\nType I\n2012 05 20 00\n114\nBac Quang\nType I\n2012 05 21 00\n195\nBac Quang\nType I\n2012 05 22 00\n131\nHa Dong\nType I\n2012 05 23 00\n186\nQuang Ha\nType I\n2012 07 20 00\n41\nBac Quang\nType I\n2012 07 21 00\n24.6\nLai Chau\nType II\n2012 07 22 00\n116\nVinh Yen\nType II\n2012 07 23 00\n75\nVinh\nType II\n2012 07 25 00\n229\nTuyen Quang\nType II\n2012 07 26 00\n104\nHam Yen\nType II\n2012 07 27 00\n112\nBac Quang\nType I\n2012 08 03 00\n58.1\nCon Cuong\nType I\n2012 08 04 00\n34.1\nTinh Gia\nType I\n2012 08 05 00\n70\nLang Son\nType I\n2012 08 06 00\n153\nMuong La\nType I\n2012 08 07 00\n163\nQuang Ha\nType I\n2012 08 13 00\n63.6\nMuong La\nType II\n2012 08 14 00\n64.5\nNho Quan\nType II\n2012 08 15 00\n74\nDo Luong\nType II\n2012 08 16 00\n76\nTam Dao\nType II\n2012 08 31 00\n49.1\nSin Ho\nType IV\n2012 09 01 00\n67\nNhu Xuan\nType IV\n2012 09 02 00\n124\nQuynh Luu\nType IV\n2012 09 03 00\n84\nMoc Chau\nType IV\n2012 09 04 00\n157\nHuong Khe\nType IV\n2012 09 16 00\n151.4\nMuong Te\nType IV\n2012 09 17 00\n104.6\nTam Duong\nType III\n2013 06 19 00\n109\nBac Quang\nType II\n2013 06 20 00\n72.2\nHuong Khe\nType II\n2013 06 21 00\n59\nNam Dinh\nType II\n2013 06 22 00\n218\nHa Tinh\nType II\n2014 08 25 00\n91\nLao Cai\nType II\n2014 08 26 00\n89\nVan Ly\nType II\n2014 08 27 00\n157\nHon Ngu\nType II\n2015 05 18 00\n90\nHoi Xuan\nType III\n2015 05 19 00\n58\nSin Ho\nType III\n2015 05 20 00\n106\nThat Khe\nType III\n2015 05 21 00\n72\nTuyen Quang\nType III\n2015 06 21 00\n124\nLuc Yen\nType II\n2015 06 22 00\n72.7\nDo Luong\nType II\n2015 07 01 00\n84.5\nCao Bang\nType I\n2015 07 02 00\n123.0\nVan Chan\nType I\n2015 07 03 00\n74.0\nHuong Khe\nType I\n2015 07 21 00\n54.5\nHoa Binh\nType I\n2015 07 22 00\n19\nSin Ho\nType I\n2015 07 23 00\n92\nSin Ho\nType I\n2015 07 24 00\n180\nQuynh Nhai\nType I\n2015 07 25 00\n181\nCua Ong\nType I\n2015 07 26 00\n432\nCua Ong\nType I\n2015 07 27 00\n347\nQuang Ha\nType I\n2015 07 28 00\n224\nMong Cai\nType I\n2015 07 29 00\n247\nCua Ong\nType I\n2015 07 30 00\n145\nQuang Ha\nType I\n2015 07 31 00\n239\nQuang Ha\nType I\n2015 08 01 00\n157\nPhu Lien\nType I\n2015 09 18 00\n73\nHai Duong\nType IV\n2015 09 19 00\n78.0\nDinh Hoa\nType IV\n2016 05 20 00\n118\nTinh Gia\nType I\n2016 05 21 00\n107.0\nHa Nam\nType I\n2016 05 22 00\n92\nSa Pa\nType I\n2016 05 23 00\n190.4\nYen Bai\nType I\n2016 07 24 00\n60\nPhu Lien\nType II\n2016 07 25 00\n17\nThai Binh\nType II\n2016 07 26 00\n46\nDinh Hoa\nType II\n2016 07 30 00\n18.1\nMu Cang Chai\nType II\n2016 08 01 00\n19\nSin Ho\nType I\n2016 08 02 00\n150\nNinh Binh\nType I\n2016 08 10 00\n81\nQuynh Nhai\nType I\n2016 08 11 00\n117\nThanh Hoa\nType I\n2016 08 12 00\n87\nTay Hieu\nType I\nType I: activities of trough or ITCZ; type II: affected by tropical cyclone or low-pressure system; type III: related to cold surge from the north; type IV: combinations of different patterns.\n\n\n## 2.4. Validation Methods\n\nBy finding the nearest grids to each station position (listed in Table1), the daily accumulated rainfall for these heavy rainfall cases from WRF-ARW model forecasts can be assigned. The verification scores used in this study are frequency bias (BIAS), probability of detection (POD), false alarm ratio (FAR), threat score (TS), and equitable threat score (ETS). If we denote H for the hit rate of occurred rainfalls (at a given threshold) for both forecast and observation, M for the missed rate of occurred rainfall forecast, and F for the false alarm rate of the forecast, the BIAS, POD, FAR, and TS are calculated by the following equations:(1)BIAS=H+FH+M,perfect value=1,<1=underestimation,>1=overestimation,POD=HH+M,perfect value=1,FAR=FH+F,perfect value=0,TS=HH+M+F,perfect value=1,no skill=0.If we set Hitsrandom = (H + F) (H + M)/T, where T is the sum of H, M, F, and the number of nonoccurred rainfalls for both forecast and observation, the ETS is calculated by(2)ETS=H−HitsrandomH+M+F−Hitsrandom,perfect value=1,no skill<=0.Other meanings of these scores can be found in Wilks’ study [31]. The verification will be carried out for the 5 km domain and for 24 h accumulated rainfall at 24 h, 48 h, and 72 h forecast ranges. Other analysis charts include the histogram of precipitation occurrences at given thresholds (>25 mm/24 h, >50 mm/24 h, and >100 mm/24 h) at the observation stations.\n\n\n## 3. Results\n\n### 3.1. General Performance\n\nThe histogram charts (Figure3) show the number of observations or forecasts that occurred at stations for given ranges—or bins. We divided rainfall into 4 main bins (0–25 mm, 25–50 mm, 50–100 mm, and >100 mm) for different rainfall classes. For all 24 h, 48 h, and 72 h forecasts, it is quite clear that most forecasts from the BMJ scheme are in the 0–25 mm range, higher than the number of observations, while the KF scheme tends to have less forecasts than observations. In contrast, at the thresholds greater than 25 mm, for the BMJ scheme, the number of forecasts is less than the number of observations, while the KF scheme tends to have more forecasts than the number of observations.Figure 3\nHistogram of daily rainfall frequency at different thresholds (bins) for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dotted lines are the observation frequency. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure4 shows the BIAS score at different thresholds (>25 mm/24 h and >50 mm/24 h) and separated for KF and BMJ scheme combinations. Overall assessment through the BIAS score is quite similar to the results from the evaluation through histograms: simulations with the BMJ scheme tend to be lower than observations at most forecast ranges and different thresholds (BIAS < 1), while the simulations with the KF scheme tend to be higher than the observations (BIAS > 1). The BIAS tends to decrease significantly when the forecast ranges increase. When the validation thresholds are increased, the simulation with the BMJ scheme tends to decrease BIAS, whereas in combination with the KF scheme, BIAS increases with both the forecast ranges and the evaluation thresholds.Figure 4\nThe BIAS score at 24 h, 48 h, and 72 h for different thresholds (over 25 mm, 50 mm, and 100 mm) for BMJ scheme combinations (a) and for KF scheme combinations (b). The number of samples is 8064 for an individual forecast.\n(a)\n(b)The individual assessment in each combination of BMJ and KF schemes shows that when combined with the Goddard radiation scheme, the BIAS increases from 0.1 to 0.2 compared to the Dudhia scheme. These results are similar for all validating thresholds and for 24 h, 48 h, and 72 h forecast ranges. The difference in simulations with different boundary layer schemes is unclear while evaluating with thresholds below 25 mm/24 h; however, at higher thresholds, it is apparent: the change of BIAS when combining with the KF scheme is much higher. For example, compared to the BMJ scheme, the BIAS score of KF-WSM3-God-MYJ at the >100 mm/24 h threshold at the 24-hour forecast range is 1.6458 and that of KF-WSM3-God-YSU is 2.0833, while BMJ-WSM3-God-MYJ and BMJ-WSM3-God-YSU had approximately equal BIAS scores (0.8229). Thus, the combinations of the boundary layer schemes are different (here only the two schemes YSU and MYJ) with the KF scheme being much more sensitive to its combinations with the BMJ scheme and especially at the high rainfall thresholds. Details of numerical values of BIAS can be found in Tables4, 5, and 6.Table 4\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.207\n0.1517\n0.5854\n0.2719\n0.5355\n301\n347\n806\n6106\n0.1049\n0.0874\n0.4139\n0.1342\n0.6757\n60\n125\n387\n6988\nBMJ-Lin-Duh-YSU\n0.2087\n0.1551\n0.5592\n0.2692\n0.5186\n298\n321\n809\n6132\n0.1072\n0.0899\n0.4094\n0.1365\n0.6667\n61\n122\n386\n6991\nBMJ-Lin-God-MYJ\n0.2283\n0.1613\n0.7986\n0.3342\n0.5814\n370\n514\n737\n5939\n0.1225\n0.0996\n0.6197\n0.1767\n0.7148\n79\n198\n368\n6915\nBMJ-Lin-God-YSU\n0.2304\n0.1649\n0.7706\n0.3315\n0.5698\n367\n486\n740\n5967\n0.122\n0.1\n0.5839\n0.1723\n0.705\n77\n184\n370\n6929\nBMJ-WSM3-Duh-MYJ\n0.2051\n0.1433\n0.6929\n0.2882\n0.5841\n319\n448\n788\n6005\n0.1066\n0.0846\n0.5794\n0.1521\n0.7375\n68\n191\n379\n6922\nBMJ-WSM3-Duh-YSU\n0.2201\n0.1595\n0.6775\n0.3026\n0.5533\n335\n415\n772\n6038\n0.1153\n0.0928\n0.6018\n0.1655\n0.7249\n74\n195\n373\n6918\nBMJ-WSM3-God-MYJ\n0.2388\n0.1649\n0.9539\n0.3767\n0.6051\n417\n639\n690\n5814\n0.1364\n0.1089\n0.8456\n0.2215\n0.7381\n99\n279\n348\n6834\nBMJ-WSM3-God-YSU\n0.2408\n0.1709\n0.8663\n0.3622\n0.5819\n401\n558\n706\n5895\n0.1391\n0.1116\n0.8501\n0.226\n0.7342\n101\n279\n346\n6834\nBMJ-WSM5-Duh-MYJ\n0.2147\n0.153\n0.692\n0.299\n0.5679\n331\n435\n776\n6018\n0.1166\n0.0951\n0.5638\n0.1633\n0.7103\n73\n179\n374\n6934\nBMJ-WSM5-Duh-YSU\n0.2156\n0.1553\n0.6703\n0.2963\n0.558\n328\n414\n779\n6039\n0.1319\n0.1092\n0.613\n0.1879\n0.6934\n84\n190\n363\n6923\nBMJ-WSM5-God-MYJ\n0.2408\n0.1743\n0.7967\n0.3487\n0.5624\n386\n496\n721\n5957\n0.1273\n0.1044\n0.6242\n0.1834\n0.7061\n82\n197\n365\n6916\nBMJ-WSM5-God-YSU\n0.2449\n0.1766\n0.8365\n0.3613\n0.568\n400\n526\n707\n5927\n0.1386\n0.112\n0.8009\n0.2192\n0.7263\n98\n260\n349\n6853\nBMJ-WSM6-Duh-MYJ\n0.2079\n0.1419\n0.7687\n0.3044\n0.604\n337\n514\n770\n5939\n0.1166\n0.0925\n0.6711\n0.1745\n0.74\n78\n222\n369\n6891\nBMJ-WSM6-Duh-YSU\n0.2126\n0.1469\n0.7669\n0.3098\n0.596\n343\n506\n764\n5947\n0.1386\n0.1135\n0.7271\n0.2103\n0.7108\n94\n231\n353\n6882\nBMJ-WSM6-God-MYJ\n0.2552\n0.1793\n1.0126\n0.4092\n0.5959\n453\n668\n654\n5785\n0.1554\n0.1269\n0.9128\n0.2573\n0.7181\n115\n293\n332\n6820\nBMJ-WSM6-God-YSU\n0.2477\n0.1747\n0.9386\n0.3848\n0.59\n426\n613\n681\n5840\n0.1644\n0.1355\n0.9485\n0.2752\n0.7099\n123\n301\n324\n6812\nKF-Lin-Duh-MYJ\n0.2399\n0.162\n1.0497\n0.3966\n0.6222\n439\n723\n668\n5730\n0.1525\n0.1262\n0.7919\n0.2371\n0.7006\n106\n248\n341\n6865\nKF-Lin-Duh-YSU\n0.2656\n0.1884\n1.0533\n0.4309\n0.5909\n477\n689\n630\n5764\n0.1457\n0.1168\n0.9351\n0.2461\n0.7368\n110\n308\n337\n6805\nKF-Lin-God-MYJ\n0.2507\n0.165\n1.2755\n0.4562\n0.6424\n505\n907\n602\n5546\n0.1564\n0.1233\n1.2327\n0.302\n0.755\n135\n416\n312\n6697\nKF-Lin-God-YSU\n0.2649\n0.1795\n1.2818\n0.4779\n0.6272\n529\n890\n578\n5563\n0.162\n0.128\n1.311\n0.3221\n0.7543\n144\n442\n303\n6671\nKF-WSM3-Duh-MYJ\n0.2442\n0.1627\n1.1491\n0.4219\n0.6329\n467\n805\n640\n5648\n0.1581\n0.1266\n1.1141\n0.2886\n0.741\n129\n369\n318\n6744\nKF-WSM3-Duh-YSU\n0.2664\n0.1843\n1.1861\n0.4598\n0.6123\n509\n804\n598\n5649\n0.1667\n0.135\n1.1298\n0.3043\n0.7307\n136\n369\n311\n6744\nKF-WSM3-God-MYJ\n0.2743\n0.1867\n1.3668\n0.5095\n0.6272\n564\n949\n543\n5504\n0.172\n0.1368\n1.4385\n0.3579\n0.7512\n160\n483\n287\n6630\nKF-WSM3-God-YSU\n0.2739\n0.1852\n1.4029\n0.5167\n0.6317\n572\n981\n535\n5472\n0.1778\n0.1413\n1.5638\n0.387\n0.7525\n173\n526\n274\n6587\nKF-WSM5-Duh-MYJ\n0.2583\n0.1766\n1.1653\n0.4444\n0.6186\n492\n798\n615\n5655\n0.1648\n0.1333\n1.1186\n0.2998\n0.732\n134\n366\n313\n6747\nKF-WSM5-Duh-YSU\n0.2656\n0.1828\n1.2042\n0.4625\n0.6159\n512\n821\n595\n5632\n0.1588\n0.1269\n1.1387\n0.2931\n0.7426\n131\n378\n316\n6735\nKF-WSM5-God-MYJ\n0.2693\n0.1812\n1.3758\n0.5041\n0.6336\n558\n965\n549\n5488\n0.1773\n0.1421\n1.4362\n0.3669\n0.7445\n164\n478\n283\n6635\nKF-WSM5-God-YSU\n0.2826\n0.1939\n1.4146\n0.5321\n0.6239\n589\n977\n518\n5476\n0.178\n0.1415\n1.5615\n0.387\n0.7521\n173\n525\n274\n6588\nKF-WSM6-Duh-MYJ\n0.2598\n0.1758\n1.234\n0.4607\n0.6266\n510\n856\n597\n5597\n0.1663\n0.1326\n1.2908\n0.3266\n0.747\n146\n431\n301\n6682\nKF-WSM6-Duh-YSU\n0.28\n0.1952\n1.2836\n0.4995\n0.6108\n553\n868\n554\n5585\n0.1958\n0.1611\n1.4049\n0.3937\n0.7197\n176\n452\n271\n6661\nKF-WSM6-God-MYJ\n0.26\n0.1698\n1.43\n0.5014\n0.6494\n555\n1028\n552\n5425\n0.1926\n0.156\n1.5906\n0.4183\n0.737\n187\n524\n260\n6589\nKF-WSM6-God-YSU\n0.2792\n0.1888\n1.467\n0.5384\n0.633\n596\n1028\n511\n5425\n0.1844\n0.1463\n1.7584\n0.4295\n0.7557\n192\n594\n255\n6519Table 5\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.1791\n0.1054\n0.4842\n0.2254\n0.5344\n365\n419\n1254\n5522\n0.0962\n0.0703\n0.3463\n0.1181\n0.6589\n88\n170\n657\n6645\nBMJ-Lin-Duh-YSU\n0.1853\n0.1069\n0.5287\n0.239\n0.5479\n387\n469\n1232\n5472\n0.1105\n0.0803\n0.4295\n0.1423\n0.6687\n106\n214\n639\n6601\nBMJ-Lin-God-MYJ\n0.234\n0.1345\n0.769\n0.3354\n0.5639\n543\n702\n1076\n5239\n0.1225\n0.0846\n0.5987\n0.1745\n0.7085\n130\n316\n615\n6499\nBMJ-Lin-God-YSU\n0.243\n0.1408\n0.8073\n0.3533\n0.5624\n572\n735\n1047\n5206\n0.1357\n0.0971\n0.6174\n0.1933\n0.687\n144\n316\n601\n6499\nBMJ-WSM3-Duh-MYJ\n0.2332\n0.1353\n0.7505\n0.3311\n0.5588\n536\n679\n1083\n5262\n0.152\n0.109\n0.7396\n0.2295\n0.6897\n171\n380\n574\n6435\nBMJ-WSM3-Duh-YSU\n0.2579\n0.1573\n0.7956\n0.3681\n0.5373\n596\n692\n1023\n5249\n0.1587\n0.1142\n0.7839\n0.2443\n0.6884\n182\n402\n563\n6413\nBMJ-WSM3-God-MYJ\n0.2616\n0.1452\n1.0167\n0.4182\n0.5887\n677\n969\n942\n4972\n0.1546\n0.1034\n1.0054\n0.2685\n0.733\n200\n549\n545\n6266\nBMJ-WSM3-God-YSU\n0.2641\n0.148\n1.0136\n0.4206\n0.585\n681\n960\n938\n4981\n0.1539\n0.1034\n0.9826\n0.2644\n0.7309\n197\n535\n548\n6280\nBMJ-WSM5-Duh-MYJ\n0.2425\n0.1455\n0.7437\n0.3403\n0.5424\n551\n653\n1068\n5288\n0.1582\n0.1171\n0.6899\n0.2309\n0.6654\n172\n342\n573\n6473\nBMJ-WSM5-Duh-YSU\n0.2545\n0.154\n0.7931\n0.3638\n0.5413\n589\n695\n1030\n5246\n0.1698\n0.1257\n0.7758\n0.2577\n0.6678\n192\n386\n553\n6429\nBMJ-WSM5-God-MYJ\n0.2687\n0.1601\n0.9074\n0.404\n0.5548\n654\n815\n965\n5126\n0.174\n0.1286\n0.8201\n0.2698\n0.671\n201\n410\n544\n6405\nBMJ-WSM5-God-YSU\n0.2677\n0.1522\n1.0068\n0.4237\n0.5791\n686\n944\n933\n4997\n0.1674\n0.1166\n1.0027\n0.2872\n0.7135\n214\n533\n531\n6282\nBMJ-WSM6-Duh-MYJ\n0.2282\n0.1261\n0.7986\n0.3342\n0.5816\n541\n752\n1078\n5189\n0.1414\n0.097\n0.7772\n0.2201\n0.7168\n164\n415\n581\n6400\nBMJ-WSM6-Duh-YSU\n0.2471\n0.1426\n0.8394\n0.3644\n0.5659\n590\n769\n1029\n5172\n0.169\n0.1198\n0.9409\n0.2805\n0.7019\n209\n492\n536\n6323\nBMJ-WSM6-God-MYJ\n0.2484\n0.1255\n1.1081\n0.4194\n0.6215\n679\n1115\n940\n4826\n0.1471\n0.0931\n1.1141\n0.2711\n0.7566\n202\n628\n543\n6187\nBMJ-WSM6-God-YSU\n0.283\n0.1642\n1.0723\n0.4571\n0.5737\n740\n996\n879\n4945\n0.1588\n0.1053\n1.0966\n0.2872\n0.7381\n214\n603\n531\n6212\nKF-Lin-Duh-MYJ\n0.2144\n0.1147\n0.7634\n0.3113\n0.5922\n504\n732\n1115\n5209\n0.1299\n0.0905\n0.6349\n0.1879\n0.704\n140\n333\n605\n6482\nKF-Lin-Duh-YSU\n0.2312\n0.136\n0.7171\n0.3224\n0.5504\n522\n639\n1097\n5302\n0.124\n0.0847\n0.6309\n0.1799\n0.7149\n134\n336\n611\n6479\nKF-Lin-God-MYJ\n0.2584\n0.1355\n1.1174\n0.4348\n0.6108\n704\n1105\n915\n4836\n0.1304\n0.0781\n1.0362\n0.2349\n0.7733\n175\n597\n570\n6218\nKF-Lin-God-YSU\n0.2806\n0.1647\n1.0241\n0.4435\n0.5669\n718\n940\n901\n5001\n0.1611\n0.1097\n1.0215\n0.2805\n0.7254\n209\n552\n536\n6263\nKF-WSM3-Duh-MYJ\n0.266\n0.149\n1.0284\n0.4262\n0.5856\n690\n975\n929\n4966\n0.1735\n0.1212\n1.0604\n0.3047\n0.7127\n227\n563\n518\n6252\nKF-WSM3-Duh-YSU\n0.2839\n0.1719\n0.9691\n0.4355\n0.5507\n705\n864\n914\n5077\n0.1948\n0.1437\n1.0255\n0.3302\n0.678\n246\n518\n499\n6297\nKF-WSM3-God-MYJ\n0.2845\n0.1503\n1.3484\n0.5201\n0.6143\n842\n1341\n777\n4600\n0.1766\n0.1161\n1.4416\n0.3664\n0.7458\n273\n801\n472\n6014\nKF-WSM3-God-YSU\n0.3018\n0.1706\n1.3125\n0.5361\n0.5915\n868\n1257\n751\n4684\n0.1877\n0.1271\n1.455\n0.3879\n0.7334\n289\n795\n456\n6020\nKF-WSM5-Duh-MYJ\n0.2801\n0.1622\n1.055\n0.4497\n0.5738\n728\n980\n891\n4961\n0.1785\n0.1262\n1.0644\n0.3128\n0.7062\n233\n560\n512\n6255\nKF-WSM5-Duh-YSU\n0.2948\n0.1812\n1.0019\n0.4558\n0.545\n738\n884\n881\n5057\n0.2037\n0.1509\n1.102\n0.3557\n0.6772\n265\n556\n480\n6259\nKF-WSM5-God-MYJ\n0.284\n0.1477\n1.3904\n0.5287\n0.6197\n856\n1395\n763\n4546\n0.1685\n0.1064\n1.5221\n0.3638\n0.761\n271\n863\n474\n5952\nKF-WSM5-God-YSU\n0.2998\n0.1683\n1.3162\n0.5343\n0.5941\n865\n1266\n754\n4675\n0.1996\n0.1388\n1.4846\n0.4134\n0.7215\n308\n798\n437\n6017\nKF-WSM6-Duh-MYJ\n0.2799\n0.1571\n1.1353\n0.467\n0.5887\n756\n1082\n863\n4859\n0.1973\n0.1415\n1.2242\n0.3664\n0.7007\n273\n639\n472\n6176\nKF-WSM6-Duh-YSU\n0.3002\n0.1833\n1.0574\n0.475\n0.5508\n769\n943\n850\n4998\n0.211\n0.157\n1.157\n0.3758\n0.6752\n280\n582\n465\n6233\nKF-WSM6-God-MYJ\n0.2917\n0.155\n1.4095\n0.5442\n0.6139\n881\n1401\n738\n4540\n0.1788\n0.1154\n1.6107\n0.396\n0.7542\n295\n905\n450\n5910\nKF-WSM6-God-YSU\n0.3113\n0.1785\n1.357\n0.5596\n0.5876\n906\n1291\n713\n4650\n0.2101\n0.1475\n1.6054\n0.4523\n0.7182\n337\n859\n408\n5956Table 6\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 72 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.154\n0.0672\n0.3964\n0.1863\n0.53\n400\n451\n1747\n4962\n0.0797\n0.0443\n0.3412\n0.099\n0.7098\n101\n247\n919\n6293\nBMJ-Lin-Duh-YSU\n0.1539\n0.0595\n0.442\n0.1924\n0.5648\n413\n536\n1734\n4877\n0.0766\n0.0373\n0.3912\n0.099\n0.7469\n101\n298\n919\n6242\nBMJ-Lin-God-MYJ\n0.1755\n0.0611\n0.5752\n0.2352\n0.5911\n505\n730\n1642\n4683\n0.0719\n0.0285\n0.4471\n0.0971\n0.7829\n99\n357\n921\n6183\nBMJ-Lin-God-YSU\n0.1893\n0.0645\n0.653\n0.2632\n0.597\n565\n837\n1582\n4576\n0.0832\n0.0342\n0.5314\n0.1176\n0.7786\n120\n422\n900\n6118\nBMJ-WSM3-Duh-MYJ\n0.1802\n0.0656\n0.5771\n0.2408\n0.5827\n517\n722\n1630\n4691\n0.0867\n0.0391\n0.5108\n0.1206\n0.7639\n123\n398\n897\n6142\nBMJ-WSM3-Duh-YSU\n0.1988\n0.0737\n0.6572\n0.2748\n0.5819\n590\n821\n1557\n4592\n0.0935\n0.0407\n0.5941\n0.1363\n0.7706\n139\n467\n881\n6073\nBMJ-WSM3-God-MYJ\n0.2273\n0.0777\n0.871\n0.3465\n0.6021\n744\n1126\n1403\n4287\n0.0992\n0.0404\n0.7049\n0.1539\n0.7816\n157\n562\n863\n5978\nBMJ-WSM3-God-YSU\n0.2239\n0.0691\n0.9171\n0.3507\n0.6176\n753\n1216\n1394\n4197\n0.1022\n0.0373\n0.8294\n0.1696\n0.7955\n173\n673\n847\n5867\nBMJ-WSM5-Duh-MYJ\n0.1788\n0.0634\n0.5817\n0.2399\n0.5877\n515\n734\n1632\n4679\n0.0944\n0.0482\n0.4892\n0.1284\n0.7375\n131\n368\n889\n6172\nBMJ-WSM5-Duh-YSU\n0.2041\n0.0796\n0.6544\n0.2804\n0.5715\n602\n803\n1545\n4610\n0.0966\n0.0427\n0.6137\n0.1422\n0.7684\n145\n481\n875\n6059\nBMJ-WSM5-God-MYJ\n0.2169\n0.0787\n0.7667\n0.3149\n0.5893\n676\n970\n1471\n4443\n0.0908\n0.037\n0.6127\n0.1343\n0.7808\n137\n488\n883\n6052\nBMJ-WSM5-God-YSU\n0.2278\n0.0779\n0.8728\n0.3475\n0.6019\n746\n1128\n1401\n4285\n0.0947\n0.031\n0.802\n0.1559\n0.8056\n159\n659\n861\n5881\nBMJ-WSM6-Duh-MYJ\n0.1712\n0.0601\n0.5515\n0.2268\n0.5887\n487\n697\n1660\n4716\n0.0834\n0.0379\n0.4775\n0.1137\n0.7618\n116\n371\n904\n6169\nBMJ-WSM6-Duh-YSU\n0.1971\n0.069\n0.68\n0.2767\n0.5932\n594\n866\n1553\n4547\n0.0992\n0.0432\n0.652\n0.149\n0.7714\n152\n513\n868\n6027\nBMJ-WSM6-God-MYJ\n0.2191\n0.069\n0.8714\n0.3363\n0.6141\n722\n1149\n1425\n4264\n0.1\n0.0385\n0.7578\n0.1598\n0.7891\n163\n610\n857\n5930\nBMJ-WSM6-God-YSU\n0.215\n0.0632\n0.885\n0.3335\n0.6232\n716\n1184\n1431\n4229\n0.0824\n0.0185\n0.8039\n0.1373\n0.8293\n140\n680\n880\n5860\nKF-Lin-Duh-MYJ\n0.2144\n0.0871\n0.6782\n0.2962\n0.5632\n636\n820\n1511\n4593\n0.1162\n0.0632\n0.601\n0.1667\n0.7227\n170\n443\n850\n6097\nKF-Lin-Duh-YSU\n0.2063\n0.0825\n0.6502\n0.2823\n0.5659\n606\n790\n1541\n4623\n0.1069\n0.052\n0.6343\n0.1578\n0.7512\n161\n486\n859\n6054\nKF-Lin-God-MYJ\n0.2399\n0.0805\n0.9693\n0.381\n0.6069\n818\n1263\n1329\n4150\n0.1392\n0.0702\n0.9333\n0.2363\n0.7468\n241\n711\n779\n5829\nKF-Lin-God-YSU\n0.2427\n0.0831\n0.9725\n0.3852\n0.6039\n827\n1261\n1320\n4152\n0.1309\n0.0605\n0.9647\n0.2275\n0.7642\n232\n752\n788\n5788\nKF-WSM3-Duh-MYJ\n0.2377\n0.0889\n0.8677\n0.3586\n0.5867\n770\n1093\n1377\n4320\n0.1242\n0.0613\n0.7922\n0.198\n0.75\n202\n606\n818\n5934\nKF-WSM3-Duh-YSU\n0.2618\n0.1129\n0.8812\n0.3903\n0.5571\n838\n1054\n1309\n4359\n0.1675\n0.0983\n0.948\n0.2794\n0.7053\n285\n682\n735\n5858\nKF-WSM3-God-MYJ\n0.2596\n0.0844\n1.1495\n0.4429\n0.6147\n951\n1517\n1196\n3896\n0.1334\n0.0538\n1.2235\n0.2618\n0.7861\n267\n981\n753\n5559\nKF-WSM3-God-YSU\n0.2817\n0.1073\n1.1593\n0.4746\n0.5906\n1019\n1470\n1128\n3943\n0.1631\n0.081\n1.3216\n0.3255\n0.7537\n332\n1016\n688\n5524\nKF-WSM5-Duh-MYJ\n0.2508\n0.1005\n0.8882\n0.3787\n0.5737\n813\n1094\n1334\n4319\n0.1333\n0.0694\n0.8167\n0.2137\n0.7383\n218\n615\n802\n5925\nKF-WSM5-Duh-YSU\n0.2671\n0.1193\n0.8738\n0.395\n0.548\n848\n1028\n1299\n4385\n0.158\n0.0902\n0.9118\n0.2608\n0.714\n266\n664\n754\n5876\nKF-WSM5-God-MYJ\n0.2723\n0.0967\n1.1635\n0.463\n0.6021\n994\n1504\n1153\n3909\n0.1354\n0.0571\n1.1863\n0.2608\n0.7802\n266\n944\n754\n5596\nKF-WSM5-God-YSU\n0.2652\n0.0932\n1.1178\n0.4439\n0.6029\n953\n1447\n1194\n3966\n0.1507\n0.0709\n1.2382\n0.2931\n0.7633\n299\n964\n721\n5576\nKF-WSM6-Duh-MYJ\n0.2545\n0.0952\n0.9767\n0.401\n0.5894\n861\n1236\n1286\n4177\n0.1237\n0.0546\n0.9324\n0.2127\n0.7718\n217\n734\n803\n5806\nKF-WSM6-Duh-YSU\n0.2615\n0.1084\n0.9208\n0.3982\n0.5675\n855\n1122\n1292\n4291\n0.156\n0.0841\n1.0127\n0.2716\n0.7318\n277\n756\n743\n5784\nKF-WSM6-God-MYJ\n0.2693\n0.0883\n1.2259\n0.4723\n0.6147\n1014\n1618\n1133\n3795\n0.1398\n0.0571\n1.3265\n0.2853\n0.7849\n291\n1062\n729\n5478\nKF-WSM6-God-YSU\n0.2853\n0.1072\n1.2054\n0.4895\n0.5939\n1051\n1537\n1096\n3876\n0.1513\n0.0661\n1.4245\n0.3186\n0.7763\n325\n1128\n695\n5412\n\n\n### 3.2. Skill Score Validation\n\nThe charts for the skill scores at the two thresholds in Figures5 and 6 show that the TS value at the 24 h forecast range is about 0.2 to 0.27 for >25 mm/24 h and 0.1 to 0.2 for >50 mm/24 h. At 48 h, the TS is around ∼0.18 to 0.3 and ∼0.1 to 0.19 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h. At 72 h, the TS is around 0.15 to 0.25 and ∼0.08 to 0.15 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h.Figure 5\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 25 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure 6\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 50 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)The probability of detection decreases and the false alarm rate clearly increases with forecast ranges and the validating thresholds. In addition, when the threshold increases, the difference between the TS and the ETS decreases which means that the amount of Hitsrandom is too small or the cause of very small hit rate (Hitsrandom rate decreases by 90% when changing the threshold from >25 mm/24 h to >50 mm/24 h).Specific comparisons between the combinations of the BMJ or KF scheme show that the KF scheme model’s skills in heavy rain forecast in the northern region of Vietnam are better than BMJ scheme model’s skills. The average TS with the KF scheme can be about 15–25% larger than that using the BMJ scheme. If the difference of skills in a regional model is insignificant when changing the physical parameterization schemes, the lateral boundary conditions (from global forecasts) will greatly affect the quality of the dynamical downscaling forecasts after 24 h integration. However, here the skill difference when combining the two different cumulus schemes in the longer forecast range (such as 72 h) shows the importance of convection simulation capability contributing to the forecasting quality of the model. Detailed evaluation of the combination with the radiation physical schemes of KF or BMJ does not show any difference compared to changing the boundary layer schemes when looking at the skill scores TS or ETS.The combinations with YSU boundary layer schemes have better skills compared to those with MYJ schemes. In addition, when changing the complexity of the cloud microphysics schemes, the more complex the microphysical processing simulation, the better the TS and ETS (at 24, 48, and 72 h forecast ranges and two validating thresholds; see Figure7 for comparison of the change in TSs and ETSs with the cloud microphysics scheme).Figure 7\nBrief comparison of TSs and ETSs for illustration of sensitivities with microphysics schemes.For the skill comparisons of different event types (I, II, III, and IV), Figure8 shows the TSs at thresholds over 25 mm/24 h and over 50 mm/24 h for 24 h and 48 h forecast ranges. For type I, which is associated with the activity of ITCZ and low-pressure trough over the Bac Bo area, the KF scheme proved its forecast skills in almost forecast ranges and thresholds mentioned in this research. However, with the rain caused by tropical cyclone in type II, the difference between KF and BMJ schemes within 24 h was smaller than that in type I. In 48 h and 72 h, the BMJ scheme showed more skilled forecast with the skill score for threshold 25 mm ranging from 0.25 to 0.35 of the BMJ scheme, compared with 0.2 to 0.3 of the KF scheme, and the skill score for threshold 50 mm ranging from 0.2 to 0.3 of the BMJ scheme, compared with 0.2 to 0.25 of the KF scheme. In type II, both KF and BMJ schemes combined with the simple cloud microphysics Lin scheme showed lowest skill score. For type III, which is associated with the activity of cold surge and its role in squeezing the low-pressure trough from the north towards Bac Bo, the KF scheme was only skillful in threshold 25 mm in 24 h. Particularly in type IV, which contains heavy rain events caused by a complex combination of situations resulting in a trough in Bac Bo, the KF scheme still showed skilled forecast compared to very low forecast (no skill with threshold over 50 mm/24 h and 25 mm/24 h) of BMJ scheme experiments. More details of TSs for different types are listed in Tables 7 and 8.Figure 8\nTSs for different types of heavy rainfall events in northern Vietnam for daily accumulation thresholds over (a) 25 mm and 50 mm (b) at 24 h forecast ranges and over 25 mm (c) and 50 mm (d) at 48 h forecast ranges. The right vertical axis is from 0 to 0.4.\n(a)\n(b)\n(c)\n(d)Table 7\nTSs for different types of main heavy rainfall, at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.2546\n0.1309\n0.15\n0.0682\n0.124\n0.0791\n0.0769\n0\nBMJ-Lin-Duh-YSU\n0.2625\n0.1019\n0.2346\n0.0706\n0.1244\n0.0692\n0.16\n0\nBMJ-Lin-God-MYJ\n0.2644\n0.1638\n0.2235\n0.1011\n0.139\n0.1064\n0.0882\n0\nBMJ-Lin-God-YSU\n0.2761\n0.1519\n0.1939\n0.093\n0.1369\n0.087\n0.1562\n0.0333\nBMJ-WSM3-Duh-MYJ\n0.2503\n0.1237\n0.1818\n0.09\n0.1279\n0.0621\n0.129\n0\nBMJ-WSM3-Duh-YSU\n0.2745\n0.1179\n0.2439\n0.0652\n0.1379\n0.0699\n0.1471\n0\nBMJ-WSM3-God-MYJ\n0.2834\n0.1651\n0.21\n0.09\n0.1556\n0.1104\n0.1136\n0\nBMJ-WSM3-God-YSU\n0.2943\n0.1505\n0.202\n0.0745\n0.167\n0.0798\n0.1304\n0.0312\nBMJ-WSM5-Duh-MYJ\n0.2618\n0.1349\n0.1977\n0.0707\n0.1429\n0.0563\n0.1515\n0\nBMJ-WSM5-Duh-YSU\n0.263\n0.1351\n0.2073\n0.0652\n0.1603\n0.0789\n0.1389\n0\nBMJ-WSM5-God-MYJ\n0.3032\n0.1376\n0.1978\n0.0714\n0.1565\n0.0496\n0.1515\n0.0345\nBMJ-WSM5-God-YSU\n0.3028\n0.1525\n0.1939\n0.0737\n0.1677\n0.0696\n0.15\n0.0312\nBMJ-WSM6-Duh-MYJ\n0.2571\n0.133\n0.1304\n0.0926\n0.1403\n0.0872\n0.0732\n0\nBMJ-WSM6-Duh-YSU\n0.2643\n0.1281\n0.1828\n0.06\n0.1714\n0.0719\n0.1212\n0\nBMJ-WSM6-God-MYJ\n0.2991\n0.1781\n0.2574\n0.0784\n0.1706\n0.1391\n0.15\n0.0256\nBMJ-WSM6-God-YSU\n0.3043\n0.1595\n0.1944\n0.0652\n0.1916\n0.1104\n0.1429\n0\nKF-Lin-Duh-MYJ\n0.2881\n0.1673\n0.2136\n0.1449\n0.1844\n0.0683\n0.1026\n0.1818\nKF-Lin-Duh-YSU\n0.3125\n0.1951\n0.2474\n0.1667\n0.1778\n0.0698\n0.06\n0.1892\nKF-Lin-God-MYJ\n0.2917\n0.1914\n0.2255\n0.1558\n0.1725\n0.1237\n0.0889\n0.1471\nKF-Lin-God-YSU\n0.3129\n0.183\n0.2653\n0.1769\n0.1839\n0.1162\n0.093\n0.1316\nKF-WSM3-Duh-MYJ\n0.2872\n0.1776\n0.2212\n0.1655\n0.1893\n0.0753\n0.1224\n0.1667\nKF-WSM3-Duh-YSU\n0.309\n0.1881\n0.2551\n0.219\n0.1858\n0.1176\n0.1111\n0.2059\nKF-WSM3-God-MYJ\n0.3264\n0.1924\n0.2526\n0.1656\n0.2057\n0.0909\n0.0889\n0.1579\nKF-WSM3-God-YSU\n0.3188\n0.1841\n0.3069\n0.2014\n0.2027\n0.1185\n0.098\n0.1795\nKF-WSM5-Duh-MYJ\n0.3062\n0.1836\n0.25\n0.1655\n0.2048\n0.0688\n0.0851\n0.1795\nKF-WSM5-Duh-YSU\n0.3116\n0.1892\n0.2653\n0.1838\n0.1823\n0.1111\n0.08\n0.1818\nKF-WSM5-God-MYJ\n0.3141\n0.1996\n0.25\n0.1772\n0.2047\n0.1122\n0.1064\n0.1622\nKF-WSM5-God-YSU\n0.3317\n0.2025\n0.2642\n0.1875\n0.2063\n0.1085\n0.1228\n0.1579\nKF-WSM6-Duh-MYJ\n0.3063\n0.1823\n0.2233\n0.1812\n0.1963\n0.0952\n0.0833\n0.1538\nKF-WSM6-Duh-YSU\n0.3339\n0.1927\n0.2476\n0.1898\n0.2244\n0.1179\n0.1071\n0.2812\nKF-WSM6-God-MYJ\n0.3138\n0.1781\n0.2268\n0.1465\n0.2207\n0.1268\n0.1429\n0.125\nKF-WSM6-God-YSU\n0.3323\n0.1793\n0.2843\n0.2\n0.2078\n0.1255\n0.1538\n0.1429Table 8\nTSs for different types of main heavy rainfall, at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.1629\n0.2426\n0.0854\n0.0922\n0.0768\n0.1533\n0.0526\n0.0333\nBMJ-Lin-Duh-YSU\n0.1755\n0.2271\n0.0909\n0.1438\n0.0971\n0.1559\n0.0833\n0.0164\nBMJ-Lin-God-MYJ\n0.2276\n0.3104\n0.075\n0.0798\n0.1139\n0.1631\n0.0333\n0.0303\nBMJ-Lin-God-YSU\n0.2482\n0.2723\n0.1429\n0.12\n0.1405\n0.1543\n0.0385\n0.0167\nBMJ-WSM3-Duh-MYJ\n0.2235\n0.3055\n0.0833\n0.1311\n0.1147\n0.2848\n0.0571\n0\nBMJ-WSM3-Duh-YSU\n0.2365\n0.3419\n0.1538\n0.1617\n0.1136\n0.3072\n0.0556\n0.0116\nBMJ-WSM3-God-MYJ\n0.2583\n0.3448\n0.0979\n0.1029\n0.1386\n0.2466\n0.0182\n0\nBMJ-WSM3-God-YSU\n0.2587\n0.3338\n0.1176\n0.1272\n0.1541\n0.2125\n0.02\n0\nBMJ-WSM5-Duh-MYJ\n0.2301\n0.3206\n0.0928\n0.1228\n0.1287\n0.2706\n0.0741\n0.0115\nBMJ-WSM5-Duh-YSU\n0.243\n0.3157\n0.1373\n0.172\n0.1397\n0.2813\n0.0312\n0.0241\nBMJ-WSM5-God-MYJ\n0.2594\n0.344\n0.1327\n0.1337\n0.1479\n0.2928\n0.0286\n0\nBMJ-WSM5-God-YSU\n0.2588\n0.3329\n0.1797\n0.1364\n0.1383\n0.2918\n0\n0.0115\nBMJ-WSM6-Duh-MYJ\n0.217\n0.304\n0.1418\n0.1027\n0.1214\n0.2212\n0.0784\n0.0235\nBMJ-WSM6-Duh-YSU\n0.2331\n0.3216\n0.1368\n0.1587\n0.1348\n0.2968\n0.0714\n0.0103\nBMJ-WSM6-God-MYJ\n0.2522\n0.3055\n0.1474\n0.0955\n0.132\n0.2314\n0.0541\n0.0215\nBMJ-WSM6-God-YSU\n0.2739\n0.3707\n0.2\n0.1026\n0.1333\n0.2658\n0.0484\n0.0319\nKF-Lin-Duh-MYJ\n0.2259\n0.2093\n0.0811\n0.225\n0.1331\n0.1208\n0.027\n0.1899\nKF-Lin-Duh-YSU\n0.2714\n0.172\n0.0957\n0.2199\n0.1347\n0.1017\n0.0667\n0.1395\nKF-Lin-God-MYJ\n0.268\n0.2537\n0.1043\n0.2771\n0.1267\n0.1545\n0.0385\n0.1237\nKF-Lin-God-YSU\n0.3125\n0.2384\n0.1058\n0.2788\n0.1704\n0.1608\n0.0488\n0.1327\nKF-WSM3-Duh-MYJ\n0.2689\n0.2805\n0.1298\n0.2783\n0.156\n0.241\n0.0577\n0.1327\nKF-WSM3-Duh-YSU\n0.3066\n0.2739\n0.069\n0.285\n0.1971\n0.2068\n0.0444\n0.2088\nKF-WSM3-God-MYJ\n0.2945\n0.2976\n0.1286\n0.2595\n0.1651\n0.2451\n0.0526\n0.1111\nKF-WSM3-God-YSU\n0.3202\n0.2956\n0.1407\n0.2822\n0.1872\n0.2136\n0.0923\n0.1569\nKF-WSM5-Duh-MYJ\n0.2924\n0.2921\n0.1176\n0.2546\n0.1586\n0.238\n0.0727\n0.1765\nKF-WSM5-Duh-YSU\n0.3217\n0.2847\n0.0976\n0.26\n0.1995\n0.2403\n0.0755\n0.1753\nKF-WSM5-God-MYJ\n0.2946\n0.2953\n0.1192\n0.2672\n0.1499\n0.25\n0.038\n0.1293\nKF-WSM5-God-YSU\n0.3265\n0.2768\n0.1361\n0.2881\n0.201\n0.2344\n0.0959\n0.1404\nKF-WSM6-Duh-MYJ\n0.2922\n0.2874\n0.1389\n0.2597\n0.1874\n0.25\n0.0685\n0.1667\nKF-WSM6-Duh-YSU\n0.3291\n0.2706\n0.1311\n0.2995\n0.208\n0.2378\n0.0893\n0.2083\nKF-WSM6-God-MYJ\n0.3002\n0.3115\n0.1465\n0.2556\n0.1675\n0.2537\n0.0435\n0.1333\nKF-WSM6-God-YSU\n0.3426\n0.2798\n0.1595\n0.2888\n0.2091\n0.2565\n0.0674\n0.177\n\n\n## 3.1. General Performance\n\nThe histogram charts (Figure3) show the number of observations or forecasts that occurred at stations for given ranges—or bins. We divided rainfall into 4 main bins (0–25 mm, 25–50 mm, 50–100 mm, and >100 mm) for different rainfall classes. For all 24 h, 48 h, and 72 h forecasts, it is quite clear that most forecasts from the BMJ scheme are in the 0–25 mm range, higher than the number of observations, while the KF scheme tends to have less forecasts than observations. In contrast, at the thresholds greater than 25 mm, for the BMJ scheme, the number of forecasts is less than the number of observations, while the KF scheme tends to have more forecasts than the number of observations.Figure 3\nHistogram of daily rainfall frequency at different thresholds (bins) for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dotted lines are the observation frequency. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure4 shows the BIAS score at different thresholds (>25 mm/24 h and >50 mm/24 h) and separated for KF and BMJ scheme combinations. Overall assessment through the BIAS score is quite similar to the results from the evaluation through histograms: simulations with the BMJ scheme tend to be lower than observations at most forecast ranges and different thresholds (BIAS < 1), while the simulations with the KF scheme tend to be higher than the observations (BIAS > 1). The BIAS tends to decrease significantly when the forecast ranges increase. When the validation thresholds are increased, the simulation with the BMJ scheme tends to decrease BIAS, whereas in combination with the KF scheme, BIAS increases with both the forecast ranges and the evaluation thresholds.Figure 4\nThe BIAS score at 24 h, 48 h, and 72 h for different thresholds (over 25 mm, 50 mm, and 100 mm) for BMJ scheme combinations (a) and for KF scheme combinations (b). The number of samples is 8064 for an individual forecast.\n(a)\n(b)The individual assessment in each combination of BMJ and KF schemes shows that when combined with the Goddard radiation scheme, the BIAS increases from 0.1 to 0.2 compared to the Dudhia scheme. These results are similar for all validating thresholds and for 24 h, 48 h, and 72 h forecast ranges. The difference in simulations with different boundary layer schemes is unclear while evaluating with thresholds below 25 mm/24 h; however, at higher thresholds, it is apparent: the change of BIAS when combining with the KF scheme is much higher. For example, compared to the BMJ scheme, the BIAS score of KF-WSM3-God-MYJ at the >100 mm/24 h threshold at the 24-hour forecast range is 1.6458 and that of KF-WSM3-God-YSU is 2.0833, while BMJ-WSM3-God-MYJ and BMJ-WSM3-God-YSU had approximately equal BIAS scores (0.8229). Thus, the combinations of the boundary layer schemes are different (here only the two schemes YSU and MYJ) with the KF scheme being much more sensitive to its combinations with the BMJ scheme and especially at the high rainfall thresholds. Details of numerical values of BIAS can be found in Tables4, 5, and 6.Table 4\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.207\n0.1517\n0.5854\n0.2719\n0.5355\n301\n347\n806\n6106\n0.1049\n0.0874\n0.4139\n0.1342\n0.6757\n60\n125\n387\n6988\nBMJ-Lin-Duh-YSU\n0.2087\n0.1551\n0.5592\n0.2692\n0.5186\n298\n321\n809\n6132\n0.1072\n0.0899\n0.4094\n0.1365\n0.6667\n61\n122\n386\n6991\nBMJ-Lin-God-MYJ\n0.2283\n0.1613\n0.7986\n0.3342\n0.5814\n370\n514\n737\n5939\n0.1225\n0.0996\n0.6197\n0.1767\n0.7148\n79\n198\n368\n6915\nBMJ-Lin-God-YSU\n0.2304\n0.1649\n0.7706\n0.3315\n0.5698\n367\n486\n740\n5967\n0.122\n0.1\n0.5839\n0.1723\n0.705\n77\n184\n370\n6929\nBMJ-WSM3-Duh-MYJ\n0.2051\n0.1433\n0.6929\n0.2882\n0.5841\n319\n448\n788\n6005\n0.1066\n0.0846\n0.5794\n0.1521\n0.7375\n68\n191\n379\n6922\nBMJ-WSM3-Duh-YSU\n0.2201\n0.1595\n0.6775\n0.3026\n0.5533\n335\n415\n772\n6038\n0.1153\n0.0928\n0.6018\n0.1655\n0.7249\n74\n195\n373\n6918\nBMJ-WSM3-God-MYJ\n0.2388\n0.1649\n0.9539\n0.3767\n0.6051\n417\n639\n690\n5814\n0.1364\n0.1089\n0.8456\n0.2215\n0.7381\n99\n279\n348\n6834\nBMJ-WSM3-God-YSU\n0.2408\n0.1709\n0.8663\n0.3622\n0.5819\n401\n558\n706\n5895\n0.1391\n0.1116\n0.8501\n0.226\n0.7342\n101\n279\n346\n6834\nBMJ-WSM5-Duh-MYJ\n0.2147\n0.153\n0.692\n0.299\n0.5679\n331\n435\n776\n6018\n0.1166\n0.0951\n0.5638\n0.1633\n0.7103\n73\n179\n374\n6934\nBMJ-WSM5-Duh-YSU\n0.2156\n0.1553\n0.6703\n0.2963\n0.558\n328\n414\n779\n6039\n0.1319\n0.1092\n0.613\n0.1879\n0.6934\n84\n190\n363\n6923\nBMJ-WSM5-God-MYJ\n0.2408\n0.1743\n0.7967\n0.3487\n0.5624\n386\n496\n721\n5957\n0.1273\n0.1044\n0.6242\n0.1834\n0.7061\n82\n197\n365\n6916\nBMJ-WSM5-God-YSU\n0.2449\n0.1766\n0.8365\n0.3613\n0.568\n400\n526\n707\n5927\n0.1386\n0.112\n0.8009\n0.2192\n0.7263\n98\n260\n349\n6853\nBMJ-WSM6-Duh-MYJ\n0.2079\n0.1419\n0.7687\n0.3044\n0.604\n337\n514\n770\n5939\n0.1166\n0.0925\n0.6711\n0.1745\n0.74\n78\n222\n369\n6891\nBMJ-WSM6-Duh-YSU\n0.2126\n0.1469\n0.7669\n0.3098\n0.596\n343\n506\n764\n5947\n0.1386\n0.1135\n0.7271\n0.2103\n0.7108\n94\n231\n353\n6882\nBMJ-WSM6-God-MYJ\n0.2552\n0.1793\n1.0126\n0.4092\n0.5959\n453\n668\n654\n5785\n0.1554\n0.1269\n0.9128\n0.2573\n0.7181\n115\n293\n332\n6820\nBMJ-WSM6-God-YSU\n0.2477\n0.1747\n0.9386\n0.3848\n0.59\n426\n613\n681\n5840\n0.1644\n0.1355\n0.9485\n0.2752\n0.7099\n123\n301\n324\n6812\nKF-Lin-Duh-MYJ\n0.2399\n0.162\n1.0497\n0.3966\n0.6222\n439\n723\n668\n5730\n0.1525\n0.1262\n0.7919\n0.2371\n0.7006\n106\n248\n341\n6865\nKF-Lin-Duh-YSU\n0.2656\n0.1884\n1.0533\n0.4309\n0.5909\n477\n689\n630\n5764\n0.1457\n0.1168\n0.9351\n0.2461\n0.7368\n110\n308\n337\n6805\nKF-Lin-God-MYJ\n0.2507\n0.165\n1.2755\n0.4562\n0.6424\n505\n907\n602\n5546\n0.1564\n0.1233\n1.2327\n0.302\n0.755\n135\n416\n312\n6697\nKF-Lin-God-YSU\n0.2649\n0.1795\n1.2818\n0.4779\n0.6272\n529\n890\n578\n5563\n0.162\n0.128\n1.311\n0.3221\n0.7543\n144\n442\n303\n6671\nKF-WSM3-Duh-MYJ\n0.2442\n0.1627\n1.1491\n0.4219\n0.6329\n467\n805\n640\n5648\n0.1581\n0.1266\n1.1141\n0.2886\n0.741\n129\n369\n318\n6744\nKF-WSM3-Duh-YSU\n0.2664\n0.1843\n1.1861\n0.4598\n0.6123\n509\n804\n598\n5649\n0.1667\n0.135\n1.1298\n0.3043\n0.7307\n136\n369\n311\n6744\nKF-WSM3-God-MYJ\n0.2743\n0.1867\n1.3668\n0.5095\n0.6272\n564\n949\n543\n5504\n0.172\n0.1368\n1.4385\n0.3579\n0.7512\n160\n483\n287\n6630\nKF-WSM3-God-YSU\n0.2739\n0.1852\n1.4029\n0.5167\n0.6317\n572\n981\n535\n5472\n0.1778\n0.1413\n1.5638\n0.387\n0.7525\n173\n526\n274\n6587\nKF-WSM5-Duh-MYJ\n0.2583\n0.1766\n1.1653\n0.4444\n0.6186\n492\n798\n615\n5655\n0.1648\n0.1333\n1.1186\n0.2998\n0.732\n134\n366\n313\n6747\nKF-WSM5-Duh-YSU\n0.2656\n0.1828\n1.2042\n0.4625\n0.6159\n512\n821\n595\n5632\n0.1588\n0.1269\n1.1387\n0.2931\n0.7426\n131\n378\n316\n6735\nKF-WSM5-God-MYJ\n0.2693\n0.1812\n1.3758\n0.5041\n0.6336\n558\n965\n549\n5488\n0.1773\n0.1421\n1.4362\n0.3669\n0.7445\n164\n478\n283\n6635\nKF-WSM5-God-YSU\n0.2826\n0.1939\n1.4146\n0.5321\n0.6239\n589\n977\n518\n5476\n0.178\n0.1415\n1.5615\n0.387\n0.7521\n173\n525\n274\n6588\nKF-WSM6-Duh-MYJ\n0.2598\n0.1758\n1.234\n0.4607\n0.6266\n510\n856\n597\n5597\n0.1663\n0.1326\n1.2908\n0.3266\n0.747\n146\n431\n301\n6682\nKF-WSM6-Duh-YSU\n0.28\n0.1952\n1.2836\n0.4995\n0.6108\n553\n868\n554\n5585\n0.1958\n0.1611\n1.4049\n0.3937\n0.7197\n176\n452\n271\n6661\nKF-WSM6-God-MYJ\n0.26\n0.1698\n1.43\n0.5014\n0.6494\n555\n1028\n552\n5425\n0.1926\n0.156\n1.5906\n0.4183\n0.737\n187\n524\n260\n6589\nKF-WSM6-God-YSU\n0.2792\n0.1888\n1.467\n0.5384\n0.633\n596\n1028\n511\n5425\n0.1844\n0.1463\n1.7584\n0.4295\n0.7557\n192\n594\n255\n6519Table 5\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.1791\n0.1054\n0.4842\n0.2254\n0.5344\n365\n419\n1254\n5522\n0.0962\n0.0703\n0.3463\n0.1181\n0.6589\n88\n170\n657\n6645\nBMJ-Lin-Duh-YSU\n0.1853\n0.1069\n0.5287\n0.239\n0.5479\n387\n469\n1232\n5472\n0.1105\n0.0803\n0.4295\n0.1423\n0.6687\n106\n214\n639\n6601\nBMJ-Lin-God-MYJ\n0.234\n0.1345\n0.769\n0.3354\n0.5639\n543\n702\n1076\n5239\n0.1225\n0.0846\n0.5987\n0.1745\n0.7085\n130\n316\n615\n6499\nBMJ-Lin-God-YSU\n0.243\n0.1408\n0.8073\n0.3533\n0.5624\n572\n735\n1047\n5206\n0.1357\n0.0971\n0.6174\n0.1933\n0.687\n144\n316\n601\n6499\nBMJ-WSM3-Duh-MYJ\n0.2332\n0.1353\n0.7505\n0.3311\n0.5588\n536\n679\n1083\n5262\n0.152\n0.109\n0.7396\n0.2295\n0.6897\n171\n380\n574\n6435\nBMJ-WSM3-Duh-YSU\n0.2579\n0.1573\n0.7956\n0.3681\n0.5373\n596\n692\n1023\n5249\n0.1587\n0.1142\n0.7839\n0.2443\n0.6884\n182\n402\n563\n6413\nBMJ-WSM3-God-MYJ\n0.2616\n0.1452\n1.0167\n0.4182\n0.5887\n677\n969\n942\n4972\n0.1546\n0.1034\n1.0054\n0.2685\n0.733\n200\n549\n545\n6266\nBMJ-WSM3-God-YSU\n0.2641\n0.148\n1.0136\n0.4206\n0.585\n681\n960\n938\n4981\n0.1539\n0.1034\n0.9826\n0.2644\n0.7309\n197\n535\n548\n6280\nBMJ-WSM5-Duh-MYJ\n0.2425\n0.1455\n0.7437\n0.3403\n0.5424\n551\n653\n1068\n5288\n0.1582\n0.1171\n0.6899\n0.2309\n0.6654\n172\n342\n573\n6473\nBMJ-WSM5-Duh-YSU\n0.2545\n0.154\n0.7931\n0.3638\n0.5413\n589\n695\n1030\n5246\n0.1698\n0.1257\n0.7758\n0.2577\n0.6678\n192\n386\n553\n6429\nBMJ-WSM5-God-MYJ\n0.2687\n0.1601\n0.9074\n0.404\n0.5548\n654\n815\n965\n5126\n0.174\n0.1286\n0.8201\n0.2698\n0.671\n201\n410\n544\n6405\nBMJ-WSM5-God-YSU\n0.2677\n0.1522\n1.0068\n0.4237\n0.5791\n686\n944\n933\n4997\n0.1674\n0.1166\n1.0027\n0.2872\n0.7135\n214\n533\n531\n6282\nBMJ-WSM6-Duh-MYJ\n0.2282\n0.1261\n0.7986\n0.3342\n0.5816\n541\n752\n1078\n5189\n0.1414\n0.097\n0.7772\n0.2201\n0.7168\n164\n415\n581\n6400\nBMJ-WSM6-Duh-YSU\n0.2471\n0.1426\n0.8394\n0.3644\n0.5659\n590\n769\n1029\n5172\n0.169\n0.1198\n0.9409\n0.2805\n0.7019\n209\n492\n536\n6323\nBMJ-WSM6-God-MYJ\n0.2484\n0.1255\n1.1081\n0.4194\n0.6215\n679\n1115\n940\n4826\n0.1471\n0.0931\n1.1141\n0.2711\n0.7566\n202\n628\n543\n6187\nBMJ-WSM6-God-YSU\n0.283\n0.1642\n1.0723\n0.4571\n0.5737\n740\n996\n879\n4945\n0.1588\n0.1053\n1.0966\n0.2872\n0.7381\n214\n603\n531\n6212\nKF-Lin-Duh-MYJ\n0.2144\n0.1147\n0.7634\n0.3113\n0.5922\n504\n732\n1115\n5209\n0.1299\n0.0905\n0.6349\n0.1879\n0.704\n140\n333\n605\n6482\nKF-Lin-Duh-YSU\n0.2312\n0.136\n0.7171\n0.3224\n0.5504\n522\n639\n1097\n5302\n0.124\n0.0847\n0.6309\n0.1799\n0.7149\n134\n336\n611\n6479\nKF-Lin-God-MYJ\n0.2584\n0.1355\n1.1174\n0.4348\n0.6108\n704\n1105\n915\n4836\n0.1304\n0.0781\n1.0362\n0.2349\n0.7733\n175\n597\n570\n6218\nKF-Lin-God-YSU\n0.2806\n0.1647\n1.0241\n0.4435\n0.5669\n718\n940\n901\n5001\n0.1611\n0.1097\n1.0215\n0.2805\n0.7254\n209\n552\n536\n6263\nKF-WSM3-Duh-MYJ\n0.266\n0.149\n1.0284\n0.4262\n0.5856\n690\n975\n929\n4966\n0.1735\n0.1212\n1.0604\n0.3047\n0.7127\n227\n563\n518\n6252\nKF-WSM3-Duh-YSU\n0.2839\n0.1719\n0.9691\n0.4355\n0.5507\n705\n864\n914\n5077\n0.1948\n0.1437\n1.0255\n0.3302\n0.678\n246\n518\n499\n6297\nKF-WSM3-God-MYJ\n0.2845\n0.1503\n1.3484\n0.5201\n0.6143\n842\n1341\n777\n4600\n0.1766\n0.1161\n1.4416\n0.3664\n0.7458\n273\n801\n472\n6014\nKF-WSM3-God-YSU\n0.3018\n0.1706\n1.3125\n0.5361\n0.5915\n868\n1257\n751\n4684\n0.1877\n0.1271\n1.455\n0.3879\n0.7334\n289\n795\n456\n6020\nKF-WSM5-Duh-MYJ\n0.2801\n0.1622\n1.055\n0.4497\n0.5738\n728\n980\n891\n4961\n0.1785\n0.1262\n1.0644\n0.3128\n0.7062\n233\n560\n512\n6255\nKF-WSM5-Duh-YSU\n0.2948\n0.1812\n1.0019\n0.4558\n0.545\n738\n884\n881\n5057\n0.2037\n0.1509\n1.102\n0.3557\n0.6772\n265\n556\n480\n6259\nKF-WSM5-God-MYJ\n0.284\n0.1477\n1.3904\n0.5287\n0.6197\n856\n1395\n763\n4546\n0.1685\n0.1064\n1.5221\n0.3638\n0.761\n271\n863\n474\n5952\nKF-WSM5-God-YSU\n0.2998\n0.1683\n1.3162\n0.5343\n0.5941\n865\n1266\n754\n4675\n0.1996\n0.1388\n1.4846\n0.4134\n0.7215\n308\n798\n437\n6017\nKF-WSM6-Duh-MYJ\n0.2799\n0.1571\n1.1353\n0.467\n0.5887\n756\n1082\n863\n4859\n0.1973\n0.1415\n1.2242\n0.3664\n0.7007\n273\n639\n472\n6176\nKF-WSM6-Duh-YSU\n0.3002\n0.1833\n1.0574\n0.475\n0.5508\n769\n943\n850\n4998\n0.211\n0.157\n1.157\n0.3758\n0.6752\n280\n582\n465\n6233\nKF-WSM6-God-MYJ\n0.2917\n0.155\n1.4095\n0.5442\n0.6139\n881\n1401\n738\n4540\n0.1788\n0.1154\n1.6107\n0.396\n0.7542\n295\n905\n450\n5910\nKF-WSM6-God-YSU\n0.3113\n0.1785\n1.357\n0.5596\n0.5876\n906\n1291\n713\n4650\n0.2101\n0.1475\n1.6054\n0.4523\n0.7182\n337\n859\n408\n5956Table 6\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 72 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.154\n0.0672\n0.3964\n0.1863\n0.53\n400\n451\n1747\n4962\n0.0797\n0.0443\n0.3412\n0.099\n0.7098\n101\n247\n919\n6293\nBMJ-Lin-Duh-YSU\n0.1539\n0.0595\n0.442\n0.1924\n0.5648\n413\n536\n1734\n4877\n0.0766\n0.0373\n0.3912\n0.099\n0.7469\n101\n298\n919\n6242\nBMJ-Lin-God-MYJ\n0.1755\n0.0611\n0.5752\n0.2352\n0.5911\n505\n730\n1642\n4683\n0.0719\n0.0285\n0.4471\n0.0971\n0.7829\n99\n357\n921\n6183\nBMJ-Lin-God-YSU\n0.1893\n0.0645\n0.653\n0.2632\n0.597\n565\n837\n1582\n4576\n0.0832\n0.0342\n0.5314\n0.1176\n0.7786\n120\n422\n900\n6118\nBMJ-WSM3-Duh-MYJ\n0.1802\n0.0656\n0.5771\n0.2408\n0.5827\n517\n722\n1630\n4691\n0.0867\n0.0391\n0.5108\n0.1206\n0.7639\n123\n398\n897\n6142\nBMJ-WSM3-Duh-YSU\n0.1988\n0.0737\n0.6572\n0.2748\n0.5819\n590\n821\n1557\n4592\n0.0935\n0.0407\n0.5941\n0.1363\n0.7706\n139\n467\n881\n6073\nBMJ-WSM3-God-MYJ\n0.2273\n0.0777\n0.871\n0.3465\n0.6021\n744\n1126\n1403\n4287\n0.0992\n0.0404\n0.7049\n0.1539\n0.7816\n157\n562\n863\n5978\nBMJ-WSM3-God-YSU\n0.2239\n0.0691\n0.9171\n0.3507\n0.6176\n753\n1216\n1394\n4197\n0.1022\n0.0373\n0.8294\n0.1696\n0.7955\n173\n673\n847\n5867\nBMJ-WSM5-Duh-MYJ\n0.1788\n0.0634\n0.5817\n0.2399\n0.5877\n515\n734\n1632\n4679\n0.0944\n0.0482\n0.4892\n0.1284\n0.7375\n131\n368\n889\n6172\nBMJ-WSM5-Duh-YSU\n0.2041\n0.0796\n0.6544\n0.2804\n0.5715\n602\n803\n1545\n4610\n0.0966\n0.0427\n0.6137\n0.1422\n0.7684\n145\n481\n875\n6059\nBMJ-WSM5-God-MYJ\n0.2169\n0.0787\n0.7667\n0.3149\n0.5893\n676\n970\n1471\n4443\n0.0908\n0.037\n0.6127\n0.1343\n0.7808\n137\n488\n883\n6052\nBMJ-WSM5-God-YSU\n0.2278\n0.0779\n0.8728\n0.3475\n0.6019\n746\n1128\n1401\n4285\n0.0947\n0.031\n0.802\n0.1559\n0.8056\n159\n659\n861\n5881\nBMJ-WSM6-Duh-MYJ\n0.1712\n0.0601\n0.5515\n0.2268\n0.5887\n487\n697\n1660\n4716\n0.0834\n0.0379\n0.4775\n0.1137\n0.7618\n116\n371\n904\n6169\nBMJ-WSM6-Duh-YSU\n0.1971\n0.069\n0.68\n0.2767\n0.5932\n594\n866\n1553\n4547\n0.0992\n0.0432\n0.652\n0.149\n0.7714\n152\n513\n868\n6027\nBMJ-WSM6-God-MYJ\n0.2191\n0.069\n0.8714\n0.3363\n0.6141\n722\n1149\n1425\n4264\n0.1\n0.0385\n0.7578\n0.1598\n0.7891\n163\n610\n857\n5930\nBMJ-WSM6-God-YSU\n0.215\n0.0632\n0.885\n0.3335\n0.6232\n716\n1184\n1431\n4229\n0.0824\n0.0185\n0.8039\n0.1373\n0.8293\n140\n680\n880\n5860\nKF-Lin-Duh-MYJ\n0.2144\n0.0871\n0.6782\n0.2962\n0.5632\n636\n820\n1511\n4593\n0.1162\n0.0632\n0.601\n0.1667\n0.7227\n170\n443\n850\n6097\nKF-Lin-Duh-YSU\n0.2063\n0.0825\n0.6502\n0.2823\n0.5659\n606\n790\n1541\n4623\n0.1069\n0.052\n0.6343\n0.1578\n0.7512\n161\n486\n859\n6054\nKF-Lin-God-MYJ\n0.2399\n0.0805\n0.9693\n0.381\n0.6069\n818\n1263\n1329\n4150\n0.1392\n0.0702\n0.9333\n0.2363\n0.7468\n241\n711\n779\n5829\nKF-Lin-God-YSU\n0.2427\n0.0831\n0.9725\n0.3852\n0.6039\n827\n1261\n1320\n4152\n0.1309\n0.0605\n0.9647\n0.2275\n0.7642\n232\n752\n788\n5788\nKF-WSM3-Duh-MYJ\n0.2377\n0.0889\n0.8677\n0.3586\n0.5867\n770\n1093\n1377\n4320\n0.1242\n0.0613\n0.7922\n0.198\n0.75\n202\n606\n818\n5934\nKF-WSM3-Duh-YSU\n0.2618\n0.1129\n0.8812\n0.3903\n0.5571\n838\n1054\n1309\n4359\n0.1675\n0.0983\n0.948\n0.2794\n0.7053\n285\n682\n735\n5858\nKF-WSM3-God-MYJ\n0.2596\n0.0844\n1.1495\n0.4429\n0.6147\n951\n1517\n1196\n3896\n0.1334\n0.0538\n1.2235\n0.2618\n0.7861\n267\n981\n753\n5559\nKF-WSM3-God-YSU\n0.2817\n0.1073\n1.1593\n0.4746\n0.5906\n1019\n1470\n1128\n3943\n0.1631\n0.081\n1.3216\n0.3255\n0.7537\n332\n1016\n688\n5524\nKF-WSM5-Duh-MYJ\n0.2508\n0.1005\n0.8882\n0.3787\n0.5737\n813\n1094\n1334\n4319\n0.1333\n0.0694\n0.8167\n0.2137\n0.7383\n218\n615\n802\n5925\nKF-WSM5-Duh-YSU\n0.2671\n0.1193\n0.8738\n0.395\n0.548\n848\n1028\n1299\n4385\n0.158\n0.0902\n0.9118\n0.2608\n0.714\n266\n664\n754\n5876\nKF-WSM5-God-MYJ\n0.2723\n0.0967\n1.1635\n0.463\n0.6021\n994\n1504\n1153\n3909\n0.1354\n0.0571\n1.1863\n0.2608\n0.7802\n266\n944\n754\n5596\nKF-WSM5-God-YSU\n0.2652\n0.0932\n1.1178\n0.4439\n0.6029\n953\n1447\n1194\n3966\n0.1507\n0.0709\n1.2382\n0.2931\n0.7633\n299\n964\n721\n5576\nKF-WSM6-Duh-MYJ\n0.2545\n0.0952\n0.9767\n0.401\n0.5894\n861\n1236\n1286\n4177\n0.1237\n0.0546\n0.9324\n0.2127\n0.7718\n217\n734\n803\n5806\nKF-WSM6-Duh-YSU\n0.2615\n0.1084\n0.9208\n0.3982\n0.5675\n855\n1122\n1292\n4291\n0.156\n0.0841\n1.0127\n0.2716\n0.7318\n277\n756\n743\n5784\nKF-WSM6-God-MYJ\n0.2693\n0.0883\n1.2259\n0.4723\n0.6147\n1014\n1618\n1133\n3795\n0.1398\n0.0571\n1.3265\n0.2853\n0.7849\n291\n1062\n729\n5478\nKF-WSM6-God-YSU\n0.2853\n0.1072\n1.2054\n0.4895\n0.5939\n1051\n1537\n1096\n3876\n0.1513\n0.0661\n1.4245\n0.3186\n0.7763\n325\n1128\n695\n5412\n\n\n## 3.2. Skill Score Validation\n\nThe charts for the skill scores at the two thresholds in Figures5 and 6 show that the TS value at the 24 h forecast range is about 0.2 to 0.27 for >25 mm/24 h and 0.1 to 0.2 for >50 mm/24 h. At 48 h, the TS is around ∼0.18 to 0.3 and ∼0.1 to 0.19 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h. At 72 h, the TS is around 0.15 to 0.25 and ∼0.08 to 0.15 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h.Figure 5\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 25 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure 6\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 50 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)The probability of detection decreases and the false alarm rate clearly increases with forecast ranges and the validating thresholds. In addition, when the threshold increases, the difference between the TS and the ETS decreases which means that the amount of Hitsrandom is too small or the cause of very small hit rate (Hitsrandom rate decreases by 90% when changing the threshold from >25 mm/24 h to >50 mm/24 h).Specific comparisons between the combinations of the BMJ or KF scheme show that the KF scheme model’s skills in heavy rain forecast in the northern region of Vietnam are better than BMJ scheme model’s skills. The average TS with the KF scheme can be about 15–25% larger than that using the BMJ scheme. If the difference of skills in a regional model is insignificant when changing the physical parameterization schemes, the lateral boundary conditions (from global forecasts) will greatly affect the quality of the dynamical downscaling forecasts after 24 h integration. However, here the skill difference when combining the two different cumulus schemes in the longer forecast range (such as 72 h) shows the importance of convection simulation capability contributing to the forecasting quality of the model. Detailed evaluation of the combination with the radiation physical schemes of KF or BMJ does not show any difference compared to changing the boundary layer schemes when looking at the skill scores TS or ETS.The combinations with YSU boundary layer schemes have better skills compared to those with MYJ schemes. In addition, when changing the complexity of the cloud microphysics schemes, the more complex the microphysical processing simulation, the better the TS and ETS (at 24, 48, and 72 h forecast ranges and two validating thresholds; see Figure7 for comparison of the change in TSs and ETSs with the cloud microphysics scheme).Figure 7\nBrief comparison of TSs and ETSs for illustration of sensitivities with microphysics schemes.For the skill comparisons of different event types (I, II, III, and IV), Figure8 shows the TSs at thresholds over 25 mm/24 h and over 50 mm/24 h for 24 h and 48 h forecast ranges. For type I, which is associated with the activity of ITCZ and low-pressure trough over the Bac Bo area, the KF scheme proved its forecast skills in almost forecast ranges and thresholds mentioned in this research. However, with the rain caused by tropical cyclone in type II, the difference between KF and BMJ schemes within 24 h was smaller than that in type I. In 48 h and 72 h, the BMJ scheme showed more skilled forecast with the skill score for threshold 25 mm ranging from 0.25 to 0.35 of the BMJ scheme, compared with 0.2 to 0.3 of the KF scheme, and the skill score for threshold 50 mm ranging from 0.2 to 0.3 of the BMJ scheme, compared with 0.2 to 0.25 of the KF scheme. In type II, both KF and BMJ schemes combined with the simple cloud microphysics Lin scheme showed lowest skill score. For type III, which is associated with the activity of cold surge and its role in squeezing the low-pressure trough from the north towards Bac Bo, the KF scheme was only skillful in threshold 25 mm in 24 h. Particularly in type IV, which contains heavy rain events caused by a complex combination of situations resulting in a trough in Bac Bo, the KF scheme still showed skilled forecast compared to very low forecast (no skill with threshold over 50 mm/24 h and 25 mm/24 h) of BMJ scheme experiments. More details of TSs for different types are listed in Tables 7 and 8.Figure 8\nTSs for different types of heavy rainfall events in northern Vietnam for daily accumulation thresholds over (a) 25 mm and 50 mm (b) at 24 h forecast ranges and over 25 mm (c) and 50 mm (d) at 48 h forecast ranges. The right vertical axis is from 0 to 0.4.\n(a)\n(b)\n(c)\n(d)Table 7\nTSs for different types of main heavy rainfall, at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.2546\n0.1309\n0.15\n0.0682\n0.124\n0.0791\n0.0769\n0\nBMJ-Lin-Duh-YSU\n0.2625\n0.1019\n0.2346\n0.0706\n0.1244\n0.0692\n0.16\n0\nBMJ-Lin-God-MYJ\n0.2644\n0.1638\n0.2235\n0.1011\n0.139\n0.1064\n0.0882\n0\nBMJ-Lin-God-YSU\n0.2761\n0.1519\n0.1939\n0.093\n0.1369\n0.087\n0.1562\n0.0333\nBMJ-WSM3-Duh-MYJ\n0.2503\n0.1237\n0.1818\n0.09\n0.1279\n0.0621\n0.129\n0\nBMJ-WSM3-Duh-YSU\n0.2745\n0.1179\n0.2439\n0.0652\n0.1379\n0.0699\n0.1471\n0\nBMJ-WSM3-God-MYJ\n0.2834\n0.1651\n0.21\n0.09\n0.1556\n0.1104\n0.1136\n0\nBMJ-WSM3-God-YSU\n0.2943\n0.1505\n0.202\n0.0745\n0.167\n0.0798\n0.1304\n0.0312\nBMJ-WSM5-Duh-MYJ\n0.2618\n0.1349\n0.1977\n0.0707\n0.1429\n0.0563\n0.1515\n0\nBMJ-WSM5-Duh-YSU\n0.263\n0.1351\n0.2073\n0.0652\n0.1603\n0.0789\n0.1389\n0\nBMJ-WSM5-God-MYJ\n0.3032\n0.1376\n0.1978\n0.0714\n0.1565\n0.0496\n0.1515\n0.0345\nBMJ-WSM5-God-YSU\n0.3028\n0.1525\n0.1939\n0.0737\n0.1677\n0.0696\n0.15\n0.0312\nBMJ-WSM6-Duh-MYJ\n0.2571\n0.133\n0.1304\n0.0926\n0.1403\n0.0872\n0.0732\n0\nBMJ-WSM6-Duh-YSU\n0.2643\n0.1281\n0.1828\n0.06\n0.1714\n0.0719\n0.1212\n0\nBMJ-WSM6-God-MYJ\n0.2991\n0.1781\n0.2574\n0.0784\n0.1706\n0.1391\n0.15\n0.0256\nBMJ-WSM6-God-YSU\n0.3043\n0.1595\n0.1944\n0.0652\n0.1916\n0.1104\n0.1429\n0\nKF-Lin-Duh-MYJ\n0.2881\n0.1673\n0.2136\n0.1449\n0.1844\n0.0683\n0.1026\n0.1818\nKF-Lin-Duh-YSU\n0.3125\n0.1951\n0.2474\n0.1667\n0.1778\n0.0698\n0.06\n0.1892\nKF-Lin-God-MYJ\n0.2917\n0.1914\n0.2255\n0.1558\n0.1725\n0.1237\n0.0889\n0.1471\nKF-Lin-God-YSU\n0.3129\n0.183\n0.2653\n0.1769\n0.1839\n0.1162\n0.093\n0.1316\nKF-WSM3-Duh-MYJ\n0.2872\n0.1776\n0.2212\n0.1655\n0.1893\n0.0753\n0.1224\n0.1667\nKF-WSM3-Duh-YSU\n0.309\n0.1881\n0.2551\n0.219\n0.1858\n0.1176\n0.1111\n0.2059\nKF-WSM3-God-MYJ\n0.3264\n0.1924\n0.2526\n0.1656\n0.2057\n0.0909\n0.0889\n0.1579\nKF-WSM3-God-YSU\n0.3188\n0.1841\n0.3069\n0.2014\n0.2027\n0.1185\n0.098\n0.1795\nKF-WSM5-Duh-MYJ\n0.3062\n0.1836\n0.25\n0.1655\n0.2048\n0.0688\n0.0851\n0.1795\nKF-WSM5-Duh-YSU\n0.3116\n0.1892\n0.2653\n0.1838\n0.1823\n0.1111\n0.08\n0.1818\nKF-WSM5-God-MYJ\n0.3141\n0.1996\n0.25\n0.1772\n0.2047\n0.1122\n0.1064\n0.1622\nKF-WSM5-God-YSU\n0.3317\n0.2025\n0.2642\n0.1875\n0.2063\n0.1085\n0.1228\n0.1579\nKF-WSM6-Duh-MYJ\n0.3063\n0.1823\n0.2233\n0.1812\n0.1963\n0.0952\n0.0833\n0.1538\nKF-WSM6-Duh-YSU\n0.3339\n0.1927\n0.2476\n0.1898\n0.2244\n0.1179\n0.1071\n0.2812\nKF-WSM6-God-MYJ\n0.3138\n0.1781\n0.2268\n0.1465\n0.2207\n0.1268\n0.1429\n0.125\nKF-WSM6-God-YSU\n0.3323\n0.1793\n0.2843\n0.2\n0.2078\n0.1255\n0.1538\n0.1429Table 8\nTSs for different types of main heavy rainfall, at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.1629\n0.2426\n0.0854\n0.0922\n0.0768\n0.1533\n0.0526\n0.0333\nBMJ-Lin-Duh-YSU\n0.1755\n0.2271\n0.0909\n0.1438\n0.0971\n0.1559\n0.0833\n0.0164\nBMJ-Lin-God-MYJ\n0.2276\n0.3104\n0.075\n0.0798\n0.1139\n0.1631\n0.0333\n0.0303\nBMJ-Lin-God-YSU\n0.2482\n0.2723\n0.1429\n0.12\n0.1405\n0.1543\n0.0385\n0.0167\nBMJ-WSM3-Duh-MYJ\n0.2235\n0.3055\n0.0833\n0.1311\n0.1147\n0.2848\n0.0571\n0\nBMJ-WSM3-Duh-YSU\n0.2365\n0.3419\n0.1538\n0.1617\n0.1136\n0.3072\n0.0556\n0.0116\nBMJ-WSM3-God-MYJ\n0.2583\n0.3448\n0.0979\n0.1029\n0.1386\n0.2466\n0.0182\n0\nBMJ-WSM3-God-YSU\n0.2587\n0.3338\n0.1176\n0.1272\n0.1541\n0.2125\n0.02\n0\nBMJ-WSM5-Duh-MYJ\n0.2301\n0.3206\n0.0928\n0.1228\n0.1287\n0.2706\n0.0741\n0.0115\nBMJ-WSM5-Duh-YSU\n0.243\n0.3157\n0.1373\n0.172\n0.1397\n0.2813\n0.0312\n0.0241\nBMJ-WSM5-God-MYJ\n0.2594\n0.344\n0.1327\n0.1337\n0.1479\n0.2928\n0.0286\n0\nBMJ-WSM5-God-YSU\n0.2588\n0.3329\n0.1797\n0.1364\n0.1383\n0.2918\n0\n0.0115\nBMJ-WSM6-Duh-MYJ\n0.217\n0.304\n0.1418\n0.1027\n0.1214\n0.2212\n0.0784\n0.0235\nBMJ-WSM6-Duh-YSU\n0.2331\n0.3216\n0.1368\n0.1587\n0.1348\n0.2968\n0.0714\n0.0103\nBMJ-WSM6-God-MYJ\n0.2522\n0.3055\n0.1474\n0.0955\n0.132\n0.2314\n0.0541\n0.0215\nBMJ-WSM6-God-YSU\n0.2739\n0.3707\n0.2\n0.1026\n0.1333\n0.2658\n0.0484\n0.0319\nKF-Lin-Duh-MYJ\n0.2259\n0.2093\n0.0811\n0.225\n0.1331\n0.1208\n0.027\n0.1899\nKF-Lin-Duh-YSU\n0.2714\n0.172\n0.0957\n0.2199\n0.1347\n0.1017\n0.0667\n0.1395\nKF-Lin-God-MYJ\n0.268\n0.2537\n0.1043\n0.2771\n0.1267\n0.1545\n0.0385\n0.1237\nKF-Lin-God-YSU\n0.3125\n0.2384\n0.1058\n0.2788\n0.1704\n0.1608\n0.0488\n0.1327\nKF-WSM3-Duh-MYJ\n0.2689\n0.2805\n0.1298\n0.2783\n0.156\n0.241\n0.0577\n0.1327\nKF-WSM3-Duh-YSU\n0.3066\n0.2739\n0.069\n0.285\n0.1971\n0.2068\n0.0444\n0.2088\nKF-WSM3-God-MYJ\n0.2945\n0.2976\n0.1286\n0.2595\n0.1651\n0.2451\n0.0526\n0.1111\nKF-WSM3-God-YSU\n0.3202\n0.2956\n0.1407\n0.2822\n0.1872\n0.2136\n0.0923\n0.1569\nKF-WSM5-Duh-MYJ\n0.2924\n0.2921\n0.1176\n0.2546\n0.1586\n0.238\n0.0727\n0.1765\nKF-WSM5-Duh-YSU\n0.3217\n0.2847\n0.0976\n0.26\n0.1995\n0.2403\n0.0755\n0.1753\nKF-WSM5-God-MYJ\n0.2946\n0.2953\n0.1192\n0.2672\n0.1499\n0.25\n0.038\n0.1293\nKF-WSM5-God-YSU\n0.3265\n0.2768\n0.1361\n0.2881\n0.201\n0.2344\n0.0959\n0.1404\nKF-WSM6-Duh-MYJ\n0.2922\n0.2874\n0.1389\n0.2597\n0.1874\n0.25\n0.0685\n0.1667\nKF-WSM6-Duh-YSU\n0.3291\n0.2706\n0.1311\n0.2995\n0.208\n0.2378\n0.0893\n0.2083\nKF-WSM6-God-MYJ\n0.3002\n0.3115\n0.1465\n0.2556\n0.1675\n0.2537\n0.0435\n0.1333\nKF-WSM6-God-YSU\n0.3426\n0.2798\n0.1595\n0.2888\n0.2091\n0.2565\n0.0674\n0.177\n\n\n## 4. Conclusions\n\nFor the purpose of investigating the effects of physical schemes in the WRF-ARW model on the operational heavy rainfall forecast for the Bac Bo area, 32 different model configurations have been established by switching two typical cumulus parameterization schemes (BMJ and KF), the cloud microphysics schemes from simple (Lin) to complex (WSM with 3/5/6-layer closure assumptions), and boundary layer (YSU and MYJ) and shortwave radiation (Dudhia and Goddard) schemes. The 72 experiments of widespread heavy rainfall occurring in the Bac Bo area used boundaries from the GFS model and had the highest horizontal resolution of 5 km × 5 km.The model verification with local observation data illustrated the limited capabilities in heavy rainfall forecast for the northern part of Vietnam. On average, for the threshold over 25 mm/24 h, the TSs are from 0.2 to 0.25, 0.2 to 0.3, and 0.2 to 0.25 for 24 h, 48 h, and 72 h forecast ranges, respectively. For the threshold over 50 mm/24 h, TSs are 0.1–0.2 for 24 h and 48 h forecast ranges and about 0.1–0.15 for the 72 h forecast range. At above the 100 mm/24 h threshold, a very low skill value (below 0.1) is validated for most forecast ranges.The change of the microcloud physics from simple to complex closure assumptions shows that complex schemes give very positive results for both the BMJ and KF schemes. The model configurations with the KF scheme showed higher skills compared to BMJ scheme configurations, and these higher skill scores (TS and POD) mainly come from a higher hit rate (H) and a lower missed rate (M) but also a higher false alarm rate (F). More preliminary assessment for the KF scheme configurations showed the most sensitivity of boundary layer schemes compared to microphysics or shortwave radiation schemes and some initial comments that boundary layer interaction during the application of the KF scheme is an important factor to find the appropriate parameters for heavy rainfall forecast over the Bac Bo area in the WRF-ARW model.In terms of sample size of each category, the first two types are comparable because of the similar sample size. The other two types, however, are limited in sample size and need to be further analyzed in the subsequent research. A detailed assessment related to the origin of the mechanisms causing heavy rain illustrates that the KF scheme showed more skilled forecast with the BMJ scheme during trough- or ITCZ-related heavy rain events, whereas it was less skilled in the events caused by tropical cyclones. With the events caused by the cold surge and a combination of different patterns, the skill of the BMJ scheme was quite low. More verification with the last two types needs to be further investigated in the subsequent research because of the limited sample size studied.\n\n\n\n---\n\n*Source: 1010858-2019-08-18.xml*"},"filename":{"kind":"string","value":"1010858-2019-08-18_1010858-2019-08-18.md"},"file_size":{"kind":"number","value":94294,"string":"94,294"},"title":{"kind":"string","value":"Impacts of Different Physical Parameterization Configurations on Widespread Heavy Rain Forecast over the Northern Area of Vietnam in WRF-ARW Model"},"authors":{"kind":"string","value":"Tien Du Duc; Cuong Hoang Duc; Lars Robert Hole; Lam Hoang; Huyen Luong Thi Thanh; Hung Mai Khanh"},"journal":{"kind":"string","value":"Advances in Meteorology\n (2019)"},"category":{"kind":"string","value":"Earth and Environmental Sciences"},"publisher":{"kind":"string","value":"Hindawi"},"license":{"kind":"string","value":"CC BY 4.0"},"license_url":{"kind":"string","value":"http://creativecommons.org/licenses/by/4.0/"},"doi":{"kind":"string","value":"10.1155/2019/1010858"},"source_file":{"kind":"string","value":"1010858-2019-08-18.xml"},"content":{"kind":"string","value":"---\n\n## Abstract\n\nThis study investigates the impacts of different physical parameterization schemes in the Weather Research and Forecasting model with the ARW dynamical core (WRF-ARW model) on the forecasts of heavy rainfall over the northern part of Vietnam (Bac Bo area). Various physical model configurations generated from different typical cumulus, shortwave radiation, and boundary layer and from simple to complex cloud microphysics schemes are examined and verified for the cases of extreme heavy rainfall during 2012–2016. It is found that the most skilled forecasts come from the Kain–Fritsch (KF) scheme. However, relating to the different causes of the heavy rainfall events, the forecast cycles using the Betts–Miller–Janjic (BMJ) scheme show better skills for tropical cyclones or slowly moving surface low-pressure system situations compared to KF scheme experiments. Most of the sensitivities to KF scheme experiments are related to boundary layer schemes. Both configurations using KF or BMJ schemes show that more complex cloud microphysics schemes can also improve the heavy rain forecast with the WRF-ARW model for the Bac Bo area of Vietnam.\n\n\n---\n\n## Body\n\n## 1. Introduction\n\nElongated to 15 degrees (from 8 to 23 degrees north), Vietnam weather is affected by a variety of extratropical and tropical systems such as cold surges and cold fronts, subtropical troughs and monsoon, tropical disturbances, and typhoons [1–3, 4]. An approximate number of 28–30 heavy rain events occurring over the whole Vietnam region every year were determined from local surface stations. According to Nguyen et al. [4], the rainy season in the Vietnam area usually lasts from May to October with the peak rainy months starting in the north and then moving to the south with time because of the movement of the subtropical ridge and Intertropical Convergence Zone (ITCZ). In the northern part of Vietnam, the peak rainy months can be seen during the June–September period, particularly in July and August, which is partly related to the activity of the ITCZ [5].Apart from single weather patterns causing heavy rain for Vietnam, it is common to observe a mix of different patterns simultaneously. A good example for this is the historical rain and flood over Central Vietnam in the Hue city in 1999 due to the combination of cold surges and tropical depression-type disturbances [6]. In 2008, extreme heavy rain was witnessed in Hanoi as a result of the dramatic intensification of the easterly disturbance through the midlatitude-tropical interactions [7]. Likewise, a surface low pressure which coincided with the subtropical upper-level trough was the main factor causing historical rainfall over northeastern Vietnam in Quang Ninh Province in 2015 [8].In the northern part of Vietnam (hereinafter referred to as Bac Bo area)—the focus area of this study—tropical cyclones or surface low-pressure patterns are one among several key factors leading to heavy rain for different regions in Vietnam. ITCZ plays the vital role in facilitating the development of low-level vortexes which are likely to develop into tropical disturbances and typhoons in the summer time, particularly in July and August. As a result, rain may fall over the Bac Bo area with excessive amount, and rain duration directly depends upon the lifetime of ITCZ and the disturbances itself. The other causing reasons are related to the effects of cold surge from the north (Siberian area) or the combinations of different patterns (a cold surge with a tropical cyclone, a cold surge with a trough, a surface low-pressure system with a trough, etc.). Table1 provides the observed precipitation from SYNOP stations in the Bac Bo area with the annual rainfall mostly ranging from 1500 to 2000 mm and a high number of daily heavy rain occurrences (over 50 mm/24 h and 100 mm/24 h).Table 1\nStation information over the northern part of Vietnam, annual rainfall (over the period 1998–2018) in mm, and the number of days with daily accumulated rainfall over 50 mm/24 h (#50 mm) and over 100 mm/24 h (#100 m) in average. The number of samples is ∼7300 each station.\nStation name\nLong.\nLat.\nAnnual rain\n#50 mm\n#100 mm\nMuong Te\n102.83\n22.37\n2423\n11\n2\nSin Ho\n103.23\n22.37\n2787\n13\n2\nTam Duong\n103.48\n22.42\n2355\n9\n2\nMuong La\n104.03\n21.52\n1412\n5\n1\nThan Uyen\n103.88\n21.95\n1920\n8\n1\nQuynh Nhai\n103.57\n21.85\n1678\n7\n1\nMu Cang Chai\n104.05\n21.87\n1741\n5\n1\nTuan Giao\n103.42\n21.58\n1586\n5\n1\nPha Din\n103.5\n21.57\n1840\n6\n1\nVan Chan\n104.52\n21.58\n1481\n6\n1\nSong Ma\n103.75\n21.5\n1118\n2\n0\nCo Noi\n104.15\n21.13\n1308\n4\n0\nYen Chau\n104.3\n21.05\n1229\n3\n0\nBac Yen\n104.42\n21.23\n1519\n4\n0\nPhu Yen\n104.63\n21.27\n1496\n5\n1\nMinh Dai\n105.05\n21.17\n1695\n6\n1\nMoc Chau\n104.68\n20.83\n1581\n5\n1\nMai Chau\n105.05\n20.65\n1698\n7\n1\nPho Rang\n104.47\n22.23\n1648\n7\n1\nBac Ha\n104.28\n22.53\n1611\n4\n0\nHoang Su Phi\n104.68\n22.75\n1739\n5\n1\nBac Me\n105.37\n22.73\n1753\n6\n0\nBao Lac\n105.67\n22.95\n1179\n3\n0\nBac Quang\n104.87\n22.5\n4295\n24\n9\nLuc Yen\n104.78\n22.1\n1917\n8\n1\nHam Yen\n105.03\n22.07\n1858\n8\n1\nChiem Hoa\n105.27\n22.15\n1631\n6\n1\nCho Ra\n105.73\n22.45\n1417\n5\n0\nNguyen Binh\n105.9\n22.65\n1686\n6\n1\nNgan Son\n105.98\n22.43\n1853\n8\n1\nTrung Khanh\n106.57\n22.83\n1821\n7\n2\nDinh Hoa\n105.63\n21.92\n1749\n8\n2\nBac Son\n106.32\n21.9\n1693\n7\n2\nHuu Lung\n106.35\n21.5\n1572\n7\n1\nDinh Lap\n107.1\n21.53\n1865\n7\n2\nQuang Ha\n107.75\n21.45\n2883\n16\n5\nPhu Ho\n105.23\n21.45\n1480\n6\n1\nTam Dao\n105.65\n21.47\n2585\n13\n4\nHiep Hoa\n105.97\n21.35\n1601\n6\n1\nBac Ninh\n106.08\n21.18\n1608\n7\n2\nLuc Ngan\n106.55\n21.38\n1406\n6\n1\nSon Dong\n106.85\n21.33\n1686\n8\n2\nBa Vi\n105.42\n21.15\n1942\n9\n2\nHa Dong\n105.75\n20.97\n1635\n7\n1\nChi Linh\n106.38\n21.08\n1536\n7\n1\nUong Bi\n106.75\n21.03\n1824\n9\n2\nKim Boi\n105.53\n20.33\n2115\n9\n2\nChi Ne\n105.78\n20.48\n1841\n8\n2\nLac Son\n105.45\n20.45\n2040\n9\n2\nCuc Phuong\n105.72\n20.25\n1918\n9\n2\nYen Dinh\n105.67\n19.98\n1509\n7\n1\nSam Son\n105.9\n19.75\n1759\n9\n3\nDo Luong\n105.3\n18.89\n1871\n9\n2\nLai Chau\n103.15\n22.07\n2178\n10\n2\nSa Pa\n103.82\n22.35\n2581\n10\n2\nLao Cai\n103.97\n22.5\n1704\n7\n1\nHa Giang\n104.97\n22.82\n2372\n12\n2\nSon La\n103.9\n21.33\n1456\n4\n1\nThat Khe\n106.47\n22.25\n1482\n6\n1\nCao Bang\n106.25\n22.67\n1439\n6\n1\nBac Giang\n106.22\n21.3\n1518\n7\n1\nHon Ngu\n105.77\n18.8\n2007\n11\n4\nBac Can\n105.83\n22.15\n1421\n5\n1\nDien Bien Phu\n103\n21.37\n1536\n5\n1\nTuyen Quang\n105.22\n21.82\n1654\n8\n2\nViet Tri\n105.42\n21.3\n1601\n8\n2\nVinh Yen\n105.6\n21.32\n1489\n6\n1\nYen Bai\n104.87\n21.7\n1768\n8\n1\nSon Tay\n105.5\n21.13\n1612\n7\n1\nHoa Binh\n105.33\n20.82\n1828\n8\n2\nHuong Son\n105.43\n18.52\n2135\n10\n4\nHa Noi\n105.8\n21.03\n1647\n8\n2\nPhu Ly\n105.92\n20.55\n1731\n8\n2\nHung Yen\n106.05\n20.65\n1381\n6\n1\nNam Dinh\n106.15\n20.39\n1564\n7\n1\nNinh Binh\n105.97\n20.23\n1662\n8\n2\nPhu Lien\n106.63\n20.8\n1572\n7\n1\nHai Duong\n106.3\n20.93\n1561\n7\n1\nHon Dau\n106.8\n20.67\n1501\n8\n1\nVan Ly\n106.3\n20.12\n1620\n9\n2\nLang Son\n106.77\n21.83\n2874\n12\n2\nThai Nguyen\n105.83\n21.6\n1266\n5\n1\nNho Quan\n105.73\n20.32\n1728\n8\n2\nBai Chay\n107.07\n20.97\n1700\n8\n2\nCo To\n107.77\n20.98\n1933\n10\n3\nThai Binh\n106.35\n20.45\n1930\n10\n3\nCua Ong\n107.35\n21.02\n1591\n8\n1\nTien Yen\n107.4\n21.33\n2189\n12\n3\nMong Cai\n107.97\n21.52\n2168\n10\n3\nBach Long Vi\n107.72\n20.13\n2670\n15\n5\nHuong Khe\n105.72\n18.18\n1216\n6\n1\nThanh Hoa\n105.78\n19.75\n2516\n12\n5\nHoi Xuan\n105.12\n20.37\n1701\n8\n3\nTuong Duong\n104.43\n19.28\n1720\n6\n1\nVinh\n105.7\n18.67\n1305\n5\n1\nHa Tinh\n105.9\n18.35\n1896\n10\n4\nKy Anh\n106.28\n18.07\n2507\n13\n5\nBai Thuong\n105.38\n19.9\n1830\n7\n2\nNhu Xuan\n105.57\n19.63\n1758\n8\n3\nTinh Gia\n105.78\n19.45\n1777\n9\n3\nQuy Chau\n105.12\n19.57\n1661\n7\n1\nQuy Hop\n105.15\n19.32\n1579\n7\n2\nTay Hieu\n105.4\n19.32\n1517\n7\n2\nQuynh Luu\n105.63\n19.17\n1559\n8\n2\nCon Cuong\n104.88\n19.05\n1647\n7\n2In Vietnam National Center for Hydro-Meteorological Forecasting (NCHMF), several global model (NWP) products are mostly applied in operational forecast to predict the occurrence of heavy rain. These include the models from National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), and Germany’s National Meteorological Service (DWD). In addition, some regional NWP products including the High Resolution Regional Model (HRM) (the HRM’s information can be found at DWD’s internet linkhttps://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/hrm/HRM_users_guide.pdf) [9], the Consortium for Small-Scale Modeling (COSMO) [10] models from DWD, and the Weather Research and Forecasting (WRF-ARW) model [11] from the National Center for Atmospheric Research (NCAR) are also a useful reference source in the operational heavy rainfall forecast of Vietnam [12]. Despite the predictability of these models in some certain cases, they may fail to predict extreme events because of several reasons. Lorenz [13] pointed out the three main factors causing uncertainties in NWP are the initial conditions, the imperfection of the models, and the chaos of the atmosphere. While the initial condition problem for NWP can be reduced by data assimilation methods, the imperfection of models, which relates to many subgrid processes, can be alleviated by using proper physical parameterizations. For regional weather forecasting centers with limited capabilities in data assimilation and resource computation to provide the cloud resolved resolution forecast, choosing correct physical parameterization schemes still plays the most important role in downscaling the processes in regional NWP models [14].To illustrate the dependence of heavy rainfall forecast on physical parameterizations, the typical heavy rainfall event relating to the activities of ITCZ over the South China Sea—the East Sea of Vietnam—from 27 to 30 August 2014 in the Bac Bo area was simulated by the WRF-ARW model (see mean sea level pressure analysis of the GFS model in Figure1(a)). The 3-day accumulated rainfall was mostly from 100 mm to 150 mm, and some stations recorded more than 200 mm such as Kim Boi which recorded 229 mm (Hoa Binh Province; marked with a square in Figure 1(b)), Dinh Lap 245 mm (Lang Son Province, the northeast area; marked with a star in Figure 1(b)), and Tam Dao 341 mm (Vinh Phuc Province, center of the domain; marked with a circle in Figure 1(b)).Figure 1\nExample of impact of different physical parameterization schemes on heavy rain forecast with the WRF-ARW model (5 km horizontal resolution) over the Bac Bo area (the northern part of Vietnam), issued at 00UTC 27/08/2014. (a) Analysis of mean sea level pressure from the GFS at 00UTC 27/08/2014. (b) 72 h accumulated precipitation from synoptic observation from 00UTC 27/08/2014 to 00UTC 30/08/2014. 72 h accumulated precipitation forecast from the WRF-ARW model with the configuration of KF cumulus parameterization and shortwave radiation and cloud microphysics schemes: (c) KF-Lin-Duh-MYJ; (d) KF-WSM3-Duh-MYJ; (e) KF-WSM5-God-MYJ; (f) KF-WSM5-God-YSU. 72 h accumulated precipitation forecast from the WRF-ARW model with the configuration of BMJ cumulus parameterization: (g) BMJ-Lin-Duh-MYJ; (h) BMJ-WSM3-Duh-MYJ; (i) BMJ-WSM5-God-MYJ; (j) BMJ-WSM5-God-YSU. More explanation of model configurations from (c) to (j) experiments can be found in Table2.\n(a)\n(b)\n(c)\n(d)\n(e)\n(f)\n(g)\n(h)\n(i)\n(j)Figure1 illustrates 72 h accumulated rainfall forecasts from the WRF-ARW model at 5 km horizontal resolution, issued at 00UTC 27/08/2014 with different physical parameterization configurations by combining the Betts–Miller–Janjic (BMJ) or Kain–Fritsch (KF) cumulus schemes, the Lin or WRF single-moment three- or five-class (WSM3/WSM5) cloud microphysics schemes, the Dudhia or Goddard shortwave radiation schemes, and the Yonsei University (YSU) or Mellor–Yamada–Janjic (MYJ) boundary layer schemes.With the KF cumulus parameterization scheme in Figures1(c)–1(f), the results show that extreme rainfall over the northeast area (Lang Son Province) can be predictive with the amount of rainfall over >150 mm but was forecasted overestimation for the mountainous regions over the northwest area (Hoang Lien Son mountain ranges). The extreme rainfall over the center of the domain (Vinh Phuc Province) was not simulated well using the KF scheme for this situation.For the experiments using the BMJ scheme in Figures1(g)–1(j), the extreme rainfall over the Lang Son Province is underestimated, but cases using Lin or WSM3 for microphysics, Dudhia for shortwave radiation scheme, and the MYJ surface boundary layer (Figure 1(g) and 1(h)) can reduce the overestimation related to Hoang Lien Son mountain ranges in KF scheme experiments. The extreme heavy rainfall over the center of the domain (in Vinh Phuc Province) can be quite well forecasted when using WSM5 with Goddard Hoang Lien Son mountain ranges and the MYJ surface boundary layer (Figure 1(i)). The same configuration in Figure 1(i) but using another surface boundary layer (YSU) scheme provided totally different results (Figure 1(j)) for the southern area of the domain (overestimation).Many studies have been carried out for validating the effects of physical parameterization schemes in the WRF-ARW model. Zeyaeyan et. al. [15] evaluated the effect of various physic schemes in the WRF-ARW model on the simulation of summer rainfall over the northwest of Iran (NWI). The result shows that cumulus schemes are the most sensitive and microphysics schemes are the least sensitive. The comparison between 15 km and 5 km resolution simulations does not show obvious advantages in downscaling. These investigations showed the best results for both the 5 and 15 km resolutions with model configurations from the newer Tiedtke cumulus scheme, MYJ scheme, and WSM3/Kessler microphysics scheme. Tan [16] processed sensitivity tests with microphysics parameterization of the WRF-ARW model in the concept of quantitative extreme precipitation forecasting for hydrological inputs. About 19 bulk microphysics parameterization schemes were evaluated for a storm situation in California in 1997. The most important finding was that the extreme and short-interval simulated precipitation of the WRF-ARW model, which is very important for hydrological forecast input, can be improved by the choice of microphysics schemes. Nasrollahi et al. [17] showed that different features from hurricane (track, intensity, and precipitation) in the WRF-ARW model can be improved with suitable selections of microphysics and cumulus schemes. These results showed that the best simulated precipitation can be achieved by using BMJ cumulus parameterization combined with the WSM5 microphysics scheme, but the hurricane’s track was best estimated by using the Lin or Kessler microphysics option with BMJ cumulus parameterization. Other validations of parameterization of physical processes on the tropical cyclone of Pattanayak et al. [18] with WRF for the NMM dynamical core showed the important role of cumulus parameterization in track forecasts, thereby contributing to rainfall induced by landfall of tropical cyclones.In the other aspects related to using cumulus parameterization schemes at high-resolution simulations, Gilliland et al. [19] compared simulations using different cumulus parameterization schemes in summer-time convective activities. Compared with the model simulations at below 5 km horizontal resolution, which resolve the convection, the study showed that depending on the strength of the synoptic-scale forcing, the use of a cumulus parameterization scheme can still be warrant for representing the effects of subgrid-scale convective processes (the Kain–Fritsch scheme in this study).Thus, the application of regional models to certain regions such as Vietnam will be influenced significantly by local factors (topography and microclimate mode) as well as the effects of physical combinations. Among regional models which are applied in Vietnam, the WRF-ARW model is the most useful tool because of the capabilities of providing different configuration physical/dynamical models for implementation to various scientific communities for both research and operation. For this reason, this study will focus on the impact of some physical parameterization configurations of the WRF-ARW model combining cumulus, cloud microphysics, and shortwave radiation parameterization schemes on heavy rainfall forecast. Two typical cumulus parameterizations (adjustment and mass-flux approaches) will be investigated with the combination of simple to complex cloud microphysics schemes. The verification of dependence of the typical synoptic situations/weather patterns causing heavy rainfall for the Bac Bo area on parameterization schemes is also carried out. For the real-time adaptation forecasting verification purposes, the lateral boundary condition will be used from the Global Forecast System (GFS) of NCEP.The remainder of this paper is organized as follows: In Section2, experimental design and validation methods will be presented. Section 3 discusses the impacts of different physical parameterization configurations on heavy rainfall over the Bac Bo area, and conclusions are given in Section 4.\n\n\n## 2. Experiments\n\n### 2.1. Model Description\n\nThis study used the recently released version of the Weather Research and Forecasting model with the ARW dynamical core (WRF-ARW model; version 3.9.1.1) with multinested grids and two-way interactive options. The WRF model has been integrated many advances in model physics/numerical aspects and data assimilation by scientists and developers from the expansive research community, therefore becoming a very flexible and useful tool for both researchers and operational forecasters (https://www.mmm.ucar.edu/weather-research-and-forecasting-model).For the purpose of investigating the impact of physical parameterization schemes, similarly to the study of Kieu et. al. [20, 21], a set of combination of physical parameterizations has been generated based on (a) the modified KF and BMJ cumulus parameterization schemes; (b) the Goddard and Dudhia schemes for the shortwave radiation; (d) the YSU and MYJ planetary boundary schemes; and (e) the Lin, WSM3, WSM5, and WSM6 schemes for the cloud microphysics. There are a maximum of 32 different configuration forecasts for each heavy rainfall case listed in Table 2. The other options are the Monin–Obukhov surface layer scheme and the Rapid Radiative Transfer Model scheme for longwave radiation. Note that, with the MYJ scheme, the surface layer option will be switched to Janjic’s Eta–Monin–Obukhov scheme which is based on similar theory with viscous sublayers over both solid surfaces and water points. Skamarock et al. [22] provided the detailed description of the WRF-ARW model, and various references for physical parameterizations of the WRF-ARW model can be found from various listed references [11, 23–29, 30].Table 2\nDetails of physical parameterization configurations in different experiments.\nAbbreviation\nMicrophysics\nShortwave radiation\nBoundary layer\nBetts–Miller–Janjic (BMJ) cumulus parameterization\nBMJ-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nBMJ-Lin-Duh-YSU\nLin\nDuhia\nYSU\nBMJ-Lin-God-MYJ\nLin\nGoddard\nMYJ\nBMJ-Lin-God-YSU\nLin\nGoddard\nYSU\nBMJ-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nBMJ-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nBMJ-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nBMJ-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nBMJ-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nBMJ-WSM5-God-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nBMJ-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nBMJ-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nBMJ-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nBMJ-WSM6-God-YSU\nWSM6\nGoddard\nYSU\nKain–Fritsch (KF) cumulus parameterization\nKF-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nKF-Lin-Duh-YSU\nLin\nDuhia\nYSU\nKF-Lin-God-MYJ\nLin\nGoddard\nMYJ\nKF-Lin-God-YSU\nLin\nGoddard\nYSU\nKF-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nKF-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nKF-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nKF-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nKF-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nKF-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nKF-WSM5-God-MYJ\nWSM5\nGoddard\nMYJ\nKF-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nKF-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nKF-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nKF-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nKF-WSM6-God-YSU\nWSM6\nGoddard\nYSUThe WRF-ARW model is configured with two nested grid domains consisting of 199 × 199 grid points in the (x, y) dimensions with horizontal resolutions of 15 km (denoted as d01 domain) and 5 km (denoted as d02 domain). All domains share 41 similar vertical σ levels with the model top at 50 hPa. The higher resolution domain covers the northern part of Vietnam with a time step of 15 seconds. All validations will be carried out with forecasts from the d02 domain. Figure 2 shows the terrain used in d01 and d02 domains.Figure 2\nSYNOP station distribution over Vietnam and closed countries (black dots) and terrain of computing domains for the WRF-ARW model. The blue contour is for the outer domain (d01, 15 km), and dark green shading is for the inner domain (d02, 5 km), illustrated by the Diana meteorological visualization software of the Norwegian Meteorological Institute.\n\n\n### 2.2. Boundary Conditions\n\nThe GFS model of NCEP used to provide boundary conditions for the WRF-ARW model in this study has a 0.5-degree horizontal resolution and be prepared every three hours from 1000 hPa to 1 hPa. The GFS data for this study were downloaded from the Research Data Archive at the National Center for Atmospheric Research via website linkhttps://rda.ucar.edu/datasets/ds335.0. More information on GFS data can be found at https://www.nco.ncep.noaa.gov/pmb/products/gfs/.\n\n\n### 2.3. Observation Data\n\nThe number of observation stations in Vietnam increased from 89 in 1988 to 186 in 2017, with 4 or 8 observations per day (black dots are 8 observations/day for stations in Vietnam and nearby countries in Figure2), but only 24 stations are reported to WMO. The difference in location and topography results in the significant change from one climate to another; therefore, Vietnam has been divided into several climate zones. The highest station density is in the Red River Delta area (the southern part of northern Vietnam) with approximately 1 station per 25 km × 25 km area. The coarsest station density is in the Central Highlands area (latitude between ∼11 N and 16 N) with approximately 1 station per 55 km × 55 km area. On average, the current surface observation network density of Vietnam is about 1 station per 35 km × 35 km for flat regions and 1 station per 50 km × 50 km for mountainous complex regions. In this paper, in order to verify model forecast for the Bac Bo area, we used observation data from the northern SYNOP stations for the period from 2012 to 2016 listed in Table 1.In this study, 72 cases of typical widespread heavy rains which occurred in the northern part of Vietnam in the period 2012–2016 were selected. For each case, the forecasting cycles are chosen so that the 72-hour forecast range can cover the maximum duration of heavy rain episodes.With respect to causes of heavy rainfall events, there are four main categories: (i) activities of ITCZs or troughs: type I, (ii) affected by tropical cyclones or surface low-pressure system (staying at least more than 2 days over the Bac Bo area): type II, (iii) related to the cold surge from the north: type III, and (iv) the complex combinations from different patterns: type IV. The list of forecasted events is given in Table3 with the station name, the maximum value of daily rainfall, and the type of each heavy rainfall case. The sample number of type I, type II, type III, and type IV has 37, 21, 5, and 9 forecast cycles, respectively.Table 3\nList of forecast cycles related to heavy rain cases from 2012 to 2016, the station with maximum daily accumulated rainfall up to 72 h for each cycle, and types of main synoptic situations.\nForecast cycle (year month day hour)\nMaximum 24 h accumulation observation (mm)\nStation with maximum observation\nTypes of rain events\n2012 05 18 00\n37\nBac Yen\nType I\n2012 05 19 00\n44\nLac Son\nType I\n2012 05 20 00\n114\nBac Quang\nType I\n2012 05 21 00\n195\nBac Quang\nType I\n2012 05 22 00\n131\nHa Dong\nType I\n2012 05 23 00\n186\nQuang Ha\nType I\n2012 07 20 00\n41\nBac Quang\nType I\n2012 07 21 00\n24.6\nLai Chau\nType II\n2012 07 22 00\n116\nVinh Yen\nType II\n2012 07 23 00\n75\nVinh\nType II\n2012 07 25 00\n229\nTuyen Quang\nType II\n2012 07 26 00\n104\nHam Yen\nType II\n2012 07 27 00\n112\nBac Quang\nType I\n2012 08 03 00\n58.1\nCon Cuong\nType I\n2012 08 04 00\n34.1\nTinh Gia\nType I\n2012 08 05 00\n70\nLang Son\nType I\n2012 08 06 00\n153\nMuong La\nType I\n2012 08 07 00\n163\nQuang Ha\nType I\n2012 08 13 00\n63.6\nMuong La\nType II\n2012 08 14 00\n64.5\nNho Quan\nType II\n2012 08 15 00\n74\nDo Luong\nType II\n2012 08 16 00\n76\nTam Dao\nType II\n2012 08 31 00\n49.1\nSin Ho\nType IV\n2012 09 01 00\n67\nNhu Xuan\nType IV\n2012 09 02 00\n124\nQuynh Luu\nType IV\n2012 09 03 00\n84\nMoc Chau\nType IV\n2012 09 04 00\n157\nHuong Khe\nType IV\n2012 09 16 00\n151.4\nMuong Te\nType IV\n2012 09 17 00\n104.6\nTam Duong\nType III\n2013 06 19 00\n109\nBac Quang\nType II\n2013 06 20 00\n72.2\nHuong Khe\nType II\n2013 06 21 00\n59\nNam Dinh\nType II\n2013 06 22 00\n218\nHa Tinh\nType II\n2014 08 25 00\n91\nLao Cai\nType II\n2014 08 26 00\n89\nVan Ly\nType II\n2014 08 27 00\n157\nHon Ngu\nType II\n2015 05 18 00\n90\nHoi Xuan\nType III\n2015 05 19 00\n58\nSin Ho\nType III\n2015 05 20 00\n106\nThat Khe\nType III\n2015 05 21 00\n72\nTuyen Quang\nType III\n2015 06 21 00\n124\nLuc Yen\nType II\n2015 06 22 00\n72.7\nDo Luong\nType II\n2015 07 01 00\n84.5\nCao Bang\nType I\n2015 07 02 00\n123.0\nVan Chan\nType I\n2015 07 03 00\n74.0\nHuong Khe\nType I\n2015 07 21 00\n54.5\nHoa Binh\nType I\n2015 07 22 00\n19\nSin Ho\nType I\n2015 07 23 00\n92\nSin Ho\nType I\n2015 07 24 00\n180\nQuynh Nhai\nType I\n2015 07 25 00\n181\nCua Ong\nType I\n2015 07 26 00\n432\nCua Ong\nType I\n2015 07 27 00\n347\nQuang Ha\nType I\n2015 07 28 00\n224\nMong Cai\nType I\n2015 07 29 00\n247\nCua Ong\nType I\n2015 07 30 00\n145\nQuang Ha\nType I\n2015 07 31 00\n239\nQuang Ha\nType I\n2015 08 01 00\n157\nPhu Lien\nType I\n2015 09 18 00\n73\nHai Duong\nType IV\n2015 09 19 00\n78.0\nDinh Hoa\nType IV\n2016 05 20 00\n118\nTinh Gia\nType I\n2016 05 21 00\n107.0\nHa Nam\nType I\n2016 05 22 00\n92\nSa Pa\nType I\n2016 05 23 00\n190.4\nYen Bai\nType I\n2016 07 24 00\n60\nPhu Lien\nType II\n2016 07 25 00\n17\nThai Binh\nType II\n2016 07 26 00\n46\nDinh Hoa\nType II\n2016 07 30 00\n18.1\nMu Cang Chai\nType II\n2016 08 01 00\n19\nSin Ho\nType I\n2016 08 02 00\n150\nNinh Binh\nType I\n2016 08 10 00\n81\nQuynh Nhai\nType I\n2016 08 11 00\n117\nThanh Hoa\nType I\n2016 08 12 00\n87\nTay Hieu\nType I\nType I: activities of trough or ITCZ; type II: affected by tropical cyclone or low-pressure system; type III: related to cold surge from the north; type IV: combinations of different patterns.\n\n\n### 2.4. Validation Methods\n\nBy finding the nearest grids to each station position (listed in Table1), the daily accumulated rainfall for these heavy rainfall cases from WRF-ARW model forecasts can be assigned. The verification scores used in this study are frequency bias (BIAS), probability of detection (POD), false alarm ratio (FAR), threat score (TS), and equitable threat score (ETS). If we denote H for the hit rate of occurred rainfalls (at a given threshold) for both forecast and observation, M for the missed rate of occurred rainfall forecast, and F for the false alarm rate of the forecast, the BIAS, POD, FAR, and TS are calculated by the following equations:(1)BIAS=H+FH+M,perfect value=1,<1=underestimation,>1=overestimation,POD=HH+M,perfect value=1,FAR=FH+F,perfect value=0,TS=HH+M+F,perfect value=1,no skill=0.If we set Hitsrandom = (H + F) (H + M)/T, where T is the sum of H, M, F, and the number of nonoccurred rainfalls for both forecast and observation, the ETS is calculated by(2)ETS=H−HitsrandomH+M+F−Hitsrandom,perfect value=1,no skill<=0.Other meanings of these scores can be found in Wilks’ study [31]. The verification will be carried out for the 5 km domain and for 24 h accumulated rainfall at 24 h, 48 h, and 72 h forecast ranges. Other analysis charts include the histogram of precipitation occurrences at given thresholds (>25 mm/24 h, >50 mm/24 h, and >100 mm/24 h) at the observation stations.\n\n\n## 2.1. Model Description\n\nThis study used the recently released version of the Weather Research and Forecasting model with the ARW dynamical core (WRF-ARW model; version 3.9.1.1) with multinested grids and two-way interactive options. The WRF model has been integrated many advances in model physics/numerical aspects and data assimilation by scientists and developers from the expansive research community, therefore becoming a very flexible and useful tool for both researchers and operational forecasters (https://www.mmm.ucar.edu/weather-research-and-forecasting-model).For the purpose of investigating the impact of physical parameterization schemes, similarly to the study of Kieu et. al. [20, 21], a set of combination of physical parameterizations has been generated based on (a) the modified KF and BMJ cumulus parameterization schemes; (b) the Goddard and Dudhia schemes for the shortwave radiation; (d) the YSU and MYJ planetary boundary schemes; and (e) the Lin, WSM3, WSM5, and WSM6 schemes for the cloud microphysics. There are a maximum of 32 different configuration forecasts for each heavy rainfall case listed in Table 2. The other options are the Monin–Obukhov surface layer scheme and the Rapid Radiative Transfer Model scheme for longwave radiation. Note that, with the MYJ scheme, the surface layer option will be switched to Janjic’s Eta–Monin–Obukhov scheme which is based on similar theory with viscous sublayers over both solid surfaces and water points. Skamarock et al. [22] provided the detailed description of the WRF-ARW model, and various references for physical parameterizations of the WRF-ARW model can be found from various listed references [11, 23–29, 30].Table 2\nDetails of physical parameterization configurations in different experiments.\nAbbreviation\nMicrophysics\nShortwave radiation\nBoundary layer\nBetts–Miller–Janjic (BMJ) cumulus parameterization\nBMJ-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nBMJ-Lin-Duh-YSU\nLin\nDuhia\nYSU\nBMJ-Lin-God-MYJ\nLin\nGoddard\nMYJ\nBMJ-Lin-God-YSU\nLin\nGoddard\nYSU\nBMJ-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nBMJ-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nBMJ-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nBMJ-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nBMJ-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nBMJ-WSM5-God-MYJ\nWSM5\nDuhia\nMYJ\nBMJ-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nBMJ-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nBMJ-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nBMJ-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nBMJ-WSM6-God-YSU\nWSM6\nGoddard\nYSU\nKain–Fritsch (KF) cumulus parameterization\nKF-Lin-Duh-MYJ\nLin\nDuhia\nMYJ\nKF-Lin-Duh-YSU\nLin\nDuhia\nYSU\nKF-Lin-God-MYJ\nLin\nGoddard\nMYJ\nKF-Lin-God-YSU\nLin\nGoddard\nYSU\nKF-WSM3-Duh-MYJ\nWSM3\nDuhia\nMYJ\nKF-WSM3-Duh-YSU\nWSM3\nDuhia\nYSU\nKF-WSM3-God-MYJ\nWSM3\nGoddard\nMYJ\nKF-WSM3-God-YSU\nWSM3\nGoddard\nYSU\nKF-WSM5-Duh-MYJ\nWSM5\nDuhia\nMYJ\nKF-WSM5-Duh-YSU\nWSM5\nDuhia\nYSU\nKF-WSM5-God-MYJ\nWSM5\nGoddard\nMYJ\nKF-WSM5-God-YSU\nWSM5\nGoddard\nYSU\nKF-WSM6-Duh-MYJ\nWSM6\nDuhia\nMYJ\nKF-WSM6-Duh-YSU\nWSM6\nDuhia\nYSU\nKF-WSM6-God-MYJ\nWSM6\nGoddard\nMYJ\nKF-WSM6-God-YSU\nWSM6\nGoddard\nYSUThe WRF-ARW model is configured with two nested grid domains consisting of 199 × 199 grid points in the (x, y) dimensions with horizontal resolutions of 15 km (denoted as d01 domain) and 5 km (denoted as d02 domain). All domains share 41 similar vertical σ levels with the model top at 50 hPa. The higher resolution domain covers the northern part of Vietnam with a time step of 15 seconds. All validations will be carried out with forecasts from the d02 domain. Figure 2 shows the terrain used in d01 and d02 domains.Figure 2\nSYNOP station distribution over Vietnam and closed countries (black dots) and terrain of computing domains for the WRF-ARW model. The blue contour is for the outer domain (d01, 15 km), and dark green shading is for the inner domain (d02, 5 km), illustrated by the Diana meteorological visualization software of the Norwegian Meteorological Institute.\n\n\n## 2.2. Boundary Conditions\n\nThe GFS model of NCEP used to provide boundary conditions for the WRF-ARW model in this study has a 0.5-degree horizontal resolution and be prepared every three hours from 1000 hPa to 1 hPa. The GFS data for this study were downloaded from the Research Data Archive at the National Center for Atmospheric Research via website linkhttps://rda.ucar.edu/datasets/ds335.0. More information on GFS data can be found at https://www.nco.ncep.noaa.gov/pmb/products/gfs/.\n\n\n## 2.3. Observation Data\n\nThe number of observation stations in Vietnam increased from 89 in 1988 to 186 in 2017, with 4 or 8 observations per day (black dots are 8 observations/day for stations in Vietnam and nearby countries in Figure2), but only 24 stations are reported to WMO. The difference in location and topography results in the significant change from one climate to another; therefore, Vietnam has been divided into several climate zones. The highest station density is in the Red River Delta area (the southern part of northern Vietnam) with approximately 1 station per 25 km × 25 km area. The coarsest station density is in the Central Highlands area (latitude between ∼11 N and 16 N) with approximately 1 station per 55 km × 55 km area. On average, the current surface observation network density of Vietnam is about 1 station per 35 km × 35 km for flat regions and 1 station per 50 km × 50 km for mountainous complex regions. In this paper, in order to verify model forecast for the Bac Bo area, we used observation data from the northern SYNOP stations for the period from 2012 to 2016 listed in Table 1.In this study, 72 cases of typical widespread heavy rains which occurred in the northern part of Vietnam in the period 2012–2016 were selected. For each case, the forecasting cycles are chosen so that the 72-hour forecast range can cover the maximum duration of heavy rain episodes.With respect to causes of heavy rainfall events, there are four main categories: (i) activities of ITCZs or troughs: type I, (ii) affected by tropical cyclones or surface low-pressure system (staying at least more than 2 days over the Bac Bo area): type II, (iii) related to the cold surge from the north: type III, and (iv) the complex combinations from different patterns: type IV. The list of forecasted events is given in Table3 with the station name, the maximum value of daily rainfall, and the type of each heavy rainfall case. The sample number of type I, type II, type III, and type IV has 37, 21, 5, and 9 forecast cycles, respectively.Table 3\nList of forecast cycles related to heavy rain cases from 2012 to 2016, the station with maximum daily accumulated rainfall up to 72 h for each cycle, and types of main synoptic situations.\nForecast cycle (year month day hour)\nMaximum 24 h accumulation observation (mm)\nStation with maximum observation\nTypes of rain events\n2012 05 18 00\n37\nBac Yen\nType I\n2012 05 19 00\n44\nLac Son\nType I\n2012 05 20 00\n114\nBac Quang\nType I\n2012 05 21 00\n195\nBac Quang\nType I\n2012 05 22 00\n131\nHa Dong\nType I\n2012 05 23 00\n186\nQuang Ha\nType I\n2012 07 20 00\n41\nBac Quang\nType I\n2012 07 21 00\n24.6\nLai Chau\nType II\n2012 07 22 00\n116\nVinh Yen\nType II\n2012 07 23 00\n75\nVinh\nType II\n2012 07 25 00\n229\nTuyen Quang\nType II\n2012 07 26 00\n104\nHam Yen\nType II\n2012 07 27 00\n112\nBac Quang\nType I\n2012 08 03 00\n58.1\nCon Cuong\nType I\n2012 08 04 00\n34.1\nTinh Gia\nType I\n2012 08 05 00\n70\nLang Son\nType I\n2012 08 06 00\n153\nMuong La\nType I\n2012 08 07 00\n163\nQuang Ha\nType I\n2012 08 13 00\n63.6\nMuong La\nType II\n2012 08 14 00\n64.5\nNho Quan\nType II\n2012 08 15 00\n74\nDo Luong\nType II\n2012 08 16 00\n76\nTam Dao\nType II\n2012 08 31 00\n49.1\nSin Ho\nType IV\n2012 09 01 00\n67\nNhu Xuan\nType IV\n2012 09 02 00\n124\nQuynh Luu\nType IV\n2012 09 03 00\n84\nMoc Chau\nType IV\n2012 09 04 00\n157\nHuong Khe\nType IV\n2012 09 16 00\n151.4\nMuong Te\nType IV\n2012 09 17 00\n104.6\nTam Duong\nType III\n2013 06 19 00\n109\nBac Quang\nType II\n2013 06 20 00\n72.2\nHuong Khe\nType II\n2013 06 21 00\n59\nNam Dinh\nType II\n2013 06 22 00\n218\nHa Tinh\nType II\n2014 08 25 00\n91\nLao Cai\nType II\n2014 08 26 00\n89\nVan Ly\nType II\n2014 08 27 00\n157\nHon Ngu\nType II\n2015 05 18 00\n90\nHoi Xuan\nType III\n2015 05 19 00\n58\nSin Ho\nType III\n2015 05 20 00\n106\nThat Khe\nType III\n2015 05 21 00\n72\nTuyen Quang\nType III\n2015 06 21 00\n124\nLuc Yen\nType II\n2015 06 22 00\n72.7\nDo Luong\nType II\n2015 07 01 00\n84.5\nCao Bang\nType I\n2015 07 02 00\n123.0\nVan Chan\nType I\n2015 07 03 00\n74.0\nHuong Khe\nType I\n2015 07 21 00\n54.5\nHoa Binh\nType I\n2015 07 22 00\n19\nSin Ho\nType I\n2015 07 23 00\n92\nSin Ho\nType I\n2015 07 24 00\n180\nQuynh Nhai\nType I\n2015 07 25 00\n181\nCua Ong\nType I\n2015 07 26 00\n432\nCua Ong\nType I\n2015 07 27 00\n347\nQuang Ha\nType I\n2015 07 28 00\n224\nMong Cai\nType I\n2015 07 29 00\n247\nCua Ong\nType I\n2015 07 30 00\n145\nQuang Ha\nType I\n2015 07 31 00\n239\nQuang Ha\nType I\n2015 08 01 00\n157\nPhu Lien\nType I\n2015 09 18 00\n73\nHai Duong\nType IV\n2015 09 19 00\n78.0\nDinh Hoa\nType IV\n2016 05 20 00\n118\nTinh Gia\nType I\n2016 05 21 00\n107.0\nHa Nam\nType I\n2016 05 22 00\n92\nSa Pa\nType I\n2016 05 23 00\n190.4\nYen Bai\nType I\n2016 07 24 00\n60\nPhu Lien\nType II\n2016 07 25 00\n17\nThai Binh\nType II\n2016 07 26 00\n46\nDinh Hoa\nType II\n2016 07 30 00\n18.1\nMu Cang Chai\nType II\n2016 08 01 00\n19\nSin Ho\nType I\n2016 08 02 00\n150\nNinh Binh\nType I\n2016 08 10 00\n81\nQuynh Nhai\nType I\n2016 08 11 00\n117\nThanh Hoa\nType I\n2016 08 12 00\n87\nTay Hieu\nType I\nType I: activities of trough or ITCZ; type II: affected by tropical cyclone or low-pressure system; type III: related to cold surge from the north; type IV: combinations of different patterns.\n\n\n## 2.4. Validation Methods\n\nBy finding the nearest grids to each station position (listed in Table1), the daily accumulated rainfall for these heavy rainfall cases from WRF-ARW model forecasts can be assigned. The verification scores used in this study are frequency bias (BIAS), probability of detection (POD), false alarm ratio (FAR), threat score (TS), and equitable threat score (ETS). If we denote H for the hit rate of occurred rainfalls (at a given threshold) for both forecast and observation, M for the missed rate of occurred rainfall forecast, and F for the false alarm rate of the forecast, the BIAS, POD, FAR, and TS are calculated by the following equations:(1)BIAS=H+FH+M,perfect value=1,<1=underestimation,>1=overestimation,POD=HH+M,perfect value=1,FAR=FH+F,perfect value=0,TS=HH+M+F,perfect value=1,no skill=0.If we set Hitsrandom = (H + F) (H + M)/T, where T is the sum of H, M, F, and the number of nonoccurred rainfalls for both forecast and observation, the ETS is calculated by(2)ETS=H−HitsrandomH+M+F−Hitsrandom,perfect value=1,no skill<=0.Other meanings of these scores can be found in Wilks’ study [31]. The verification will be carried out for the 5 km domain and for 24 h accumulated rainfall at 24 h, 48 h, and 72 h forecast ranges. Other analysis charts include the histogram of precipitation occurrences at given thresholds (>25 mm/24 h, >50 mm/24 h, and >100 mm/24 h) at the observation stations.\n\n\n## 3. Results\n\n### 3.1. General Performance\n\nThe histogram charts (Figure3) show the number of observations or forecasts that occurred at stations for given ranges—or bins. We divided rainfall into 4 main bins (0–25 mm, 25–50 mm, 50–100 mm, and >100 mm) for different rainfall classes. For all 24 h, 48 h, and 72 h forecasts, it is quite clear that most forecasts from the BMJ scheme are in the 0–25 mm range, higher than the number of observations, while the KF scheme tends to have less forecasts than observations. In contrast, at the thresholds greater than 25 mm, for the BMJ scheme, the number of forecasts is less than the number of observations, while the KF scheme tends to have more forecasts than the number of observations.Figure 3\nHistogram of daily rainfall frequency at different thresholds (bins) for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dotted lines are the observation frequency. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure4 shows the BIAS score at different thresholds (>25 mm/24 h and >50 mm/24 h) and separated for KF and BMJ scheme combinations. Overall assessment through the BIAS score is quite similar to the results from the evaluation through histograms: simulations with the BMJ scheme tend to be lower than observations at most forecast ranges and different thresholds (BIAS < 1), while the simulations with the KF scheme tend to be higher than the observations (BIAS > 1). The BIAS tends to decrease significantly when the forecast ranges increase. When the validation thresholds are increased, the simulation with the BMJ scheme tends to decrease BIAS, whereas in combination with the KF scheme, BIAS increases with both the forecast ranges and the evaluation thresholds.Figure 4\nThe BIAS score at 24 h, 48 h, and 72 h for different thresholds (over 25 mm, 50 mm, and 100 mm) for BMJ scheme combinations (a) and for KF scheme combinations (b). The number of samples is 8064 for an individual forecast.\n(a)\n(b)The individual assessment in each combination of BMJ and KF schemes shows that when combined with the Goddard radiation scheme, the BIAS increases from 0.1 to 0.2 compared to the Dudhia scheme. These results are similar for all validating thresholds and for 24 h, 48 h, and 72 h forecast ranges. The difference in simulations with different boundary layer schemes is unclear while evaluating with thresholds below 25 mm/24 h; however, at higher thresholds, it is apparent: the change of BIAS when combining with the KF scheme is much higher. For example, compared to the BMJ scheme, the BIAS score of KF-WSM3-God-MYJ at the >100 mm/24 h threshold at the 24-hour forecast range is 1.6458 and that of KF-WSM3-God-YSU is 2.0833, while BMJ-WSM3-God-MYJ and BMJ-WSM3-God-YSU had approximately equal BIAS scores (0.8229). Thus, the combinations of the boundary layer schemes are different (here only the two schemes YSU and MYJ) with the KF scheme being much more sensitive to its combinations with the BMJ scheme and especially at the high rainfall thresholds. Details of numerical values of BIAS can be found in Tables4, 5, and 6.Table 4\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.207\n0.1517\n0.5854\n0.2719\n0.5355\n301\n347\n806\n6106\n0.1049\n0.0874\n0.4139\n0.1342\n0.6757\n60\n125\n387\n6988\nBMJ-Lin-Duh-YSU\n0.2087\n0.1551\n0.5592\n0.2692\n0.5186\n298\n321\n809\n6132\n0.1072\n0.0899\n0.4094\n0.1365\n0.6667\n61\n122\n386\n6991\nBMJ-Lin-God-MYJ\n0.2283\n0.1613\n0.7986\n0.3342\n0.5814\n370\n514\n737\n5939\n0.1225\n0.0996\n0.6197\n0.1767\n0.7148\n79\n198\n368\n6915\nBMJ-Lin-God-YSU\n0.2304\n0.1649\n0.7706\n0.3315\n0.5698\n367\n486\n740\n5967\n0.122\n0.1\n0.5839\n0.1723\n0.705\n77\n184\n370\n6929\nBMJ-WSM3-Duh-MYJ\n0.2051\n0.1433\n0.6929\n0.2882\n0.5841\n319\n448\n788\n6005\n0.1066\n0.0846\n0.5794\n0.1521\n0.7375\n68\n191\n379\n6922\nBMJ-WSM3-Duh-YSU\n0.2201\n0.1595\n0.6775\n0.3026\n0.5533\n335\n415\n772\n6038\n0.1153\n0.0928\n0.6018\n0.1655\n0.7249\n74\n195\n373\n6918\nBMJ-WSM3-God-MYJ\n0.2388\n0.1649\n0.9539\n0.3767\n0.6051\n417\n639\n690\n5814\n0.1364\n0.1089\n0.8456\n0.2215\n0.7381\n99\n279\n348\n6834\nBMJ-WSM3-God-YSU\n0.2408\n0.1709\n0.8663\n0.3622\n0.5819\n401\n558\n706\n5895\n0.1391\n0.1116\n0.8501\n0.226\n0.7342\n101\n279\n346\n6834\nBMJ-WSM5-Duh-MYJ\n0.2147\n0.153\n0.692\n0.299\n0.5679\n331\n435\n776\n6018\n0.1166\n0.0951\n0.5638\n0.1633\n0.7103\n73\n179\n374\n6934\nBMJ-WSM5-Duh-YSU\n0.2156\n0.1553\n0.6703\n0.2963\n0.558\n328\n414\n779\n6039\n0.1319\n0.1092\n0.613\n0.1879\n0.6934\n84\n190\n363\n6923\nBMJ-WSM5-God-MYJ\n0.2408\n0.1743\n0.7967\n0.3487\n0.5624\n386\n496\n721\n5957\n0.1273\n0.1044\n0.6242\n0.1834\n0.7061\n82\n197\n365\n6916\nBMJ-WSM5-God-YSU\n0.2449\n0.1766\n0.8365\n0.3613\n0.568\n400\n526\n707\n5927\n0.1386\n0.112\n0.8009\n0.2192\n0.7263\n98\n260\n349\n6853\nBMJ-WSM6-Duh-MYJ\n0.2079\n0.1419\n0.7687\n0.3044\n0.604\n337\n514\n770\n5939\n0.1166\n0.0925\n0.6711\n0.1745\n0.74\n78\n222\n369\n6891\nBMJ-WSM6-Duh-YSU\n0.2126\n0.1469\n0.7669\n0.3098\n0.596\n343\n506\n764\n5947\n0.1386\n0.1135\n0.7271\n0.2103\n0.7108\n94\n231\n353\n6882\nBMJ-WSM6-God-MYJ\n0.2552\n0.1793\n1.0126\n0.4092\n0.5959\n453\n668\n654\n5785\n0.1554\n0.1269\n0.9128\n0.2573\n0.7181\n115\n293\n332\n6820\nBMJ-WSM6-God-YSU\n0.2477\n0.1747\n0.9386\n0.3848\n0.59\n426\n613\n681\n5840\n0.1644\n0.1355\n0.9485\n0.2752\n0.7099\n123\n301\n324\n6812\nKF-Lin-Duh-MYJ\n0.2399\n0.162\n1.0497\n0.3966\n0.6222\n439\n723\n668\n5730\n0.1525\n0.1262\n0.7919\n0.2371\n0.7006\n106\n248\n341\n6865\nKF-Lin-Duh-YSU\n0.2656\n0.1884\n1.0533\n0.4309\n0.5909\n477\n689\n630\n5764\n0.1457\n0.1168\n0.9351\n0.2461\n0.7368\n110\n308\n337\n6805\nKF-Lin-God-MYJ\n0.2507\n0.165\n1.2755\n0.4562\n0.6424\n505\n907\n602\n5546\n0.1564\n0.1233\n1.2327\n0.302\n0.755\n135\n416\n312\n6697\nKF-Lin-God-YSU\n0.2649\n0.1795\n1.2818\n0.4779\n0.6272\n529\n890\n578\n5563\n0.162\n0.128\n1.311\n0.3221\n0.7543\n144\n442\n303\n6671\nKF-WSM3-Duh-MYJ\n0.2442\n0.1627\n1.1491\n0.4219\n0.6329\n467\n805\n640\n5648\n0.1581\n0.1266\n1.1141\n0.2886\n0.741\n129\n369\n318\n6744\nKF-WSM3-Duh-YSU\n0.2664\n0.1843\n1.1861\n0.4598\n0.6123\n509\n804\n598\n5649\n0.1667\n0.135\n1.1298\n0.3043\n0.7307\n136\n369\n311\n6744\nKF-WSM3-God-MYJ\n0.2743\n0.1867\n1.3668\n0.5095\n0.6272\n564\n949\n543\n5504\n0.172\n0.1368\n1.4385\n0.3579\n0.7512\n160\n483\n287\n6630\nKF-WSM3-God-YSU\n0.2739\n0.1852\n1.4029\n0.5167\n0.6317\n572\n981\n535\n5472\n0.1778\n0.1413\n1.5638\n0.387\n0.7525\n173\n526\n274\n6587\nKF-WSM5-Duh-MYJ\n0.2583\n0.1766\n1.1653\n0.4444\n0.6186\n492\n798\n615\n5655\n0.1648\n0.1333\n1.1186\n0.2998\n0.732\n134\n366\n313\n6747\nKF-WSM5-Duh-YSU\n0.2656\n0.1828\n1.2042\n0.4625\n0.6159\n512\n821\n595\n5632\n0.1588\n0.1269\n1.1387\n0.2931\n0.7426\n131\n378\n316\n6735\nKF-WSM5-God-MYJ\n0.2693\n0.1812\n1.3758\n0.5041\n0.6336\n558\n965\n549\n5488\n0.1773\n0.1421\n1.4362\n0.3669\n0.7445\n164\n478\n283\n6635\nKF-WSM5-God-YSU\n0.2826\n0.1939\n1.4146\n0.5321\n0.6239\n589\n977\n518\n5476\n0.178\n0.1415\n1.5615\n0.387\n0.7521\n173\n525\n274\n6588\nKF-WSM6-Duh-MYJ\n0.2598\n0.1758\n1.234\n0.4607\n0.6266\n510\n856\n597\n5597\n0.1663\n0.1326\n1.2908\n0.3266\n0.747\n146\n431\n301\n6682\nKF-WSM6-Duh-YSU\n0.28\n0.1952\n1.2836\n0.4995\n0.6108\n553\n868\n554\n5585\n0.1958\n0.1611\n1.4049\n0.3937\n0.7197\n176\n452\n271\n6661\nKF-WSM6-God-MYJ\n0.26\n0.1698\n1.43\n0.5014\n0.6494\n555\n1028\n552\n5425\n0.1926\n0.156\n1.5906\n0.4183\n0.737\n187\n524\n260\n6589\nKF-WSM6-God-YSU\n0.2792\n0.1888\n1.467\n0.5384\n0.633\n596\n1028\n511\n5425\n0.1844\n0.1463\n1.7584\n0.4295\n0.7557\n192\n594\n255\n6519Table 5\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.1791\n0.1054\n0.4842\n0.2254\n0.5344\n365\n419\n1254\n5522\n0.0962\n0.0703\n0.3463\n0.1181\n0.6589\n88\n170\n657\n6645\nBMJ-Lin-Duh-YSU\n0.1853\n0.1069\n0.5287\n0.239\n0.5479\n387\n469\n1232\n5472\n0.1105\n0.0803\n0.4295\n0.1423\n0.6687\n106\n214\n639\n6601\nBMJ-Lin-God-MYJ\n0.234\n0.1345\n0.769\n0.3354\n0.5639\n543\n702\n1076\n5239\n0.1225\n0.0846\n0.5987\n0.1745\n0.7085\n130\n316\n615\n6499\nBMJ-Lin-God-YSU\n0.243\n0.1408\n0.8073\n0.3533\n0.5624\n572\n735\n1047\n5206\n0.1357\n0.0971\n0.6174\n0.1933\n0.687\n144\n316\n601\n6499\nBMJ-WSM3-Duh-MYJ\n0.2332\n0.1353\n0.7505\n0.3311\n0.5588\n536\n679\n1083\n5262\n0.152\n0.109\n0.7396\n0.2295\n0.6897\n171\n380\n574\n6435\nBMJ-WSM3-Duh-YSU\n0.2579\n0.1573\n0.7956\n0.3681\n0.5373\n596\n692\n1023\n5249\n0.1587\n0.1142\n0.7839\n0.2443\n0.6884\n182\n402\n563\n6413\nBMJ-WSM3-God-MYJ\n0.2616\n0.1452\n1.0167\n0.4182\n0.5887\n677\n969\n942\n4972\n0.1546\n0.1034\n1.0054\n0.2685\n0.733\n200\n549\n545\n6266\nBMJ-WSM3-God-YSU\n0.2641\n0.148\n1.0136\n0.4206\n0.585\n681\n960\n938\n4981\n0.1539\n0.1034\n0.9826\n0.2644\n0.7309\n197\n535\n548\n6280\nBMJ-WSM5-Duh-MYJ\n0.2425\n0.1455\n0.7437\n0.3403\n0.5424\n551\n653\n1068\n5288\n0.1582\n0.1171\n0.6899\n0.2309\n0.6654\n172\n342\n573\n6473\nBMJ-WSM5-Duh-YSU\n0.2545\n0.154\n0.7931\n0.3638\n0.5413\n589\n695\n1030\n5246\n0.1698\n0.1257\n0.7758\n0.2577\n0.6678\n192\n386\n553\n6429\nBMJ-WSM5-God-MYJ\n0.2687\n0.1601\n0.9074\n0.404\n0.5548\n654\n815\n965\n5126\n0.174\n0.1286\n0.8201\n0.2698\n0.671\n201\n410\n544\n6405\nBMJ-WSM5-God-YSU\n0.2677\n0.1522\n1.0068\n0.4237\n0.5791\n686\n944\n933\n4997\n0.1674\n0.1166\n1.0027\n0.2872\n0.7135\n214\n533\n531\n6282\nBMJ-WSM6-Duh-MYJ\n0.2282\n0.1261\n0.7986\n0.3342\n0.5816\n541\n752\n1078\n5189\n0.1414\n0.097\n0.7772\n0.2201\n0.7168\n164\n415\n581\n6400\nBMJ-WSM6-Duh-YSU\n0.2471\n0.1426\n0.8394\n0.3644\n0.5659\n590\n769\n1029\n5172\n0.169\n0.1198\n0.9409\n0.2805\n0.7019\n209\n492\n536\n6323\nBMJ-WSM6-God-MYJ\n0.2484\n0.1255\n1.1081\n0.4194\n0.6215\n679\n1115\n940\n4826\n0.1471\n0.0931\n1.1141\n0.2711\n0.7566\n202\n628\n543\n6187\nBMJ-WSM6-God-YSU\n0.283\n0.1642\n1.0723\n0.4571\n0.5737\n740\n996\n879\n4945\n0.1588\n0.1053\n1.0966\n0.2872\n0.7381\n214\n603\n531\n6212\nKF-Lin-Duh-MYJ\n0.2144\n0.1147\n0.7634\n0.3113\n0.5922\n504\n732\n1115\n5209\n0.1299\n0.0905\n0.6349\n0.1879\n0.704\n140\n333\n605\n6482\nKF-Lin-Duh-YSU\n0.2312\n0.136\n0.7171\n0.3224\n0.5504\n522\n639\n1097\n5302\n0.124\n0.0847\n0.6309\n0.1799\n0.7149\n134\n336\n611\n6479\nKF-Lin-God-MYJ\n0.2584\n0.1355\n1.1174\n0.4348\n0.6108\n704\n1105\n915\n4836\n0.1304\n0.0781\n1.0362\n0.2349\n0.7733\n175\n597\n570\n6218\nKF-Lin-God-YSU\n0.2806\n0.1647\n1.0241\n0.4435\n0.5669\n718\n940\n901\n5001\n0.1611\n0.1097\n1.0215\n0.2805\n0.7254\n209\n552\n536\n6263\nKF-WSM3-Duh-MYJ\n0.266\n0.149\n1.0284\n0.4262\n0.5856\n690\n975\n929\n4966\n0.1735\n0.1212\n1.0604\n0.3047\n0.7127\n227\n563\n518\n6252\nKF-WSM3-Duh-YSU\n0.2839\n0.1719\n0.9691\n0.4355\n0.5507\n705\n864\n914\n5077\n0.1948\n0.1437\n1.0255\n0.3302\n0.678\n246\n518\n499\n6297\nKF-WSM3-God-MYJ\n0.2845\n0.1503\n1.3484\n0.5201\n0.6143\n842\n1341\n777\n4600\n0.1766\n0.1161\n1.4416\n0.3664\n0.7458\n273\n801\n472\n6014\nKF-WSM3-God-YSU\n0.3018\n0.1706\n1.3125\n0.5361\n0.5915\n868\n1257\n751\n4684\n0.1877\n0.1271\n1.455\n0.3879\n0.7334\n289\n795\n456\n6020\nKF-WSM5-Duh-MYJ\n0.2801\n0.1622\n1.055\n0.4497\n0.5738\n728\n980\n891\n4961\n0.1785\n0.1262\n1.0644\n0.3128\n0.7062\n233\n560\n512\n6255\nKF-WSM5-Duh-YSU\n0.2948\n0.1812\n1.0019\n0.4558\n0.545\n738\n884\n881\n5057\n0.2037\n0.1509\n1.102\n0.3557\n0.6772\n265\n556\n480\n6259\nKF-WSM5-God-MYJ\n0.284\n0.1477\n1.3904\n0.5287\n0.6197\n856\n1395\n763\n4546\n0.1685\n0.1064\n1.5221\n0.3638\n0.761\n271\n863\n474\n5952\nKF-WSM5-God-YSU\n0.2998\n0.1683\n1.3162\n0.5343\n0.5941\n865\n1266\n754\n4675\n0.1996\n0.1388\n1.4846\n0.4134\n0.7215\n308\n798\n437\n6017\nKF-WSM6-Duh-MYJ\n0.2799\n0.1571\n1.1353\n0.467\n0.5887\n756\n1082\n863\n4859\n0.1973\n0.1415\n1.2242\n0.3664\n0.7007\n273\n639\n472\n6176\nKF-WSM6-Duh-YSU\n0.3002\n0.1833\n1.0574\n0.475\n0.5508\n769\n943\n850\n4998\n0.211\n0.157\n1.157\n0.3758\n0.6752\n280\n582\n465\n6233\nKF-WSM6-God-MYJ\n0.2917\n0.155\n1.4095\n0.5442\n0.6139\n881\n1401\n738\n4540\n0.1788\n0.1154\n1.6107\n0.396\n0.7542\n295\n905\n450\n5910\nKF-WSM6-God-YSU\n0.3113\n0.1785\n1.357\n0.5596\n0.5876\n906\n1291\n713\n4650\n0.2101\n0.1475\n1.6054\n0.4523\n0.7182\n337\n859\n408\n5956Table 6\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 72 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.154\n0.0672\n0.3964\n0.1863\n0.53\n400\n451\n1747\n4962\n0.0797\n0.0443\n0.3412\n0.099\n0.7098\n101\n247\n919\n6293\nBMJ-Lin-Duh-YSU\n0.1539\n0.0595\n0.442\n0.1924\n0.5648\n413\n536\n1734\n4877\n0.0766\n0.0373\n0.3912\n0.099\n0.7469\n101\n298\n919\n6242\nBMJ-Lin-God-MYJ\n0.1755\n0.0611\n0.5752\n0.2352\n0.5911\n505\n730\n1642\n4683\n0.0719\n0.0285\n0.4471\n0.0971\n0.7829\n99\n357\n921\n6183\nBMJ-Lin-God-YSU\n0.1893\n0.0645\n0.653\n0.2632\n0.597\n565\n837\n1582\n4576\n0.0832\n0.0342\n0.5314\n0.1176\n0.7786\n120\n422\n900\n6118\nBMJ-WSM3-Duh-MYJ\n0.1802\n0.0656\n0.5771\n0.2408\n0.5827\n517\n722\n1630\n4691\n0.0867\n0.0391\n0.5108\n0.1206\n0.7639\n123\n398\n897\n6142\nBMJ-WSM3-Duh-YSU\n0.1988\n0.0737\n0.6572\n0.2748\n0.5819\n590\n821\n1557\n4592\n0.0935\n0.0407\n0.5941\n0.1363\n0.7706\n139\n467\n881\n6073\nBMJ-WSM3-God-MYJ\n0.2273\n0.0777\n0.871\n0.3465\n0.6021\n744\n1126\n1403\n4287\n0.0992\n0.0404\n0.7049\n0.1539\n0.7816\n157\n562\n863\n5978\nBMJ-WSM3-God-YSU\n0.2239\n0.0691\n0.9171\n0.3507\n0.6176\n753\n1216\n1394\n4197\n0.1022\n0.0373\n0.8294\n0.1696\n0.7955\n173\n673\n847\n5867\nBMJ-WSM5-Duh-MYJ\n0.1788\n0.0634\n0.5817\n0.2399\n0.5877\n515\n734\n1632\n4679\n0.0944\n0.0482\n0.4892\n0.1284\n0.7375\n131\n368\n889\n6172\nBMJ-WSM5-Duh-YSU\n0.2041\n0.0796\n0.6544\n0.2804\n0.5715\n602\n803\n1545\n4610\n0.0966\n0.0427\n0.6137\n0.1422\n0.7684\n145\n481\n875\n6059\nBMJ-WSM5-God-MYJ\n0.2169\n0.0787\n0.7667\n0.3149\n0.5893\n676\n970\n1471\n4443\n0.0908\n0.037\n0.6127\n0.1343\n0.7808\n137\n488\n883\n6052\nBMJ-WSM5-God-YSU\n0.2278\n0.0779\n0.8728\n0.3475\n0.6019\n746\n1128\n1401\n4285\n0.0947\n0.031\n0.802\n0.1559\n0.8056\n159\n659\n861\n5881\nBMJ-WSM6-Duh-MYJ\n0.1712\n0.0601\n0.5515\n0.2268\n0.5887\n487\n697\n1660\n4716\n0.0834\n0.0379\n0.4775\n0.1137\n0.7618\n116\n371\n904\n6169\nBMJ-WSM6-Duh-YSU\n0.1971\n0.069\n0.68\n0.2767\n0.5932\n594\n866\n1553\n4547\n0.0992\n0.0432\n0.652\n0.149\n0.7714\n152\n513\n868\n6027\nBMJ-WSM6-God-MYJ\n0.2191\n0.069\n0.8714\n0.3363\n0.6141\n722\n1149\n1425\n4264\n0.1\n0.0385\n0.7578\n0.1598\n0.7891\n163\n610\n857\n5930\nBMJ-WSM6-God-YSU\n0.215\n0.0632\n0.885\n0.3335\n0.6232\n716\n1184\n1431\n4229\n0.0824\n0.0185\n0.8039\n0.1373\n0.8293\n140\n680\n880\n5860\nKF-Lin-Duh-MYJ\n0.2144\n0.0871\n0.6782\n0.2962\n0.5632\n636\n820\n1511\n4593\n0.1162\n0.0632\n0.601\n0.1667\n0.7227\n170\n443\n850\n6097\nKF-Lin-Duh-YSU\n0.2063\n0.0825\n0.6502\n0.2823\n0.5659\n606\n790\n1541\n4623\n0.1069\n0.052\n0.6343\n0.1578\n0.7512\n161\n486\n859\n6054\nKF-Lin-God-MYJ\n0.2399\n0.0805\n0.9693\n0.381\n0.6069\n818\n1263\n1329\n4150\n0.1392\n0.0702\n0.9333\n0.2363\n0.7468\n241\n711\n779\n5829\nKF-Lin-God-YSU\n0.2427\n0.0831\n0.9725\n0.3852\n0.6039\n827\n1261\n1320\n4152\n0.1309\n0.0605\n0.9647\n0.2275\n0.7642\n232\n752\n788\n5788\nKF-WSM3-Duh-MYJ\n0.2377\n0.0889\n0.8677\n0.3586\n0.5867\n770\n1093\n1377\n4320\n0.1242\n0.0613\n0.7922\n0.198\n0.75\n202\n606\n818\n5934\nKF-WSM3-Duh-YSU\n0.2618\n0.1129\n0.8812\n0.3903\n0.5571\n838\n1054\n1309\n4359\n0.1675\n0.0983\n0.948\n0.2794\n0.7053\n285\n682\n735\n5858\nKF-WSM3-God-MYJ\n0.2596\n0.0844\n1.1495\n0.4429\n0.6147\n951\n1517\n1196\n3896\n0.1334\n0.0538\n1.2235\n0.2618\n0.7861\n267\n981\n753\n5559\nKF-WSM3-God-YSU\n0.2817\n0.1073\n1.1593\n0.4746\n0.5906\n1019\n1470\n1128\n3943\n0.1631\n0.081\n1.3216\n0.3255\n0.7537\n332\n1016\n688\n5524\nKF-WSM5-Duh-MYJ\n0.2508\n0.1005\n0.8882\n0.3787\n0.5737\n813\n1094\n1334\n4319\n0.1333\n0.0694\n0.8167\n0.2137\n0.7383\n218\n615\n802\n5925\nKF-WSM5-Duh-YSU\n0.2671\n0.1193\n0.8738\n0.395\n0.548\n848\n1028\n1299\n4385\n0.158\n0.0902\n0.9118\n0.2608\n0.714\n266\n664\n754\n5876\nKF-WSM5-God-MYJ\n0.2723\n0.0967\n1.1635\n0.463\n0.6021\n994\n1504\n1153\n3909\n0.1354\n0.0571\n1.1863\n0.2608\n0.7802\n266\n944\n754\n5596\nKF-WSM5-God-YSU\n0.2652\n0.0932\n1.1178\n0.4439\n0.6029\n953\n1447\n1194\n3966\n0.1507\n0.0709\n1.2382\n0.2931\n0.7633\n299\n964\n721\n5576\nKF-WSM6-Duh-MYJ\n0.2545\n0.0952\n0.9767\n0.401\n0.5894\n861\n1236\n1286\n4177\n0.1237\n0.0546\n0.9324\n0.2127\n0.7718\n217\n734\n803\n5806\nKF-WSM6-Duh-YSU\n0.2615\n0.1084\n0.9208\n0.3982\n0.5675\n855\n1122\n1292\n4291\n0.156\n0.0841\n1.0127\n0.2716\n0.7318\n277\n756\n743\n5784\nKF-WSM6-God-MYJ\n0.2693\n0.0883\n1.2259\n0.4723\n0.6147\n1014\n1618\n1133\n3795\n0.1398\n0.0571\n1.3265\n0.2853\n0.7849\n291\n1062\n729\n5478\nKF-WSM6-God-YSU\n0.2853\n0.1072\n1.2054\n0.4895\n0.5939\n1051\n1537\n1096\n3876\n0.1513\n0.0661\n1.4245\n0.3186\n0.7763\n325\n1128\n695\n5412\n\n\n### 3.2. Skill Score Validation\n\nThe charts for the skill scores at the two thresholds in Figures5 and 6 show that the TS value at the 24 h forecast range is about 0.2 to 0.27 for >25 mm/24 h and 0.1 to 0.2 for >50 mm/24 h. At 48 h, the TS is around ∼0.18 to 0.3 and ∼0.1 to 0.19 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h. At 72 h, the TS is around 0.15 to 0.25 and ∼0.08 to 0.15 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h.Figure 5\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 25 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure 6\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 50 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)The probability of detection decreases and the false alarm rate clearly increases with forecast ranges and the validating thresholds. In addition, when the threshold increases, the difference between the TS and the ETS decreases which means that the amount of Hitsrandom is too small or the cause of very small hit rate (Hitsrandom rate decreases by 90% when changing the threshold from >25 mm/24 h to >50 mm/24 h).Specific comparisons between the combinations of the BMJ or KF scheme show that the KF scheme model’s skills in heavy rain forecast in the northern region of Vietnam are better than BMJ scheme model’s skills. The average TS with the KF scheme can be about 15–25% larger than that using the BMJ scheme. If the difference of skills in a regional model is insignificant when changing the physical parameterization schemes, the lateral boundary conditions (from global forecasts) will greatly affect the quality of the dynamical downscaling forecasts after 24 h integration. However, here the skill difference when combining the two different cumulus schemes in the longer forecast range (such as 72 h) shows the importance of convection simulation capability contributing to the forecasting quality of the model. Detailed evaluation of the combination with the radiation physical schemes of KF or BMJ does not show any difference compared to changing the boundary layer schemes when looking at the skill scores TS or ETS.The combinations with YSU boundary layer schemes have better skills compared to those with MYJ schemes. In addition, when changing the complexity of the cloud microphysics schemes, the more complex the microphysical processing simulation, the better the TS and ETS (at 24, 48, and 72 h forecast ranges and two validating thresholds; see Figure7 for comparison of the change in TSs and ETSs with the cloud microphysics scheme).Figure 7\nBrief comparison of TSs and ETSs for illustration of sensitivities with microphysics schemes.For the skill comparisons of different event types (I, II, III, and IV), Figure8 shows the TSs at thresholds over 25 mm/24 h and over 50 mm/24 h for 24 h and 48 h forecast ranges. For type I, which is associated with the activity of ITCZ and low-pressure trough over the Bac Bo area, the KF scheme proved its forecast skills in almost forecast ranges and thresholds mentioned in this research. However, with the rain caused by tropical cyclone in type II, the difference between KF and BMJ schemes within 24 h was smaller than that in type I. In 48 h and 72 h, the BMJ scheme showed more skilled forecast with the skill score for threshold 25 mm ranging from 0.25 to 0.35 of the BMJ scheme, compared with 0.2 to 0.3 of the KF scheme, and the skill score for threshold 50 mm ranging from 0.2 to 0.3 of the BMJ scheme, compared with 0.2 to 0.25 of the KF scheme. In type II, both KF and BMJ schemes combined with the simple cloud microphysics Lin scheme showed lowest skill score. For type III, which is associated with the activity of cold surge and its role in squeezing the low-pressure trough from the north towards Bac Bo, the KF scheme was only skillful in threshold 25 mm in 24 h. Particularly in type IV, which contains heavy rain events caused by a complex combination of situations resulting in a trough in Bac Bo, the KF scheme still showed skilled forecast compared to very low forecast (no skill with threshold over 50 mm/24 h and 25 mm/24 h) of BMJ scheme experiments. More details of TSs for different types are listed in Tables 7 and 8.Figure 8\nTSs for different types of heavy rainfall events in northern Vietnam for daily accumulation thresholds over (a) 25 mm and 50 mm (b) at 24 h forecast ranges and over 25 mm (c) and 50 mm (d) at 48 h forecast ranges. The right vertical axis is from 0 to 0.4.\n(a)\n(b)\n(c)\n(d)Table 7\nTSs for different types of main heavy rainfall, at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.2546\n0.1309\n0.15\n0.0682\n0.124\n0.0791\n0.0769\n0\nBMJ-Lin-Duh-YSU\n0.2625\n0.1019\n0.2346\n0.0706\n0.1244\n0.0692\n0.16\n0\nBMJ-Lin-God-MYJ\n0.2644\n0.1638\n0.2235\n0.1011\n0.139\n0.1064\n0.0882\n0\nBMJ-Lin-God-YSU\n0.2761\n0.1519\n0.1939\n0.093\n0.1369\n0.087\n0.1562\n0.0333\nBMJ-WSM3-Duh-MYJ\n0.2503\n0.1237\n0.1818\n0.09\n0.1279\n0.0621\n0.129\n0\nBMJ-WSM3-Duh-YSU\n0.2745\n0.1179\n0.2439\n0.0652\n0.1379\n0.0699\n0.1471\n0\nBMJ-WSM3-God-MYJ\n0.2834\n0.1651\n0.21\n0.09\n0.1556\n0.1104\n0.1136\n0\nBMJ-WSM3-God-YSU\n0.2943\n0.1505\n0.202\n0.0745\n0.167\n0.0798\n0.1304\n0.0312\nBMJ-WSM5-Duh-MYJ\n0.2618\n0.1349\n0.1977\n0.0707\n0.1429\n0.0563\n0.1515\n0\nBMJ-WSM5-Duh-YSU\n0.263\n0.1351\n0.2073\n0.0652\n0.1603\n0.0789\n0.1389\n0\nBMJ-WSM5-God-MYJ\n0.3032\n0.1376\n0.1978\n0.0714\n0.1565\n0.0496\n0.1515\n0.0345\nBMJ-WSM5-God-YSU\n0.3028\n0.1525\n0.1939\n0.0737\n0.1677\n0.0696\n0.15\n0.0312\nBMJ-WSM6-Duh-MYJ\n0.2571\n0.133\n0.1304\n0.0926\n0.1403\n0.0872\n0.0732\n0\nBMJ-WSM6-Duh-YSU\n0.2643\n0.1281\n0.1828\n0.06\n0.1714\n0.0719\n0.1212\n0\nBMJ-WSM6-God-MYJ\n0.2991\n0.1781\n0.2574\n0.0784\n0.1706\n0.1391\n0.15\n0.0256\nBMJ-WSM6-God-YSU\n0.3043\n0.1595\n0.1944\n0.0652\n0.1916\n0.1104\n0.1429\n0\nKF-Lin-Duh-MYJ\n0.2881\n0.1673\n0.2136\n0.1449\n0.1844\n0.0683\n0.1026\n0.1818\nKF-Lin-Duh-YSU\n0.3125\n0.1951\n0.2474\n0.1667\n0.1778\n0.0698\n0.06\n0.1892\nKF-Lin-God-MYJ\n0.2917\n0.1914\n0.2255\n0.1558\n0.1725\n0.1237\n0.0889\n0.1471\nKF-Lin-God-YSU\n0.3129\n0.183\n0.2653\n0.1769\n0.1839\n0.1162\n0.093\n0.1316\nKF-WSM3-Duh-MYJ\n0.2872\n0.1776\n0.2212\n0.1655\n0.1893\n0.0753\n0.1224\n0.1667\nKF-WSM3-Duh-YSU\n0.309\n0.1881\n0.2551\n0.219\n0.1858\n0.1176\n0.1111\n0.2059\nKF-WSM3-God-MYJ\n0.3264\n0.1924\n0.2526\n0.1656\n0.2057\n0.0909\n0.0889\n0.1579\nKF-WSM3-God-YSU\n0.3188\n0.1841\n0.3069\n0.2014\n0.2027\n0.1185\n0.098\n0.1795\nKF-WSM5-Duh-MYJ\n0.3062\n0.1836\n0.25\n0.1655\n0.2048\n0.0688\n0.0851\n0.1795\nKF-WSM5-Duh-YSU\n0.3116\n0.1892\n0.2653\n0.1838\n0.1823\n0.1111\n0.08\n0.1818\nKF-WSM5-God-MYJ\n0.3141\n0.1996\n0.25\n0.1772\n0.2047\n0.1122\n0.1064\n0.1622\nKF-WSM5-God-YSU\n0.3317\n0.2025\n0.2642\n0.1875\n0.2063\n0.1085\n0.1228\n0.1579\nKF-WSM6-Duh-MYJ\n0.3063\n0.1823\n0.2233\n0.1812\n0.1963\n0.0952\n0.0833\n0.1538\nKF-WSM6-Duh-YSU\n0.3339\n0.1927\n0.2476\n0.1898\n0.2244\n0.1179\n0.1071\n0.2812\nKF-WSM6-God-MYJ\n0.3138\n0.1781\n0.2268\n0.1465\n0.2207\n0.1268\n0.1429\n0.125\nKF-WSM6-God-YSU\n0.3323\n0.1793\n0.2843\n0.2\n0.2078\n0.1255\n0.1538\n0.1429Table 8\nTSs for different types of main heavy rainfall, at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.1629\n0.2426\n0.0854\n0.0922\n0.0768\n0.1533\n0.0526\n0.0333\nBMJ-Lin-Duh-YSU\n0.1755\n0.2271\n0.0909\n0.1438\n0.0971\n0.1559\n0.0833\n0.0164\nBMJ-Lin-God-MYJ\n0.2276\n0.3104\n0.075\n0.0798\n0.1139\n0.1631\n0.0333\n0.0303\nBMJ-Lin-God-YSU\n0.2482\n0.2723\n0.1429\n0.12\n0.1405\n0.1543\n0.0385\n0.0167\nBMJ-WSM3-Duh-MYJ\n0.2235\n0.3055\n0.0833\n0.1311\n0.1147\n0.2848\n0.0571\n0\nBMJ-WSM3-Duh-YSU\n0.2365\n0.3419\n0.1538\n0.1617\n0.1136\n0.3072\n0.0556\n0.0116\nBMJ-WSM3-God-MYJ\n0.2583\n0.3448\n0.0979\n0.1029\n0.1386\n0.2466\n0.0182\n0\nBMJ-WSM3-God-YSU\n0.2587\n0.3338\n0.1176\n0.1272\n0.1541\n0.2125\n0.02\n0\nBMJ-WSM5-Duh-MYJ\n0.2301\n0.3206\n0.0928\n0.1228\n0.1287\n0.2706\n0.0741\n0.0115\nBMJ-WSM5-Duh-YSU\n0.243\n0.3157\n0.1373\n0.172\n0.1397\n0.2813\n0.0312\n0.0241\nBMJ-WSM5-God-MYJ\n0.2594\n0.344\n0.1327\n0.1337\n0.1479\n0.2928\n0.0286\n0\nBMJ-WSM5-God-YSU\n0.2588\n0.3329\n0.1797\n0.1364\n0.1383\n0.2918\n0\n0.0115\nBMJ-WSM6-Duh-MYJ\n0.217\n0.304\n0.1418\n0.1027\n0.1214\n0.2212\n0.0784\n0.0235\nBMJ-WSM6-Duh-YSU\n0.2331\n0.3216\n0.1368\n0.1587\n0.1348\n0.2968\n0.0714\n0.0103\nBMJ-WSM6-God-MYJ\n0.2522\n0.3055\n0.1474\n0.0955\n0.132\n0.2314\n0.0541\n0.0215\nBMJ-WSM6-God-YSU\n0.2739\n0.3707\n0.2\n0.1026\n0.1333\n0.2658\n0.0484\n0.0319\nKF-Lin-Duh-MYJ\n0.2259\n0.2093\n0.0811\n0.225\n0.1331\n0.1208\n0.027\n0.1899\nKF-Lin-Duh-YSU\n0.2714\n0.172\n0.0957\n0.2199\n0.1347\n0.1017\n0.0667\n0.1395\nKF-Lin-God-MYJ\n0.268\n0.2537\n0.1043\n0.2771\n0.1267\n0.1545\n0.0385\n0.1237\nKF-Lin-God-YSU\n0.3125\n0.2384\n0.1058\n0.2788\n0.1704\n0.1608\n0.0488\n0.1327\nKF-WSM3-Duh-MYJ\n0.2689\n0.2805\n0.1298\n0.2783\n0.156\n0.241\n0.0577\n0.1327\nKF-WSM3-Duh-YSU\n0.3066\n0.2739\n0.069\n0.285\n0.1971\n0.2068\n0.0444\n0.2088\nKF-WSM3-God-MYJ\n0.2945\n0.2976\n0.1286\n0.2595\n0.1651\n0.2451\n0.0526\n0.1111\nKF-WSM3-God-YSU\n0.3202\n0.2956\n0.1407\n0.2822\n0.1872\n0.2136\n0.0923\n0.1569\nKF-WSM5-Duh-MYJ\n0.2924\n0.2921\n0.1176\n0.2546\n0.1586\n0.238\n0.0727\n0.1765\nKF-WSM5-Duh-YSU\n0.3217\n0.2847\n0.0976\n0.26\n0.1995\n0.2403\n0.0755\n0.1753\nKF-WSM5-God-MYJ\n0.2946\n0.2953\n0.1192\n0.2672\n0.1499\n0.25\n0.038\n0.1293\nKF-WSM5-God-YSU\n0.3265\n0.2768\n0.1361\n0.2881\n0.201\n0.2344\n0.0959\n0.1404\nKF-WSM6-Duh-MYJ\n0.2922\n0.2874\n0.1389\n0.2597\n0.1874\n0.25\n0.0685\n0.1667\nKF-WSM6-Duh-YSU\n0.3291\n0.2706\n0.1311\n0.2995\n0.208\n0.2378\n0.0893\n0.2083\nKF-WSM6-God-MYJ\n0.3002\n0.3115\n0.1465\n0.2556\n0.1675\n0.2537\n0.0435\n0.1333\nKF-WSM6-God-YSU\n0.3426\n0.2798\n0.1595\n0.2888\n0.2091\n0.2565\n0.0674\n0.177\n\n\n## 3.1. General Performance\n\nThe histogram charts (Figure3) show the number of observations or forecasts that occurred at stations for given ranges—or bins. We divided rainfall into 4 main bins (0–25 mm, 25–50 mm, 50–100 mm, and >100 mm) for different rainfall classes. For all 24 h, 48 h, and 72 h forecasts, it is quite clear that most forecasts from the BMJ scheme are in the 0–25 mm range, higher than the number of observations, while the KF scheme tends to have less forecasts than observations. In contrast, at the thresholds greater than 25 mm, for the BMJ scheme, the number of forecasts is less than the number of observations, while the KF scheme tends to have more forecasts than the number of observations.Figure 3\nHistogram of daily rainfall frequency at different thresholds (bins) for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dotted lines are the observation frequency. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure4 shows the BIAS score at different thresholds (>25 mm/24 h and >50 mm/24 h) and separated for KF and BMJ scheme combinations. Overall assessment through the BIAS score is quite similar to the results from the evaluation through histograms: simulations with the BMJ scheme tend to be lower than observations at most forecast ranges and different thresholds (BIAS < 1), while the simulations with the KF scheme tend to be higher than the observations (BIAS > 1). The BIAS tends to decrease significantly when the forecast ranges increase. When the validation thresholds are increased, the simulation with the BMJ scheme tends to decrease BIAS, whereas in combination with the KF scheme, BIAS increases with both the forecast ranges and the evaluation thresholds.Figure 4\nThe BIAS score at 24 h, 48 h, and 72 h for different thresholds (over 25 mm, 50 mm, and 100 mm) for BMJ scheme combinations (a) and for KF scheme combinations (b). The number of samples is 8064 for an individual forecast.\n(a)\n(b)The individual assessment in each combination of BMJ and KF schemes shows that when combined with the Goddard radiation scheme, the BIAS increases from 0.1 to 0.2 compared to the Dudhia scheme. These results are similar for all validating thresholds and for 24 h, 48 h, and 72 h forecast ranges. The difference in simulations with different boundary layer schemes is unclear while evaluating with thresholds below 25 mm/24 h; however, at higher thresholds, it is apparent: the change of BIAS when combining with the KF scheme is much higher. For example, compared to the BMJ scheme, the BIAS score of KF-WSM3-God-MYJ at the >100 mm/24 h threshold at the 24-hour forecast range is 1.6458 and that of KF-WSM3-God-YSU is 2.0833, while BMJ-WSM3-God-MYJ and BMJ-WSM3-God-YSU had approximately equal BIAS scores (0.8229). Thus, the combinations of the boundary layer schemes are different (here only the two schemes YSU and MYJ) with the KF scheme being much more sensitive to its combinations with the BMJ scheme and especially at the high rainfall thresholds. Details of numerical values of BIAS can be found in Tables4, 5, and 6.Table 4\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.207\n0.1517\n0.5854\n0.2719\n0.5355\n301\n347\n806\n6106\n0.1049\n0.0874\n0.4139\n0.1342\n0.6757\n60\n125\n387\n6988\nBMJ-Lin-Duh-YSU\n0.2087\n0.1551\n0.5592\n0.2692\n0.5186\n298\n321\n809\n6132\n0.1072\n0.0899\n0.4094\n0.1365\n0.6667\n61\n122\n386\n6991\nBMJ-Lin-God-MYJ\n0.2283\n0.1613\n0.7986\n0.3342\n0.5814\n370\n514\n737\n5939\n0.1225\n0.0996\n0.6197\n0.1767\n0.7148\n79\n198\n368\n6915\nBMJ-Lin-God-YSU\n0.2304\n0.1649\n0.7706\n0.3315\n0.5698\n367\n486\n740\n5967\n0.122\n0.1\n0.5839\n0.1723\n0.705\n77\n184\n370\n6929\nBMJ-WSM3-Duh-MYJ\n0.2051\n0.1433\n0.6929\n0.2882\n0.5841\n319\n448\n788\n6005\n0.1066\n0.0846\n0.5794\n0.1521\n0.7375\n68\n191\n379\n6922\nBMJ-WSM3-Duh-YSU\n0.2201\n0.1595\n0.6775\n0.3026\n0.5533\n335\n415\n772\n6038\n0.1153\n0.0928\n0.6018\n0.1655\n0.7249\n74\n195\n373\n6918\nBMJ-WSM3-God-MYJ\n0.2388\n0.1649\n0.9539\n0.3767\n0.6051\n417\n639\n690\n5814\n0.1364\n0.1089\n0.8456\n0.2215\n0.7381\n99\n279\n348\n6834\nBMJ-WSM3-God-YSU\n0.2408\n0.1709\n0.8663\n0.3622\n0.5819\n401\n558\n706\n5895\n0.1391\n0.1116\n0.8501\n0.226\n0.7342\n101\n279\n346\n6834\nBMJ-WSM5-Duh-MYJ\n0.2147\n0.153\n0.692\n0.299\n0.5679\n331\n435\n776\n6018\n0.1166\n0.0951\n0.5638\n0.1633\n0.7103\n73\n179\n374\n6934\nBMJ-WSM5-Duh-YSU\n0.2156\n0.1553\n0.6703\n0.2963\n0.558\n328\n414\n779\n6039\n0.1319\n0.1092\n0.613\n0.1879\n0.6934\n84\n190\n363\n6923\nBMJ-WSM5-God-MYJ\n0.2408\n0.1743\n0.7967\n0.3487\n0.5624\n386\n496\n721\n5957\n0.1273\n0.1044\n0.6242\n0.1834\n0.7061\n82\n197\n365\n6916\nBMJ-WSM5-God-YSU\n0.2449\n0.1766\n0.8365\n0.3613\n0.568\n400\n526\n707\n5927\n0.1386\n0.112\n0.8009\n0.2192\n0.7263\n98\n260\n349\n6853\nBMJ-WSM6-Duh-MYJ\n0.2079\n0.1419\n0.7687\n0.3044\n0.604\n337\n514\n770\n5939\n0.1166\n0.0925\n0.6711\n0.1745\n0.74\n78\n222\n369\n6891\nBMJ-WSM6-Duh-YSU\n0.2126\n0.1469\n0.7669\n0.3098\n0.596\n343\n506\n764\n5947\n0.1386\n0.1135\n0.7271\n0.2103\n0.7108\n94\n231\n353\n6882\nBMJ-WSM6-God-MYJ\n0.2552\n0.1793\n1.0126\n0.4092\n0.5959\n453\n668\n654\n5785\n0.1554\n0.1269\n0.9128\n0.2573\n0.7181\n115\n293\n332\n6820\nBMJ-WSM6-God-YSU\n0.2477\n0.1747\n0.9386\n0.3848\n0.59\n426\n613\n681\n5840\n0.1644\n0.1355\n0.9485\n0.2752\n0.7099\n123\n301\n324\n6812\nKF-Lin-Duh-MYJ\n0.2399\n0.162\n1.0497\n0.3966\n0.6222\n439\n723\n668\n5730\n0.1525\n0.1262\n0.7919\n0.2371\n0.7006\n106\n248\n341\n6865\nKF-Lin-Duh-YSU\n0.2656\n0.1884\n1.0533\n0.4309\n0.5909\n477\n689\n630\n5764\n0.1457\n0.1168\n0.9351\n0.2461\n0.7368\n110\n308\n337\n6805\nKF-Lin-God-MYJ\n0.2507\n0.165\n1.2755\n0.4562\n0.6424\n505\n907\n602\n5546\n0.1564\n0.1233\n1.2327\n0.302\n0.755\n135\n416\n312\n6697\nKF-Lin-God-YSU\n0.2649\n0.1795\n1.2818\n0.4779\n0.6272\n529\n890\n578\n5563\n0.162\n0.128\n1.311\n0.3221\n0.7543\n144\n442\n303\n6671\nKF-WSM3-Duh-MYJ\n0.2442\n0.1627\n1.1491\n0.4219\n0.6329\n467\n805\n640\n5648\n0.1581\n0.1266\n1.1141\n0.2886\n0.741\n129\n369\n318\n6744\nKF-WSM3-Duh-YSU\n0.2664\n0.1843\n1.1861\n0.4598\n0.6123\n509\n804\n598\n5649\n0.1667\n0.135\n1.1298\n0.3043\n0.7307\n136\n369\n311\n6744\nKF-WSM3-God-MYJ\n0.2743\n0.1867\n1.3668\n0.5095\n0.6272\n564\n949\n543\n5504\n0.172\n0.1368\n1.4385\n0.3579\n0.7512\n160\n483\n287\n6630\nKF-WSM3-God-YSU\n0.2739\n0.1852\n1.4029\n0.5167\n0.6317\n572\n981\n535\n5472\n0.1778\n0.1413\n1.5638\n0.387\n0.7525\n173\n526\n274\n6587\nKF-WSM5-Duh-MYJ\n0.2583\n0.1766\n1.1653\n0.4444\n0.6186\n492\n798\n615\n5655\n0.1648\n0.1333\n1.1186\n0.2998\n0.732\n134\n366\n313\n6747\nKF-WSM5-Duh-YSU\n0.2656\n0.1828\n1.2042\n0.4625\n0.6159\n512\n821\n595\n5632\n0.1588\n0.1269\n1.1387\n0.2931\n0.7426\n131\n378\n316\n6735\nKF-WSM5-God-MYJ\n0.2693\n0.1812\n1.3758\n0.5041\n0.6336\n558\n965\n549\n5488\n0.1773\n0.1421\n1.4362\n0.3669\n0.7445\n164\n478\n283\n6635\nKF-WSM5-God-YSU\n0.2826\n0.1939\n1.4146\n0.5321\n0.6239\n589\n977\n518\n5476\n0.178\n0.1415\n1.5615\n0.387\n0.7521\n173\n525\n274\n6588\nKF-WSM6-Duh-MYJ\n0.2598\n0.1758\n1.234\n0.4607\n0.6266\n510\n856\n597\n5597\n0.1663\n0.1326\n1.2908\n0.3266\n0.747\n146\n431\n301\n6682\nKF-WSM6-Duh-YSU\n0.28\n0.1952\n1.2836\n0.4995\n0.6108\n553\n868\n554\n5585\n0.1958\n0.1611\n1.4049\n0.3937\n0.7197\n176\n452\n271\n6661\nKF-WSM6-God-MYJ\n0.26\n0.1698\n1.43\n0.5014\n0.6494\n555\n1028\n552\n5425\n0.1926\n0.156\n1.5906\n0.4183\n0.737\n187\n524\n260\n6589\nKF-WSM6-God-YSU\n0.2792\n0.1888\n1.467\n0.5384\n0.633\n596\n1028\n511\n5425\n0.1844\n0.1463\n1.7584\n0.4295\n0.7557\n192\n594\n255\n6519Table 5\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.1791\n0.1054\n0.4842\n0.2254\n0.5344\n365\n419\n1254\n5522\n0.0962\n0.0703\n0.3463\n0.1181\n0.6589\n88\n170\n657\n6645\nBMJ-Lin-Duh-YSU\n0.1853\n0.1069\n0.5287\n0.239\n0.5479\n387\n469\n1232\n5472\n0.1105\n0.0803\n0.4295\n0.1423\n0.6687\n106\n214\n639\n6601\nBMJ-Lin-God-MYJ\n0.234\n0.1345\n0.769\n0.3354\n0.5639\n543\n702\n1076\n5239\n0.1225\n0.0846\n0.5987\n0.1745\n0.7085\n130\n316\n615\n6499\nBMJ-Lin-God-YSU\n0.243\n0.1408\n0.8073\n0.3533\n0.5624\n572\n735\n1047\n5206\n0.1357\n0.0971\n0.6174\n0.1933\n0.687\n144\n316\n601\n6499\nBMJ-WSM3-Duh-MYJ\n0.2332\n0.1353\n0.7505\n0.3311\n0.5588\n536\n679\n1083\n5262\n0.152\n0.109\n0.7396\n0.2295\n0.6897\n171\n380\n574\n6435\nBMJ-WSM3-Duh-YSU\n0.2579\n0.1573\n0.7956\n0.3681\n0.5373\n596\n692\n1023\n5249\n0.1587\n0.1142\n0.7839\n0.2443\n0.6884\n182\n402\n563\n6413\nBMJ-WSM3-God-MYJ\n0.2616\n0.1452\n1.0167\n0.4182\n0.5887\n677\n969\n942\n4972\n0.1546\n0.1034\n1.0054\n0.2685\n0.733\n200\n549\n545\n6266\nBMJ-WSM3-God-YSU\n0.2641\n0.148\n1.0136\n0.4206\n0.585\n681\n960\n938\n4981\n0.1539\n0.1034\n0.9826\n0.2644\n0.7309\n197\n535\n548\n6280\nBMJ-WSM5-Duh-MYJ\n0.2425\n0.1455\n0.7437\n0.3403\n0.5424\n551\n653\n1068\n5288\n0.1582\n0.1171\n0.6899\n0.2309\n0.6654\n172\n342\n573\n6473\nBMJ-WSM5-Duh-YSU\n0.2545\n0.154\n0.7931\n0.3638\n0.5413\n589\n695\n1030\n5246\n0.1698\n0.1257\n0.7758\n0.2577\n0.6678\n192\n386\n553\n6429\nBMJ-WSM5-God-MYJ\n0.2687\n0.1601\n0.9074\n0.404\n0.5548\n654\n815\n965\n5126\n0.174\n0.1286\n0.8201\n0.2698\n0.671\n201\n410\n544\n6405\nBMJ-WSM5-God-YSU\n0.2677\n0.1522\n1.0068\n0.4237\n0.5791\n686\n944\n933\n4997\n0.1674\n0.1166\n1.0027\n0.2872\n0.7135\n214\n533\n531\n6282\nBMJ-WSM6-Duh-MYJ\n0.2282\n0.1261\n0.7986\n0.3342\n0.5816\n541\n752\n1078\n5189\n0.1414\n0.097\n0.7772\n0.2201\n0.7168\n164\n415\n581\n6400\nBMJ-WSM6-Duh-YSU\n0.2471\n0.1426\n0.8394\n0.3644\n0.5659\n590\n769\n1029\n5172\n0.169\n0.1198\n0.9409\n0.2805\n0.7019\n209\n492\n536\n6323\nBMJ-WSM6-God-MYJ\n0.2484\n0.1255\n1.1081\n0.4194\n0.6215\n679\n1115\n940\n4826\n0.1471\n0.0931\n1.1141\n0.2711\n0.7566\n202\n628\n543\n6187\nBMJ-WSM6-God-YSU\n0.283\n0.1642\n1.0723\n0.4571\n0.5737\n740\n996\n879\n4945\n0.1588\n0.1053\n1.0966\n0.2872\n0.7381\n214\n603\n531\n6212\nKF-Lin-Duh-MYJ\n0.2144\n0.1147\n0.7634\n0.3113\n0.5922\n504\n732\n1115\n5209\n0.1299\n0.0905\n0.6349\n0.1879\n0.704\n140\n333\n605\n6482\nKF-Lin-Duh-YSU\n0.2312\n0.136\n0.7171\n0.3224\n0.5504\n522\n639\n1097\n5302\n0.124\n0.0847\n0.6309\n0.1799\n0.7149\n134\n336\n611\n6479\nKF-Lin-God-MYJ\n0.2584\n0.1355\n1.1174\n0.4348\n0.6108\n704\n1105\n915\n4836\n0.1304\n0.0781\n1.0362\n0.2349\n0.7733\n175\n597\n570\n6218\nKF-Lin-God-YSU\n0.2806\n0.1647\n1.0241\n0.4435\n0.5669\n718\n940\n901\n5001\n0.1611\n0.1097\n1.0215\n0.2805\n0.7254\n209\n552\n536\n6263\nKF-WSM3-Duh-MYJ\n0.266\n0.149\n1.0284\n0.4262\n0.5856\n690\n975\n929\n4966\n0.1735\n0.1212\n1.0604\n0.3047\n0.7127\n227\n563\n518\n6252\nKF-WSM3-Duh-YSU\n0.2839\n0.1719\n0.9691\n0.4355\n0.5507\n705\n864\n914\n5077\n0.1948\n0.1437\n1.0255\n0.3302\n0.678\n246\n518\n499\n6297\nKF-WSM3-God-MYJ\n0.2845\n0.1503\n1.3484\n0.5201\n0.6143\n842\n1341\n777\n4600\n0.1766\n0.1161\n1.4416\n0.3664\n0.7458\n273\n801\n472\n6014\nKF-WSM3-God-YSU\n0.3018\n0.1706\n1.3125\n0.5361\n0.5915\n868\n1257\n751\n4684\n0.1877\n0.1271\n1.455\n0.3879\n0.7334\n289\n795\n456\n6020\nKF-WSM5-Duh-MYJ\n0.2801\n0.1622\n1.055\n0.4497\n0.5738\n728\n980\n891\n4961\n0.1785\n0.1262\n1.0644\n0.3128\n0.7062\n233\n560\n512\n6255\nKF-WSM5-Duh-YSU\n0.2948\n0.1812\n1.0019\n0.4558\n0.545\n738\n884\n881\n5057\n0.2037\n0.1509\n1.102\n0.3557\n0.6772\n265\n556\n480\n6259\nKF-WSM5-God-MYJ\n0.284\n0.1477\n1.3904\n0.5287\n0.6197\n856\n1395\n763\n4546\n0.1685\n0.1064\n1.5221\n0.3638\n0.761\n271\n863\n474\n5952\nKF-WSM5-God-YSU\n0.2998\n0.1683\n1.3162\n0.5343\n0.5941\n865\n1266\n754\n4675\n0.1996\n0.1388\n1.4846\n0.4134\n0.7215\n308\n798\n437\n6017\nKF-WSM6-Duh-MYJ\n0.2799\n0.1571\n1.1353\n0.467\n0.5887\n756\n1082\n863\n4859\n0.1973\n0.1415\n1.2242\n0.3664\n0.7007\n273\n639\n472\n6176\nKF-WSM6-Duh-YSU\n0.3002\n0.1833\n1.0574\n0.475\n0.5508\n769\n943\n850\n4998\n0.211\n0.157\n1.157\n0.3758\n0.6752\n280\n582\n465\n6233\nKF-WSM6-God-MYJ\n0.2917\n0.155\n1.4095\n0.5442\n0.6139\n881\n1401\n738\n4540\n0.1788\n0.1154\n1.6107\n0.396\n0.7542\n295\n905\n450\n5910\nKF-WSM6-God-YSU\n0.3113\n0.1785\n1.357\n0.5596\n0.5876\n906\n1291\n713\n4650\n0.2101\n0.1475\n1.6054\n0.4523\n0.7182\n337\n859\n408\n5956Table 6\nSkill scores (TS, ETS, BIAS, POD, and FAR) and hit ratesH, false alarm rates F, missed rates M, and total corrected rates T at the 72 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016. The number of samples is 8064.\n>25 mm/24 h\n>50 mm/24 h\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nTS\nETS\nBIAS\nPOD\nFAR\nH\nF\nM\nT\nBMJ-Lin-Duh-MYJ\n0.154\n0.0672\n0.3964\n0.1863\n0.53\n400\n451\n1747\n4962\n0.0797\n0.0443\n0.3412\n0.099\n0.7098\n101\n247\n919\n6293\nBMJ-Lin-Duh-YSU\n0.1539\n0.0595\n0.442\n0.1924\n0.5648\n413\n536\n1734\n4877\n0.0766\n0.0373\n0.3912\n0.099\n0.7469\n101\n298\n919\n6242\nBMJ-Lin-God-MYJ\n0.1755\n0.0611\n0.5752\n0.2352\n0.5911\n505\n730\n1642\n4683\n0.0719\n0.0285\n0.4471\n0.0971\n0.7829\n99\n357\n921\n6183\nBMJ-Lin-God-YSU\n0.1893\n0.0645\n0.653\n0.2632\n0.597\n565\n837\n1582\n4576\n0.0832\n0.0342\n0.5314\n0.1176\n0.7786\n120\n422\n900\n6118\nBMJ-WSM3-Duh-MYJ\n0.1802\n0.0656\n0.5771\n0.2408\n0.5827\n517\n722\n1630\n4691\n0.0867\n0.0391\n0.5108\n0.1206\n0.7639\n123\n398\n897\n6142\nBMJ-WSM3-Duh-YSU\n0.1988\n0.0737\n0.6572\n0.2748\n0.5819\n590\n821\n1557\n4592\n0.0935\n0.0407\n0.5941\n0.1363\n0.7706\n139\n467\n881\n6073\nBMJ-WSM3-God-MYJ\n0.2273\n0.0777\n0.871\n0.3465\n0.6021\n744\n1126\n1403\n4287\n0.0992\n0.0404\n0.7049\n0.1539\n0.7816\n157\n562\n863\n5978\nBMJ-WSM3-God-YSU\n0.2239\n0.0691\n0.9171\n0.3507\n0.6176\n753\n1216\n1394\n4197\n0.1022\n0.0373\n0.8294\n0.1696\n0.7955\n173\n673\n847\n5867\nBMJ-WSM5-Duh-MYJ\n0.1788\n0.0634\n0.5817\n0.2399\n0.5877\n515\n734\n1632\n4679\n0.0944\n0.0482\n0.4892\n0.1284\n0.7375\n131\n368\n889\n6172\nBMJ-WSM5-Duh-YSU\n0.2041\n0.0796\n0.6544\n0.2804\n0.5715\n602\n803\n1545\n4610\n0.0966\n0.0427\n0.6137\n0.1422\n0.7684\n145\n481\n875\n6059\nBMJ-WSM5-God-MYJ\n0.2169\n0.0787\n0.7667\n0.3149\n0.5893\n676\n970\n1471\n4443\n0.0908\n0.037\n0.6127\n0.1343\n0.7808\n137\n488\n883\n6052\nBMJ-WSM5-God-YSU\n0.2278\n0.0779\n0.8728\n0.3475\n0.6019\n746\n1128\n1401\n4285\n0.0947\n0.031\n0.802\n0.1559\n0.8056\n159\n659\n861\n5881\nBMJ-WSM6-Duh-MYJ\n0.1712\n0.0601\n0.5515\n0.2268\n0.5887\n487\n697\n1660\n4716\n0.0834\n0.0379\n0.4775\n0.1137\n0.7618\n116\n371\n904\n6169\nBMJ-WSM6-Duh-YSU\n0.1971\n0.069\n0.68\n0.2767\n0.5932\n594\n866\n1553\n4547\n0.0992\n0.0432\n0.652\n0.149\n0.7714\n152\n513\n868\n6027\nBMJ-WSM6-God-MYJ\n0.2191\n0.069\n0.8714\n0.3363\n0.6141\n722\n1149\n1425\n4264\n0.1\n0.0385\n0.7578\n0.1598\n0.7891\n163\n610\n857\n5930\nBMJ-WSM6-God-YSU\n0.215\n0.0632\n0.885\n0.3335\n0.6232\n716\n1184\n1431\n4229\n0.0824\n0.0185\n0.8039\n0.1373\n0.8293\n140\n680\n880\n5860\nKF-Lin-Duh-MYJ\n0.2144\n0.0871\n0.6782\n0.2962\n0.5632\n636\n820\n1511\n4593\n0.1162\n0.0632\n0.601\n0.1667\n0.7227\n170\n443\n850\n6097\nKF-Lin-Duh-YSU\n0.2063\n0.0825\n0.6502\n0.2823\n0.5659\n606\n790\n1541\n4623\n0.1069\n0.052\n0.6343\n0.1578\n0.7512\n161\n486\n859\n6054\nKF-Lin-God-MYJ\n0.2399\n0.0805\n0.9693\n0.381\n0.6069\n818\n1263\n1329\n4150\n0.1392\n0.0702\n0.9333\n0.2363\n0.7468\n241\n711\n779\n5829\nKF-Lin-God-YSU\n0.2427\n0.0831\n0.9725\n0.3852\n0.6039\n827\n1261\n1320\n4152\n0.1309\n0.0605\n0.9647\n0.2275\n0.7642\n232\n752\n788\n5788\nKF-WSM3-Duh-MYJ\n0.2377\n0.0889\n0.8677\n0.3586\n0.5867\n770\n1093\n1377\n4320\n0.1242\n0.0613\n0.7922\n0.198\n0.75\n202\n606\n818\n5934\nKF-WSM3-Duh-YSU\n0.2618\n0.1129\n0.8812\n0.3903\n0.5571\n838\n1054\n1309\n4359\n0.1675\n0.0983\n0.948\n0.2794\n0.7053\n285\n682\n735\n5858\nKF-WSM3-God-MYJ\n0.2596\n0.0844\n1.1495\n0.4429\n0.6147\n951\n1517\n1196\n3896\n0.1334\n0.0538\n1.2235\n0.2618\n0.7861\n267\n981\n753\n5559\nKF-WSM3-God-YSU\n0.2817\n0.1073\n1.1593\n0.4746\n0.5906\n1019\n1470\n1128\n3943\n0.1631\n0.081\n1.3216\n0.3255\n0.7537\n332\n1016\n688\n5524\nKF-WSM5-Duh-MYJ\n0.2508\n0.1005\n0.8882\n0.3787\n0.5737\n813\n1094\n1334\n4319\n0.1333\n0.0694\n0.8167\n0.2137\n0.7383\n218\n615\n802\n5925\nKF-WSM5-Duh-YSU\n0.2671\n0.1193\n0.8738\n0.395\n0.548\n848\n1028\n1299\n4385\n0.158\n0.0902\n0.9118\n0.2608\n0.714\n266\n664\n754\n5876\nKF-WSM5-God-MYJ\n0.2723\n0.0967\n1.1635\n0.463\n0.6021\n994\n1504\n1153\n3909\n0.1354\n0.0571\n1.1863\n0.2608\n0.7802\n266\n944\n754\n5596\nKF-WSM5-God-YSU\n0.2652\n0.0932\n1.1178\n0.4439\n0.6029\n953\n1447\n1194\n3966\n0.1507\n0.0709\n1.2382\n0.2931\n0.7633\n299\n964\n721\n5576\nKF-WSM6-Duh-MYJ\n0.2545\n0.0952\n0.9767\n0.401\n0.5894\n861\n1236\n1286\n4177\n0.1237\n0.0546\n0.9324\n0.2127\n0.7718\n217\n734\n803\n5806\nKF-WSM6-Duh-YSU\n0.2615\n0.1084\n0.9208\n0.3982\n0.5675\n855\n1122\n1292\n4291\n0.156\n0.0841\n1.0127\n0.2716\n0.7318\n277\n756\n743\n5784\nKF-WSM6-God-MYJ\n0.2693\n0.0883\n1.2259\n0.4723\n0.6147\n1014\n1618\n1133\n3795\n0.1398\n0.0571\n1.3265\n0.2853\n0.7849\n291\n1062\n729\n5478\nKF-WSM6-God-YSU\n0.2853\n0.1072\n1.2054\n0.4895\n0.5939\n1051\n1537\n1096\n3876\n0.1513\n0.0661\n1.4245\n0.3186\n0.7763\n325\n1128\n695\n5412\n\n\n## 3.2. Skill Score Validation\n\nThe charts for the skill scores at the two thresholds in Figures5 and 6 show that the TS value at the 24 h forecast range is about 0.2 to 0.27 for >25 mm/24 h and 0.1 to 0.2 for >50 mm/24 h. At 48 h, the TS is around ∼0.18 to 0.3 and ∼0.1 to 0.19 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h. At 72 h, the TS is around 0.15 to 0.25 and ∼0.08 to 0.15 corresponding to two thresholds >25 mm/24 h and >50 mm/24 h.Figure 5\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 25 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)Figure 6\nSkill scores calculated for northern Vietnam for daily accumulation thresholds over 50 mm for (a) 24 h, (b) 48 h, and (c) 72 h forecast ranges. The dark grey bar is for POD, light grey bar is for FAR, blue dotted line is for TS, and red dotted line is for ETS. In each chart, the left vertical axis (0–0.9) is for POD and FAR, while the right vertical axis (0–0.35) is for TS and ETS values. The number of samples is 8064 for an individual forecast.\n(a)\n(b)\n(c)The probability of detection decreases and the false alarm rate clearly increases with forecast ranges and the validating thresholds. In addition, when the threshold increases, the difference between the TS and the ETS decreases which means that the amount of Hitsrandom is too small or the cause of very small hit rate (Hitsrandom rate decreases by 90% when changing the threshold from >25 mm/24 h to >50 mm/24 h).Specific comparisons between the combinations of the BMJ or KF scheme show that the KF scheme model’s skills in heavy rain forecast in the northern region of Vietnam are better than BMJ scheme model’s skills. The average TS with the KF scheme can be about 15–25% larger than that using the BMJ scheme. If the difference of skills in a regional model is insignificant when changing the physical parameterization schemes, the lateral boundary conditions (from global forecasts) will greatly affect the quality of the dynamical downscaling forecasts after 24 h integration. However, here the skill difference when combining the two different cumulus schemes in the longer forecast range (such as 72 h) shows the importance of convection simulation capability contributing to the forecasting quality of the model. Detailed evaluation of the combination with the radiation physical schemes of KF or BMJ does not show any difference compared to changing the boundary layer schemes when looking at the skill scores TS or ETS.The combinations with YSU boundary layer schemes have better skills compared to those with MYJ schemes. In addition, when changing the complexity of the cloud microphysics schemes, the more complex the microphysical processing simulation, the better the TS and ETS (at 24, 48, and 72 h forecast ranges and two validating thresholds; see Figure7 for comparison of the change in TSs and ETSs with the cloud microphysics scheme).Figure 7\nBrief comparison of TSs and ETSs for illustration of sensitivities with microphysics schemes.For the skill comparisons of different event types (I, II, III, and IV), Figure8 shows the TSs at thresholds over 25 mm/24 h and over 50 mm/24 h for 24 h and 48 h forecast ranges. For type I, which is associated with the activity of ITCZ and low-pressure trough over the Bac Bo area, the KF scheme proved its forecast skills in almost forecast ranges and thresholds mentioned in this research. However, with the rain caused by tropical cyclone in type II, the difference between KF and BMJ schemes within 24 h was smaller than that in type I. In 48 h and 72 h, the BMJ scheme showed more skilled forecast with the skill score for threshold 25 mm ranging from 0.25 to 0.35 of the BMJ scheme, compared with 0.2 to 0.3 of the KF scheme, and the skill score for threshold 50 mm ranging from 0.2 to 0.3 of the BMJ scheme, compared with 0.2 to 0.25 of the KF scheme. In type II, both KF and BMJ schemes combined with the simple cloud microphysics Lin scheme showed lowest skill score. For type III, which is associated with the activity of cold surge and its role in squeezing the low-pressure trough from the north towards Bac Bo, the KF scheme was only skillful in threshold 25 mm in 24 h. Particularly in type IV, which contains heavy rain events caused by a complex combination of situations resulting in a trough in Bac Bo, the KF scheme still showed skilled forecast compared to very low forecast (no skill with threshold over 50 mm/24 h and 25 mm/24 h) of BMJ scheme experiments. More details of TSs for different types are listed in Tables 7 and 8.Figure 8\nTSs for different types of heavy rainfall events in northern Vietnam for daily accumulation thresholds over (a) 25 mm and 50 mm (b) at 24 h forecast ranges and over 25 mm (c) and 50 mm (d) at 48 h forecast ranges. The right vertical axis is from 0 to 0.4.\n(a)\n(b)\n(c)\n(d)Table 7\nTSs for different types of main heavy rainfall, at the 24 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.2546\n0.1309\n0.15\n0.0682\n0.124\n0.0791\n0.0769\n0\nBMJ-Lin-Duh-YSU\n0.2625\n0.1019\n0.2346\n0.0706\n0.1244\n0.0692\n0.16\n0\nBMJ-Lin-God-MYJ\n0.2644\n0.1638\n0.2235\n0.1011\n0.139\n0.1064\n0.0882\n0\nBMJ-Lin-God-YSU\n0.2761\n0.1519\n0.1939\n0.093\n0.1369\n0.087\n0.1562\n0.0333\nBMJ-WSM3-Duh-MYJ\n0.2503\n0.1237\n0.1818\n0.09\n0.1279\n0.0621\n0.129\n0\nBMJ-WSM3-Duh-YSU\n0.2745\n0.1179\n0.2439\n0.0652\n0.1379\n0.0699\n0.1471\n0\nBMJ-WSM3-God-MYJ\n0.2834\n0.1651\n0.21\n0.09\n0.1556\n0.1104\n0.1136\n0\nBMJ-WSM3-God-YSU\n0.2943\n0.1505\n0.202\n0.0745\n0.167\n0.0798\n0.1304\n0.0312\nBMJ-WSM5-Duh-MYJ\n0.2618\n0.1349\n0.1977\n0.0707\n0.1429\n0.0563\n0.1515\n0\nBMJ-WSM5-Duh-YSU\n0.263\n0.1351\n0.2073\n0.0652\n0.1603\n0.0789\n0.1389\n0\nBMJ-WSM5-God-MYJ\n0.3032\n0.1376\n0.1978\n0.0714\n0.1565\n0.0496\n0.1515\n0.0345\nBMJ-WSM5-God-YSU\n0.3028\n0.1525\n0.1939\n0.0737\n0.1677\n0.0696\n0.15\n0.0312\nBMJ-WSM6-Duh-MYJ\n0.2571\n0.133\n0.1304\n0.0926\n0.1403\n0.0872\n0.0732\n0\nBMJ-WSM6-Duh-YSU\n0.2643\n0.1281\n0.1828\n0.06\n0.1714\n0.0719\n0.1212\n0\nBMJ-WSM6-God-MYJ\n0.2991\n0.1781\n0.2574\n0.0784\n0.1706\n0.1391\n0.15\n0.0256\nBMJ-WSM6-God-YSU\n0.3043\n0.1595\n0.1944\n0.0652\n0.1916\n0.1104\n0.1429\n0\nKF-Lin-Duh-MYJ\n0.2881\n0.1673\n0.2136\n0.1449\n0.1844\n0.0683\n0.1026\n0.1818\nKF-Lin-Duh-YSU\n0.3125\n0.1951\n0.2474\n0.1667\n0.1778\n0.0698\n0.06\n0.1892\nKF-Lin-God-MYJ\n0.2917\n0.1914\n0.2255\n0.1558\n0.1725\n0.1237\n0.0889\n0.1471\nKF-Lin-God-YSU\n0.3129\n0.183\n0.2653\n0.1769\n0.1839\n0.1162\n0.093\n0.1316\nKF-WSM3-Duh-MYJ\n0.2872\n0.1776\n0.2212\n0.1655\n0.1893\n0.0753\n0.1224\n0.1667\nKF-WSM3-Duh-YSU\n0.309\n0.1881\n0.2551\n0.219\n0.1858\n0.1176\n0.1111\n0.2059\nKF-WSM3-God-MYJ\n0.3264\n0.1924\n0.2526\n0.1656\n0.2057\n0.0909\n0.0889\n0.1579\nKF-WSM3-God-YSU\n0.3188\n0.1841\n0.3069\n0.2014\n0.2027\n0.1185\n0.098\n0.1795\nKF-WSM5-Duh-MYJ\n0.3062\n0.1836\n0.25\n0.1655\n0.2048\n0.0688\n0.0851\n0.1795\nKF-WSM5-Duh-YSU\n0.3116\n0.1892\n0.2653\n0.1838\n0.1823\n0.1111\n0.08\n0.1818\nKF-WSM5-God-MYJ\n0.3141\n0.1996\n0.25\n0.1772\n0.2047\n0.1122\n0.1064\n0.1622\nKF-WSM5-God-YSU\n0.3317\n0.2025\n0.2642\n0.1875\n0.2063\n0.1085\n0.1228\n0.1579\nKF-WSM6-Duh-MYJ\n0.3063\n0.1823\n0.2233\n0.1812\n0.1963\n0.0952\n0.0833\n0.1538\nKF-WSM6-Duh-YSU\n0.3339\n0.1927\n0.2476\n0.1898\n0.2244\n0.1179\n0.1071\n0.2812\nKF-WSM6-God-MYJ\n0.3138\n0.1781\n0.2268\n0.1465\n0.2207\n0.1268\n0.1429\n0.125\nKF-WSM6-God-YSU\n0.3323\n0.1793\n0.2843\n0.2\n0.2078\n0.1255\n0.1538\n0.1429Table 8\nTSs for different types of main heavy rainfall, at the 48 h forecast range for thresholds >25 mm/24 h and >50 mm/24 h, for the period 2012–2016.\n>25 mm/24 h\n>50 mm/24 h\nType I\nType II\nType III\nType IV\nType I\nType II\nType III\nType IV\nBMJ-Lin-Duh-MYJ\n0.1629\n0.2426\n0.0854\n0.0922\n0.0768\n0.1533\n0.0526\n0.0333\nBMJ-Lin-Duh-YSU\n0.1755\n0.2271\n0.0909\n0.1438\n0.0971\n0.1559\n0.0833\n0.0164\nBMJ-Lin-God-MYJ\n0.2276\n0.3104\n0.075\n0.0798\n0.1139\n0.1631\n0.0333\n0.0303\nBMJ-Lin-God-YSU\n0.2482\n0.2723\n0.1429\n0.12\n0.1405\n0.1543\n0.0385\n0.0167\nBMJ-WSM3-Duh-MYJ\n0.2235\n0.3055\n0.0833\n0.1311\n0.1147\n0.2848\n0.0571\n0\nBMJ-WSM3-Duh-YSU\n0.2365\n0.3419\n0.1538\n0.1617\n0.1136\n0.3072\n0.0556\n0.0116\nBMJ-WSM3-God-MYJ\n0.2583\n0.3448\n0.0979\n0.1029\n0.1386\n0.2466\n0.0182\n0\nBMJ-WSM3-God-YSU\n0.2587\n0.3338\n0.1176\n0.1272\n0.1541\n0.2125\n0.02\n0\nBMJ-WSM5-Duh-MYJ\n0.2301\n0.3206\n0.0928\n0.1228\n0.1287\n0.2706\n0.0741\n0.0115\nBMJ-WSM5-Duh-YSU\n0.243\n0.3157\n0.1373\n0.172\n0.1397\n0.2813\n0.0312\n0.0241\nBMJ-WSM5-God-MYJ\n0.2594\n0.344\n0.1327\n0.1337\n0.1479\n0.2928\n0.0286\n0\nBMJ-WSM5-God-YSU\n0.2588\n0.3329\n0.1797\n0.1364\n0.1383\n0.2918\n0\n0.0115\nBMJ-WSM6-Duh-MYJ\n0.217\n0.304\n0.1418\n0.1027\n0.1214\n0.2212\n0.0784\n0.0235\nBMJ-WSM6-Duh-YSU\n0.2331\n0.3216\n0.1368\n0.1587\n0.1348\n0.2968\n0.0714\n0.0103\nBMJ-WSM6-God-MYJ\n0.2522\n0.3055\n0.1474\n0.0955\n0.132\n0.2314\n0.0541\n0.0215\nBMJ-WSM6-God-YSU\n0.2739\n0.3707\n0.2\n0.1026\n0.1333\n0.2658\n0.0484\n0.0319\nKF-Lin-Duh-MYJ\n0.2259\n0.2093\n0.0811\n0.225\n0.1331\n0.1208\n0.027\n0.1899\nKF-Lin-Duh-YSU\n0.2714\n0.172\n0.0957\n0.2199\n0.1347\n0.1017\n0.0667\n0.1395\nKF-Lin-God-MYJ\n0.268\n0.2537\n0.1043\n0.2771\n0.1267\n0.1545\n0.0385\n0.1237\nKF-Lin-God-YSU\n0.3125\n0.2384\n0.1058\n0.2788\n0.1704\n0.1608\n0.0488\n0.1327\nKF-WSM3-Duh-MYJ\n0.2689\n0.2805\n0.1298\n0.2783\n0.156\n0.241\n0.0577\n0.1327\nKF-WSM3-Duh-YSU\n0.3066\n0.2739\n0.069\n0.285\n0.1971\n0.2068\n0.0444\n0.2088\nKF-WSM3-God-MYJ\n0.2945\n0.2976\n0.1286\n0.2595\n0.1651\n0.2451\n0.0526\n0.1111\nKF-WSM3-God-YSU\n0.3202\n0.2956\n0.1407\n0.2822\n0.1872\n0.2136\n0.0923\n0.1569\nKF-WSM5-Duh-MYJ\n0.2924\n0.2921\n0.1176\n0.2546\n0.1586\n0.238\n0.0727\n0.1765\nKF-WSM5-Duh-YSU\n0.3217\n0.2847\n0.0976\n0.26\n0.1995\n0.2403\n0.0755\n0.1753\nKF-WSM5-God-MYJ\n0.2946\n0.2953\n0.1192\n0.2672\n0.1499\n0.25\n0.038\n0.1293\nKF-WSM5-God-YSU\n0.3265\n0.2768\n0.1361\n0.2881\n0.201\n0.2344\n0.0959\n0.1404\nKF-WSM6-Duh-MYJ\n0.2922\n0.2874\n0.1389\n0.2597\n0.1874\n0.25\n0.0685\n0.1667\nKF-WSM6-Duh-YSU\n0.3291\n0.2706\n0.1311\n0.2995\n0.208\n0.2378\n0.0893\n0.2083\nKF-WSM6-God-MYJ\n0.3002\n0.3115\n0.1465\n0.2556\n0.1675\n0.2537\n0.0435\n0.1333\nKF-WSM6-God-YSU\n0.3426\n0.2798\n0.1595\n0.2888\n0.2091\n0.2565\n0.0674\n0.177\n\n\n## 4. Conclusions\n\nFor the purpose of investigating the effects of physical schemes in the WRF-ARW model on the operational heavy rainfall forecast for the Bac Bo area, 32 different model configurations have been established by switching two typical cumulus parameterization schemes (BMJ and KF), the cloud microphysics schemes from simple (Lin) to complex (WSM with 3/5/6-layer closure assumptions), and boundary layer (YSU and MYJ) and shortwave radiation (Dudhia and Goddard) schemes. The 72 experiments of widespread heavy rainfall occurring in the Bac Bo area used boundaries from the GFS model and had the highest horizontal resolution of 5 km × 5 km.The model verification with local observation data illustrated the limited capabilities in heavy rainfall forecast for the northern part of Vietnam. On average, for the threshold over 25 mm/24 h, the TSs are from 0.2 to 0.25, 0.2 to 0.3, and 0.2 to 0.25 for 24 h, 48 h, and 72 h forecast ranges, respectively. For the threshold over 50 mm/24 h, TSs are 0.1–0.2 for 24 h and 48 h forecast ranges and about 0.1–0.15 for the 72 h forecast range. At above the 100 mm/24 h threshold, a very low skill value (below 0.1) is validated for most forecast ranges.The change of the microcloud physics from simple to complex closure assumptions shows that complex schemes give very positive results for both the BMJ and KF schemes. The model configurations with the KF scheme showed higher skills compared to BMJ scheme configurations, and these higher skill scores (TS and POD) mainly come from a higher hit rate (H) and a lower missed rate (M) but also a higher false alarm rate (F). More preliminary assessment for the KF scheme configurations showed the most sensitivity of boundary layer schemes compared to microphysics or shortwave radiation schemes and some initial comments that boundary layer interaction during the application of the KF scheme is an important factor to find the appropriate parameters for heavy rainfall forecast over the Bac Bo area in the WRF-ARW model.In terms of sample size of each category, the first two types are comparable because of the similar sample size. The other two types, however, are limited in sample size and need to be further analyzed in the subsequent research. A detailed assessment related to the origin of the mechanisms causing heavy rain illustrates that the KF scheme showed more skilled forecast with the BMJ scheme during trough- or ITCZ-related heavy rain events, whereas it was less skilled in the events caused by tropical cyclones. With the events caused by the cold surge and a combination of different patterns, the skill of the BMJ scheme was quite low. More verification with the last two types needs to be further investigated in the subsequent research because of the limited sample size studied.\n\n\n\n---\n\n*Source: 1010858-2019-08-18.xml*"},"year":{"kind":"string","value":"2019"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":58480,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NzM5NjUxMSwianRpIjoiMGJhMTc2YjktOTgzNi00NGI4LTk4MzYtMzAzMzI4MWE1Y2VjIiwic3ViIjoiL2RhdGFzZXRzL21rdXJtYW4vaGluZGF3aS1qb3VybmFscy0yMDA3LTIwMjMiLCJleHAiOjE3ODc0MDAxMTEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.UolvlvYcQ2tML5Le-vkAzvO2M7usuy-y56WdY-wgzYnpaPlozJDrYQq2urv5XMWq6P37lbniVA9veZgas_YSDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":58480,"numBytesParquet":1887927271}]},"discussionsStats":{"closed":0,"open":2,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
text string | filename string | file_size int64 | title string | authors string | journal string | category string | publisher string | license string | license_url string | doi string | source_file string | content string | year string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
# Mitochondrial DNA Efflux Maintained in Gingival Fibroblasts of Patients with Periodontitis through ROS/mPTP Pathway
**Authors:** Jia Liu; Yanfeng Wang; Qiao Shi; Xiaoxuan Wang; Peihui Zou; Ming Zheng; Qingxian Luan
**Journal:** Oxidative Medicine and Cellular Longevity
(2022)
**Publisher:** Hindawi
**License:**... | 1000213-2022-06-08_1000213-2022-06-08.md | 62,217 | Mitochondrial DNA Efflux Maintained in Gingival Fibroblasts of Patients with Periodontitis through ROS/mPTP Pathway | Jia Liu; Yanfeng Wang; Qiao Shi; Xiaoxuan Wang; Peihui Zou; Ming Zheng; Qingxian Luan | Oxidative Medicine and Cellular Longevity
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1000213 | 1000213-2022-06-08.xml | ---
## Abstract
Mitochondria have their own mitochondrial DNA (mtDNA). Aberrant mtDNA is associated with inflammatory diseases. mtDNA is believed to induce inflammation via the abnormal mtDNA release. Periodontitis is an infectious, oral inflammatory disease. Human gingival fibroblasts (HGFs) from patients with chron... | 2022 |
# Research on the Characteristics of Food Impaction with Tight Proximal Contacts Based on Deep Learning
**Authors:** Yitong Cheng; Zhijiang Wang; Yue Shi; Qiaoling Guo; Qian Li; Rui Chai; Feng Wu
**Journal:** Computational and Mathematical Methods in Medicine
(2021)
**Publisher:** Hindawi
**License:** http://crea... | 1000820-2021-11-05_1000820-2021-11-05.md | 44,134 | Research on the Characteristics of Food Impaction with Tight Proximal Contacts Based on Deep Learning | Yitong Cheng; Zhijiang Wang; Yue Shi; Qiaoling Guo; Qian Li; Rui Chai; Feng Wu | Computational and Mathematical Methods in Medicine
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1000820 | 1000820-2021-11-05.xml | ---
## Abstract
Objective. Based on deep learning, the characteristics of food impaction with tight proximal contacts were studied to guide the subsequent clinical treatment of occlusal adjustment. At the same time, digital model building, software measurement, and statistical correlation analysis were used to explor... | 2021 |
# Influence of Heat-Treated and Vibratory-Assisted Weld Joints on the Mechanical Properties of 304L SS Material
**Authors:** Muvvala Chinnam Naidu; K. T. Balaram Padal; Girma Eshete
**Journal:** Journal of Nanomaterials
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:... | 1000859-2022-09-26_1000859-2022-09-26.md | 35,021 | Influence of Heat-Treated and Vibratory-Assisted Weld Joints on the Mechanical Properties of 304L SS Material | Muvvala Chinnam Naidu; K. T. Balaram Padal; Girma Eshete | Journal of Nanomaterials
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1000859 | 1000859-2022-09-26.xml | ---
## Abstract
Tensile and impact strengths of 304L SS stainless steel weldment prepared at different levels of heat treatments and with vibratory assistance were studied and compared with the conventional process of welding. The results reveal that the microstructures of weld joints after heat treatment and vibrato... | 2022 |
# A Fast Algorithm for Determining the Optimal Navigation Star for Responsive Launch Vehicles
**Authors:** Yi Zhao; Hongbo Zhang; Pengfei Li; Guojian Tang
**Journal:** International Journal of Aerospace Engineering
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10... | 1000865-2022-04-19_1000865-2022-04-19.md | 82,670 | A Fast Algorithm for Determining the Optimal Navigation Star for Responsive Launch Vehicles | Yi Zhao; Hongbo Zhang; Pengfei Li; Guojian Tang | International Journal of Aerospace Engineering
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1000865 | 1000865-2022-04-19.xml | ---
## Abstract
The platform inertial-stellar composite guidance is a composite guidance method supplemented by stellar correction on the basis of inertial navigation, which can effectively improve the accuracy of responsive launch vehicles. In order to solve the problem of rapid determining the optimal navigation st... | 2022 |
# Therapeutic Effects of the Proximal Femoral Nail for the Treatment of Unstable Intertrochanteric Fractures
**Authors:** Yuwei Cai; Wenjun Zhu; Nan Wang; Zhongxiang Yu; Yu Chen; Shengming Xu; Juntao Feng
**Journal:** Evidence-Based Complementary and Alternative Medicine
(2022)
**Publisher:** Hindawi
**License:**... | 1001354-2022-09-02_1001354-2022-09-02.md | 32,503 | Therapeutic Effects of the Proximal Femoral Nail for the Treatment of Unstable Intertrochanteric Fractures | Yuwei Cai; Wenjun Zhu; Nan Wang; Zhongxiang Yu; Yu Chen; Shengming Xu; Juntao Feng | Evidence-Based Complementary and Alternative Medicine
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1001354 | 1001354-2022-09-02.xml | ---
## Abstract
Objective. The aim of this study was to analyze the clinical effect of the proximal femoral nail on elderly patients with unstable intertrochanteric fracture and the effect of the proximal femoral nail on serum levels of matrix metalloproteinases (MMPs) and osteoprotegerin (OPG). Methods. The elderly ... | 2022 |
# ALKBH5 Is Lowly Expressed in Esophageal Squamous Cell Carcinoma and Inhibits the Malignant Proliferation and Invasion of Tumor Cells
**Authors:** Jinqiu Li; Hongqiang Liu; Shanglin Dong; Yunbo Zhang; Xiao Li; Jing Wang
**Journal:** Computational and Mathematical Methods in Medicine
(2021)
**Publisher:** Hindawi
... | 1001446-2021-11-28_1001446-2021-11-28.md | 24,324 | ALKBH5 Is Lowly Expressed in Esophageal Squamous Cell Carcinoma and Inhibits the Malignant Proliferation and Invasion of Tumor Cells | Jinqiu Li; Hongqiang Liu; Shanglin Dong; Yunbo Zhang; Xiao Li; Jing Wang | Computational and Mathematical Methods in Medicine
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1001446 | 1001446-2021-11-28.xml | ---
## Abstract
Background. Modification of N6-methyladenosine (m6A) and RNA m6A regulatory factors is required in cancer advancement. The contribution of m6A and its alteration in esophageal squamous cell carcinoma (ESCC) is still unclear. Results. ALKBH5 was lowly expressed in ESCC tissues, which the total m6A leve... | 2021 |
# Deconstruction of the Prevention of Knee Osteoarthritis by Swimming Based on Data Mining Technology
**Authors:** Jianxia Yin; Qing Li; Yao Song
**Journal:** BioMed Research International
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1001686
---
... | 1001686-2022-08-16_1001686-2022-08-16.md | 70,889 | Deconstruction of the Prevention of Knee Osteoarthritis by Swimming Based on Data Mining Technology | Jianxia Yin; Qing Li; Yao Song | BioMed Research International
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1001686 | 1001686-2022-08-16.xml | ---
## Abstract
With the continuous development of big data and the continuous improvement of people’s living standards, increasingly attention is paid to physical health. Swimming in this sport is effective in preventing the occurrence of arthritis. This paper analyzes the prevention and exploration of arthritis and... | 2022 |
# Honokiol Provides Cardioprotection from Myocardial Ischemia/Reperfusion Injury (MI/RI) by Inhibiting Mitochondrial Apoptosis via the PI3K/AKT Signaling Pathway
**Authors:** Linhua Lv; Qiuhuan Kong; Zhiying Li; Ying Zhang; Bijiao Chen; Lihua Lv; Yubi Zhang
**Journal:** Cardiovascular Therapeutics
(2022)
**Publish... | 1001692-2022-03-27_1001692-2022-03-27.md | 38,447 | Honokiol Provides Cardioprotection from Myocardial Ischemia/Reperfusion Injury (MI/RI) by Inhibiting Mitochondrial Apoptosis via the PI3K/AKT Signaling Pathway | Linhua Lv; Qiuhuan Kong; Zhiying Li; Ying Zhang; Bijiao Chen; Lihua Lv; Yubi Zhang | Cardiovascular Therapeutics
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1001692 | 1001692-2022-03-27.xml | ---
## Abstract
Background. Myocardial injury refers to a major complication that occurs in myocardial ischemia/reperfusion injury (MI/RI). Honokiol is a well-recognized active compound extracted from the traditional Chinese herb known as Magnolia officinalis and is utilized in treating different vascular diseases. T... | 2022 |
# The Correlation between the Use of the Proton Pump Inhibitor and the Clinical Efficacy of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer
**Authors:** Da-Hai Hu; Wan-Ching Wong; Jia-Xin Zhou; Ji Luo; Song-Wang Cai; Hong Zhou; Hui Tang
**Journal:** Journal of Oncology
(2022)
**Publisher:** Hindawi
**L... | 1001796-2022-07-09_1001796-2022-07-09.md | 43,403 | The Correlation between the Use of the Proton Pump Inhibitor and the Clinical Efficacy of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer | Da-Hai Hu; Wan-Ching Wong; Jia-Xin Zhou; Ji Luo; Song-Wang Cai; Hong Zhou; Hui Tang | Journal of Oncology
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1001796 | 1001796-2022-07-09.xml | ---
## Abstract
Background. To determine if the use of the Proton Pump Inhibitors (PPI) impacts the clinical efficacy of Immune Checkpoint Inhibitors (ICIs) in Non-Small Cell Lung Cancer (NSCLC), a meta-analysis was conducted. Method. Eleven studies from PubMed, EMBASE, Cochrane Library, Web of Science, and other dat... | 2022 |
# Core-EP-Nilpotent Decomposition and Its Applications
**Authors:** Yang Zhang; Zongyang Jiang
**Journal:** Journal of Mathematics
(2023)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2023/1001901
---
## Abstract
We give the illustration of a new decompositi... | 1001901-2023-01-23_1001901-2023-01-23.md | 14,245 | Core-EP-Nilpotent Decomposition and Its Applications | Yang Zhang; Zongyang Jiang | Journal of Mathematics
(2023) | Mathematical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1001901 | 1001901-2023-01-23.xml | ---
## Abstract
We give the illustration of a new decomposition: the core-EP-nilpotent decomposition, which is on the basis of the core-EP decomposition and EP-nilpotent decomposition for some square matrices in this thesis. According to the new decomposition, we show the definitions and characteristics of two new or... | 2023 |
# Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching
**Authors:** Nan Liu
**Journal:** Mathematical Problems in Engineering
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1002105
---
## ... | 1002105-2022-08-18_1002105-2022-08-18.md | 80,585 | Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching | Nan Liu | Mathematical Problems in Engineering
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1002105 | 1002105-2022-08-18.xml | ---
## Abstract
As one of the hotspots in music information extraction research, music recognition has received extensive attention from scholars in recent years. Most of the current research methods are based on traditional signal processing methods, and there is still a lot of room for improvement in recognition ac... | 2022 |
# Accurate Diagnosis and Treatment of Painful Temporomandibular Disorders: A Literature Review Supplemented by Own Clinical Experience
**Authors:** Adam Andrzej Garstka; Lidia Kozowska; Konrad Kijak; Monika Brzózka; Helena Gronwald; Piotr Skomro; Danuta Lietz-Kijak
**Journal:** Pain Research and Management
(2023)
... | 1002235-2023-01-31_1002235-2023-01-31.md | 68,438 | Accurate Diagnosis and Treatment of Painful Temporomandibular Disorders: A Literature Review Supplemented by Own Clinical Experience | Adam Andrzej Garstka; Lidia Kozowska; Konrad Kijak; Monika Brzózka; Helena Gronwald; Piotr Skomro; Danuta Lietz-Kijak | Pain Research and Management
(2023) | Other | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1002235 | 1002235-2023-01-31.xml | ---
## Abstract
Introduction. Temporomandibular disorders (TMD) is a multifactorial group of musculoskeletal disorders often with combined etiologies that demand different treatment plans. While pain is the most common reason why patients decide to seek help, TMD is not always painful. Pain is often described by pati... | 2023 |
# Analysis of Persuasive Design Mechanism Based on Unconscious Calculation
**Authors:** Hongtao Zheng; Shuo Li
**Journal:** Scientific Programming
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1002517
---
## Abstract
In order to make users’ behav... | 1002517-2022-02-22_1002517-2022-02-22.md | 85,121 | Analysis of Persuasive Design Mechanism Based on Unconscious Calculation | Hongtao Zheng; Shuo Li | Scientific Programming
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1002517 | 1002517-2022-02-22.xml | ---
## Abstract
In order to make users’ behavior more standardized, a persuasive design mechanism analysis method based on unconscious calculation is proposed. Taking the concept of persuasion and the goal of persuading function as the theoretical basis, the user behavior data is obtained in the form of correlation c... | 2022 |
# The Modulation of Interferon Regulatory Factor-1 via Caspase-1-Mediated Alveolar Macrophage Pyroptosis in Ventilator-Induced Lung Injury
**Authors:** Minhui Dai; Qian Li; Pinhua Pan
**Journal:** Mediators of Inflammation
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**D... | 1002582-2022-04-15_1002582-2022-04-15.md | 45,750 | The Modulation of Interferon Regulatory Factor-1 via Caspase-1-Mediated Alveolar Macrophage Pyroptosis in Ventilator-Induced Lung Injury | Minhui Dai; Qian Li; Pinhua Pan | Mediators of Inflammation
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1002582 | 1002582-2022-04-15.xml | ---
## Abstract
Background. To examine the role of interferon regulatory factor-1 (IRF-1) and to explore the potential molecular mechanism in ventilator-induced lung injury. Methods. Wild-type C57BL/6 mice and IRF-1 gene knockout mice/caspase-1 knockout mice were mechanically ventilated with a high tidal volume to es... | 2022 |
# Genetic Relationship and Evolution Analysis among Malus Mill Plant Populations Based on SCoT Molecular Markers
**Authors:** Yuan Yao
**Journal:** Computational and Mathematical Methods in Medicine
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/10026... | 1002624-2022-06-17_1002624-2022-06-17.md | 64,659 | Genetic Relationship and Evolution Analysis among Malus Mill Plant Populations Based on SCoT Molecular Markers | Yuan Yao | Computational and Mathematical Methods in Medicine
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1002624 | 1002624-2022-06-17.xml | ---
## Abstract
Malus Mill genotype is highly heterozygous, and many theoretical problems such as genetic relationship and evolution process among germplasm are difficult to be solved by traditional analysis methods. The development of SCoT(start codon targeted polymorphism) molecular markers suitable for apples is o... | 2022 |
# A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning
**Authors:** Omar Faruk; Eshan Ahmed; Sakil Ahmed; Anika Tabassum; Tahia Tazin; Sami Bourouis; Mohammad Monirujjaman Khan
**Journal:** Journal of Healthcare Engineering
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.or... | 1002799-2021-11-25_1002799-2021-11-25.md | 44,089 | A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning | Omar Faruk; Eshan Ahmed; Sakil Ahmed; Anika Tabassum; Tahia Tazin; Sami Bourouis; Mohammad Monirujjaman Khan | Journal of Healthcare Engineering
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1002799 | 1002799-2021-11-25.xml | ---
## Abstract
Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, includingtuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosi... | 2021 |
# The Analysis on the Current Situation of the Utilization Mode of Microalgal Biomass Materials
**Authors:** Lina Zhang; Lianfeng Wang; Huizhong Nie; Changbin Liu
**Journal:** Advances in Multimedia
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/10029... | 1002952-2022-07-21_1002952-2022-07-21.md | 34,044 | The Analysis on the Current Situation of the Utilization Mode of Microalgal Biomass Materials | Lina Zhang; Lianfeng Wang; Huizhong Nie; Changbin Liu | Advances in Multimedia
(2022) | Computer Science | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1002952 | 1002952-2022-07-21.xml | ---
## Abstract
In recent years, global warming caused by the greenhouse effect has become one of the greatest threats to mankind. This will have a serious impact on the environment and human body, such as land desertification, increase in ocean acidity, sea level rise, and increase in pests and diseases; affect peop... | 2022 |
# Analysis of 22-year Drought Characteristics in Heilongjiang Province Based on Temperature Vegetation Drought Index
**Authors:** Li Wu; Youzhi Zhang; Limin Wang; Wenhuan Xie; Lijuan Song; Haifeng Zhang; Hongwen Bi; Yanyan Zheng; Yu Zhang; Xiaofei Zhang; Yan Li; Zhiqun Lv
**Journal:** Computational Intelligence and N... | 1003243-2022-04-28_1003243-2022-04-28.md | 82,162 | Analysis of 22-year Drought Characteristics in Heilongjiang Province Based on Temperature Vegetation Drought Index | Li Wu; Youzhi Zhang; Limin Wang; Wenhuan Xie; Lijuan Song; Haifeng Zhang; Hongwen Bi; Yanyan Zheng; Yu Zhang; Xiaofei Zhang; Yan Li; Zhiqun Lv | Computational Intelligence and Neuroscience
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1003243 | 1003243-2022-04-28.xml | ---
## Abstract
Heilongjiang Province is the main grain producing region in China and an important part of Northeast China Plain, which is one of the three black soil belts in the world. The cultivated region of black soil accounts for 50.6% of the black soil region in Northeast China. Due to the obvious rise of temp... | 2022 |
# BUPNN: Manifold Learning Regularizer-Based Blood Usage Prediction Neural Network for Blood Centers
**Authors:** Lingling Pan; Zelin Zang; Siqi Ma; Wei Hu; Zhechang Hu
**Journal:** Computational Intelligence and Neuroscience
(2023)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
... | 1003310-2023-01-31_1003310-2023-01-31.md | 100,492 | BUPNN: Manifold Learning Regularizer-Based Blood Usage Prediction Neural Network for Blood Centers | Lingling Pan; Zelin Zang; Siqi Ma; Wei Hu; Zhechang Hu | Computational Intelligence and Neuroscience
(2023) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1003310 | 1003310-2023-01-31.xml | ---
## Abstract
Blood centers are an essential component of the healthcare system, as timely blood collection, processing, and efficient blood dispatch are critical to the treatment of patients and the performance of the entire healthcare system. At the same time, an efficient blood dispatching system through the hig... | 2023 |
# Synthesis and Thermal Adsorption Characteristics of Silver-Based Hybrid Nanocomposites for Automotive Friction Material Application
**Authors:** R. Venkatesh; P. Sakthivel; M. Vivekanandan; C. Ramesh Kannan; J. Phani Krishna; S. Dhanabalan; T. Thirugnanasambandham; Manaye Majora
**Journal:** Adsorption Science & Te... | 1003492-2023-02-01_1003492-2023-02-01.md | 32,238 | Synthesis and Thermal Adsorption Characteristics of Silver-Based Hybrid Nanocomposites for Automotive Friction Material Application | R. Venkatesh; P. Sakthivel; M. Vivekanandan; C. Ramesh Kannan; J. Phani Krishna; S. Dhanabalan; T. Thirugnanasambandham; Manaye Majora | Adsorption Science & Technology
(2023) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1003492 | 1003492-2023-02-01.xml | ---
## Abstract
Advances in friction materials are imposed on developing multiceramic reinforced hybrid nanocomposites with superior tribomechanical properties. The silver-based matrix metals are gained significance in various applications like bearing, ratchet, and electrical contacts due to their high frictional re... | 2023 |
# Examining Impacts of Acidic Bath Temperature on Nano-Synthesized Lead Selenide Thin Films for the Application of Solar Cells
**Authors:** Saka Abel; Jule Leta Tesfaye; N. Nagaprasad; R. Shanmugam; L. Priyanka Dwarampudi; Tyagi Deepak; Hongxia Zhang; Ramaswamy Krishnaraj; B. Stalin
**Journal:** Bioinorganic Chemistr... | 1003803-2022-01-11_1003803-2022-01-11.md | 23,179 | Examining Impacts of Acidic Bath Temperature on Nano-Synthesized Lead Selenide Thin Films for the Application of Solar Cells | Saka Abel; Jule Leta Tesfaye; N. Nagaprasad; R. Shanmugam; L. Priyanka Dwarampudi; Tyagi Deepak; Hongxia Zhang; Ramaswamy Krishnaraj; B. Stalin | Bioinorganic Chemistry and Applications
(2022) | Chemistry and Chemical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1003803 | 1003803-2022-01-11.xml | ---
## Abstract
The influence of bath temperature on nano-manufactured PbSe (lead selenide) films was successfully generated by utilizing CBD on the acid solution’s metal surface tool. Pb (NO3)2 was employed as a lead ion source as a precursor, while Na2O4Se was used as a selenide ion source. The XRD characterization... | 2022 |
# Ground State Solution for an Autonomous Nonlinear Schrödinger System
**Authors:** Min Liu; Jiu Liu
**Journal:** Journal of Function Spaces
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2021/1003941
---
## Abstract
In this paper, we study the followi... | 1003941-2021-10-27_1003941-2021-10-27.md | 14,429 | Ground State Solution for an Autonomous Nonlinear Schrödinger System | Min Liu; Jiu Liu | Journal of Function Spaces
(2021) | Mathematical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1003941 | 1003941-2021-10-27.xml | ---
## Abstract
In this paper, we study the following autonomous nonlinear Schrödinger system (discussed in the paper), whereλ,μ, and ν are positive parameters; 2∗=2N/N−2 is the critical Sobolev exponent; and f satisfies general subcritical growth conditions. With the help of the Pohožaev manifold, a ground state sol... | 2021 |
# Combined Long Short-Term Memory Network-Based Short-Term Prediction of Solar Irradiance
**Authors:** Manoharan Madhiarasan; Mohamed Louzazni
**Journal:** International Journal of Photoenergy
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1004051
-... | 1004051-2022-08-16_1004051-2022-08-16.md | 45,166 | Combined Long Short-Term Memory Network-Based Short-Term Prediction of Solar Irradiance | Manoharan Madhiarasan; Mohamed Louzazni | International Journal of Photoenergy
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1004051 | 1004051-2022-08-16.xml | ---
## Abstract
Achieving the highly accurate and generic prediction of solar irradiance is arduous because solar irradiance possesses intermittent randomness and is influenced by meteorological parameters. Therefore, this paper endeavors a new combined long short-term memory network (CLSTMN) with various influence m... | 2022 |
# Analysis of Effects of PTEN-Mediated TGF-β/Smad2 Pathway on Osteogenic Differentiation in Osteoporotic Tibial Fracture Rats and Bone Marrow Mesenchymal Stem Cell under Tension
**Authors:** Shiyong Ling; Chen Yan; Kai Huang; Bo Lv; Hua Wang; Xiaoyan Wang; Jun Chen; Jingchuan Sun
**Journal:** Cellular Microbiology
(... | 1004203-2022-04-29_1004203-2022-04-29.md | 21,951 | Analysis of Effects of PTEN-Mediated TGF-β/Smad2 Pathway on Osteogenic Differentiation in Osteoporotic Tibial Fracture Rats and Bone Marrow Mesenchymal Stem Cell under Tension | Shiyong Ling; Chen Yan; Kai Huang; Bo Lv; Hua Wang; Xiaoyan Wang; Jun Chen; Jingchuan Sun | Cellular Microbiology
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1004203 | 1004203-2022-04-29.xml | ---
## Abstract
Purpose. To discuss effects of phosphatase and tensin homolog protein (PTEN)-mediated transforming growth factor-β (TGF-β)/Smad homologue 2 (Smad2) pathway on osteogenic differentiation in osteoporotic (OP) tibial fracture rats and bone marrow mesenchymal stem cell (BMSC) under tension. Methods. A tib... | 2022 |
# Study on the Innovative Development of Digital Media Art in the Context of Artificial Intelligence
**Authors:** Chaomiao Chen
**Journal:** Computational Intelligence and Neuroscience
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1004204
---
## A... | 1004204-2022-08-08_1004204-2022-08-08.md | 77,206 | Study on the Innovative Development of Digital Media Art in the Context of Artificial Intelligence | Chaomiao Chen | Computational Intelligence and Neuroscience
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1004204 | 1004204-2022-08-08.xml | ---
## Abstract
With the rapid development of modern science and technology, the speed of digital media to disseminate information is also accelerating, and the forms of communication become more and more diversified. In order to make digital media art better innovate and develop, designers should actively explore, s... | 2022 |
# Three-Dimensional Image Analysis and Training Method of Action Characteristics of Sanda Whip Leg Technique Based on Wavelet Transform
**Authors:** Lei Song
**Journal:** Advances in Multimedia
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1004568
... | 1004568-2022-09-24_1004568-2022-09-24.md | 54,832 | Three-Dimensional Image Analysis and Training Method of Action Characteristics of Sanda Whip Leg Technique Based on Wavelet Transform | Lei Song | Advances in Multimedia
(2022) | Computer Science | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1004568 | 1004568-2022-09-24.xml | ---
## Abstract
The whip leg technique is an indispensable offensive action for high-level Sanda athletes in the competition. In order to improve the effective guidance of the action of Wushu Sanda whip leg, based on wavelet transform, the three-dimensional image analysis of Sanda whip leg technical action characteri... | 2022 |
# Compressive and Flexural Strength of Concrete with Different Nanomaterials: A Critical Review
**Authors:** R. M. Ashwini; M. Potharaju; V. Srinivas; S. Kanaka Durga; G. V. Rathnamala; Anish Paudel
**Journal:** Journal of Nanomaterials
(2023)
**Publisher:** Hindawi
**License:** http://creativecommons.org/license... | 1004597-2023-01-07_1004597-2023-01-07.md | 95,507 | Compressive and Flexural Strength of Concrete with Different Nanomaterials: A Critical Review | R. M. Ashwini; M. Potharaju; V. Srinivas; S. Kanaka Durga; G. V. Rathnamala; Anish Paudel | Journal of Nanomaterials
(2023) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1004597 | 1004597-2023-01-07.xml | ---
## Abstract
With recent technological advances, adding nanomaterials as a reinforcement material in concrete has gained immense attention. This review paper aims to report advances in the form of a one-stop shop catering to methods that focus on improving the quality of traditional concrete. Nanoparticles—the ele... | 2023 |
# Rapid Maxillary Expansion and Nocturnal Enuresis in Children and Adolescents: A Systematic Review of Controlled Clinical Trials
**Authors:** Khaled Khalaf; Dina Mansour; Zain Sawalha; Sima Habrawi
**Journal:** The Scientific World Journal
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org/lic... | 1004629-2021-06-03_1004629-2021-06-03.md | 68,938 | Rapid Maxillary Expansion and Nocturnal Enuresis in Children and Adolescents: A Systematic Review of Controlled Clinical Trials | Khaled Khalaf; Dina Mansour; Zain Sawalha; Sima Habrawi | The Scientific World Journal
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1004629 | 1004629-2021-06-03.xml | ---
## Abstract
Objectives. To evaluate the effectiveness of rapid palatal expansion in the treatment of nocturnal enuresis among 6–18-year-old children and adolescents. Methods. Comprehensive searches were carried out in 6 electronic databases (EBSCO, ProQuest, Clinical Key, Science Direct, SCOPUS, and OVID) and sup... | 2021 |
# A Case Study of Energy-Saving and Frost Heave Control Scheme in Artificial Ground Freezing Project
**Authors:** Song Zhang; Xiao-min Zhou; Jiwei Zhang; Tiecheng Sun; Wenzhu Ma; Yong Liu; Ning Yang
**Journal:** Geofluids
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DO... | 1004735-2022-06-03_1004735-2022-06-03.md | 52,121 | A Case Study of Energy-Saving and Frost Heave Control Scheme in Artificial Ground Freezing Project | Song Zhang; Xiao-min Zhou; Jiwei Zhang; Tiecheng Sun; Wenzhu Ma; Yong Liu; Ning Yang | Geofluids
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1004735 | 1004735-2022-06-03.xml | ---
## Abstract
In large-scale shallow buried artificial ground freezing (AGF) engineering, frost heave control and energy-saving have always been the most critical problems. By the research and analysis, this manuscript points out the applicability and superiority of intermittent freezing in large-scale shallow buri... | 2022 |
# Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques
**Authors:** Ebrahime Mohammed Senan; Mosleh Hmoud Al-Adhaileh; Fawaz Waselallah Alsaade; Theyazn H. H. Aldhyani; Ahmed Abdullah Alqarni; Nizar Alsharif; M. Irfan Uddin; Ahmed H. Alahmadi; Mukti... | 1004767-2021-06-09_1004767-2021-06-09.md | 46,360 | Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques | Ebrahime Mohammed Senan; Mosleh Hmoud Al-Adhaileh; Fawaz Waselallah Alsaade; Theyazn H. H. Aldhyani; Ahmed Abdullah Alqarni; Nizar Alsharif; M. Irfan Uddin; Ahmed H. Alahmadi; Mukti E Jadhav; Mohammed Y. Alzahrani | Journal of Healthcare Engineering
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1004767 | 1004767-2021-06-09.xml | ---
## Abstract
Chronic kidney disease (CKD) is among the top 20 causes of death worldwide and affects approximately 10% of the world adult population. CKD is a disorder that disrupts normal kidney function. Due to the increasing number of people with CKD, effective prediction measures for the early diagnosis of CKD ... | 2021 |
# Effect of Oxytocin Combined with Different Volume of Water Sac in High-Risk Term Pregnancies
**Authors:** Hanna Mi; Na Sun
**Journal:** Evidence-Based Complementary and Alternative Medicine
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1004816
--... | 1004816-2022-07-06_1004816-2022-07-06.md | 33,986 | Effect of Oxytocin Combined with Different Volume of Water Sac in High-Risk Term Pregnancies | Hanna Mi; Na Sun | Evidence-Based Complementary and Alternative Medicine
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1004816 | 1004816-2022-07-06.xml | ---
## Abstract
Objective. The study estimated the impacts of water sac of different capacities combined with oxytocin (OXT) on pregnant women with high-risk term pregnancies. Methods. Women with high-risk term pregnancies who received OXT were enrolled to perform labor induction using 30 mL (group A), 80 mL (group ... | 2022 |
# The Novel Application of Three-Dimensional Printing Assisted Patient-Specific Instrument Osteotomy Guide in the Precise Osteotomy of Adult Talipes Equinovarus
**Authors:** Yuan-Wei Zhang; Mu-Rong You; Xiao-Xiang Zhang; Xing-Liang Yu; Liang Zhang; Liang Deng; Zhe Wang; Xie-Ping Dong
**Journal:** BioMed Research Inte... | 1004849-2021-12-02_1004849-2021-12-02.md | 42,370 | The Novel Application of Three-Dimensional Printing Assisted Patient-Specific Instrument Osteotomy Guide in the Precise Osteotomy of Adult Talipes Equinovarus | Yuan-Wei Zhang; Mu-Rong You; Xiao-Xiang Zhang; Xing-Liang Yu; Liang Zhang; Liang Deng; Zhe Wang; Xie-Ping Dong | BioMed Research International
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1004849 | 1004849-2021-12-02.xml | ---
## Abstract
Objective. This current research is aimed at assessing clinical efficacy and prognosis of three-dimensional (3D) printing assisted patient-specific instrument (PSI) osteotomy guide in precise osteotomy of adult talipes equinovarus (ATE). Methods. We included a total of 27 patients of ATE malformation ... | 2021 |
# Myopia: Mechanisms and Strategies to Slow Down Its Progression
**Authors:** Andrea Russo; Alessandro Boldini; Davide Romano; Giuseppina Mazza; Stefano Bignotti; Francesco Morescalchi; Francesco Semeraro
**Journal:** Journal of Ophthalmology
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/l... | 1004977-2022-06-14_1004977-2022-06-14.md | 104,635 | Myopia: Mechanisms and Strategies to Slow Down Its Progression | Andrea Russo; Alessandro Boldini; Davide Romano; Giuseppina Mazza; Stefano Bignotti; Francesco Morescalchi; Francesco Semeraro | Journal of Ophthalmology
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1004977 | 1004977-2022-06-14.xml | ---
## Abstract
This topical review aimed to update and clarify the behavioral, pharmacological, surgical, and optical strategies that are currently available to prevent and reduce myopia progression. Myopia is the commonest ocular abnormality; reinstated interest is associated with high and increasing prevalence, es... | 2022 |
# Intelligent Writing System for Patients with Upper Limb Disabilities
**Authors:** Mihaela Hnatiuc; Domnica Alpetri; Muhamad Arif; Dragos Vicoveanu; Iuliana Chiuchisan; Oana Geman
**Journal:** Journal of Sensors
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1... | 1005061-2022-04-15_1005061-2022-04-15.md | 34,709 | Intelligent Writing System for Patients with Upper Limb Disabilities | Mihaela Hnatiuc; Domnica Alpetri; Muhamad Arif; Dragos Vicoveanu; Iuliana Chiuchisan; Oana Geman | Journal of Sensors
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1005061 | 1005061-2022-04-15.xml | ---
## Abstract
Subjects with neurological problems and neurological disorders can be recovered in the dedicated recovery centers and at home. The paper presents a smart writing system that uses dedicated movements to recover specific problems of the hands. The system is based on a smart pen equipped with vibration, ... | 2022 |
# Gut Microbiota and Inflammatory Cytokine Changes in Patients with Ankylosing Spondylitis
**Authors:** Bin Liu; Zhenghua Ding; Junhui Xiong; Xing Heng; Huafu Wang; Weihua Chu
**Journal:** BioMed Research International
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:*... | 1005111-2022-08-19_1005111-2022-08-19.md | 15,979 | Gut Microbiota and Inflammatory Cytokine Changes in Patients with Ankylosing Spondylitis | Bin Liu; Zhenghua Ding; Junhui Xiong; Xing Heng; Huafu Wang; Weihua Chu | BioMed Research International
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1005111 | 1005111-2022-08-19.xml | ---
## Abstract
Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by sacroiliac joint lesions and spinal ascending involvement. The aim of this work was at investigating the gut microbiota profile and proinflammatory cytokines in AS patients. Gut microbiota of AS patients was clearly differe... | 2022 |
# Fisetin Attenuates Arsenic-Induced Hepatic Damage by Improving Biochemical, Inflammatory, Apoptotic, and Histological Profile: In Vivo and In Silico Approach
**Authors:** Muhammad Umar; Saima Muzammil; Muhammad Asif Zahoor; Shama Mustafa; Asma Ashraf; Sumreen Hayat; Muhammad Umar Ijaz
**Journal:** Evidence-Based Co... | 1005255-2022-10-20_1005255-2022-10-20.md | 45,707 | Fisetin Attenuates Arsenic-Induced Hepatic Damage by Improving Biochemical, Inflammatory, Apoptotic, and Histological Profile: In Vivo and In Silico Approach | Muhammad Umar; Saima Muzammil; Muhammad Asif Zahoor; Shama Mustafa; Asma Ashraf; Sumreen Hayat; Muhammad Umar Ijaz | Evidence-Based Complementary and Alternative Medicine
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1005255 | 1005255-2022-10-20.xml | ---
## Abstract
Arsenic (As) is a toxic metalloid and human carcinogen that may cause hepatotoxicity. Fisetin (3, 3′, 4′, 7-tetrahydroxyflavone) is a phytoflavonoid, which shows diverse therapeutic activities. This study aimed to examine the remedial potential of fisetin against As-instigated hepatotoxicity in adult ... | 2022 |
# Anatomical and Biochemical Traits Related to Blue Leaf Coloration ofSelaginella uncinata
**Authors:** Lin Li; Lulu Yang; Aihua Qin; Fangyi Jiang; Limei Chen; Rongyan Deng
**Journal:** Journal of Healthcare Engineering
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:... | 1005449-2022-02-24_1005449-2022-02-24.md | 97,415 | Anatomical and Biochemical Traits Related to Blue Leaf Coloration ofSelaginella uncinata | Lin Li; Lulu Yang; Aihua Qin; Fangyi Jiang; Limei Chen; Rongyan Deng | Journal of Healthcare Engineering
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1005449 | 1005449-2022-02-24.xml | ---
## Abstract
Selaginella uncinata shows particularly rare blue leaves. Previous research has shown that structural interference by the cell wall of adaxial epidermal cells imparts blue coloration in leaves of S. uncinata; the objective of this study was to see whether anthocyanins might additionally contribute to ... | 2022 |
# Prevalence ofS. mansoni Infection and Associated Risk Factors among School Children in Guangua District, Northwest Ethiopia
**Authors:** Belaynesh Tazebew; Denekew Temesgen; Mastewal Alehegn; Desalew Salew; Molalign Tarekegn
**Journal:** Journal of Parasitology Research
(2022)
**Publisher:** Hindawi
**License:*... | 1005637-2022-04-16_1005637-2022-04-16.md | 46,730 | Prevalence ofS. mansoni Infection and Associated Risk Factors among School Children in Guangua District, Northwest Ethiopia | Belaynesh Tazebew; Denekew Temesgen; Mastewal Alehegn; Desalew Salew; Molalign Tarekegn | Journal of Parasitology Research
(2022) | Biological Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1005637 | 1005637-2022-04-16.xml | ---
## Abstract
Back ground. Schistosomiasis is one of the neglected tropical diseases and is prevalent in tropics. It causes morbidity and mortality in developing countries including Ethiopia. This study aimed to determine the prevalence of S. mansoni infection and associated risk factors among two schools of Guangu... | 2022 |
# Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing
**Authors:** Yan Li; Juan Wang; Fang Wang; Chengzhen Gao; Yuanyuan Cao; Jianhua Wang
**Journal:** BioMed Research International
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org... | 1005793-2021-10-07_1005793-2021-10-07.md | 40,073 | Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing | Yan Li; Juan Wang; Fang Wang; Chengzhen Gao; Yuanyuan Cao; Jianhua Wang | BioMed Research International
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1005793 | 1005793-2021-10-07.xml | ---
## Abstract
Objective. Ovarian cancer is the deadliest gynaecological cancer globally. In our study, we aimed to analyze specific cell subpopulations and marker genes among ovarian cancer cells by single-cell RNA sequencing (RNA-seq). Methods. Single-cell RNA-seq data of 66 high-grade serous ovarian cancer cells ... | 2021 |
# Application of Industrial Internet of Things (IIoT) in Crude Oil Production Optimization Using Pump Efficiency Control
**Authors:** Ali S. Allahloh; Mohammad Sarfraz; Mejdal Alqahtani; Shafiq Ahmad; Shamsul Huda
**Journal:** Wireless Communications and Mobile Computing
(2022)
**Publisher:** Hindawi
**License:**... | 1005813-2022-10-07_1005813-2022-10-07.md | 51,372 | Application of Industrial Internet of Things (IIoT) in Crude Oil Production Optimization Using Pump Efficiency Control | Ali S. Allahloh; Mohammad Sarfraz; Mejdal Alqahtani; Shafiq Ahmad; Shamsul Huda | Wireless Communications and Mobile Computing
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1005813 | 1005813-2022-10-07.xml | ---
## Abstract
The collapse of oil prices in mid-2014 and early 2016 was the biggest in modern history that witnessed more than a 70% drop in the price to around $40 barrel. It prompted companies to think seriously to maintain profitability. Most companies were able to survive partly by simplifying their operations.... | 2022 |
# Assessing the Potential of the Strategic Formation of Urban Platoons for Shared Automated Vehicle Fleets
**Authors:** Senlei Wang; Gonçalo Homem de Almeida Correia; Hai Xiang Lin
**Journal:** Journal of Advanced Transportation
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0... | 1005979-2022-07-21_1005979-2022-07-21.md | 137,506 | Assessing the Potential of the Strategic Formation of Urban Platoons for Shared Automated Vehicle Fleets | Senlei Wang; Gonçalo Homem de Almeida Correia; Hai Xiang Lin | Journal of Advanced Transportation
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1005979 | 1005979-2022-07-21.xml | ---
## Abstract
This paper addresses the problem of studying the impacts of the strategic formation of platoons in automated mobility-on-demand (AMoD) systems in future cities. Forming platoons has the potential to improve traffic efficiency, resulting in reduced travel times and energy consumption. However, in the p... | 2022 |
# Simulation Analysis of the Effect of Pile Spacing on the Compressive Load-Bearing Performance of CEP Double Piles
**Authors:** Yongmei Qian; Xun Li; Lishuang Ai; Yaling Jiang; Yu Dong
**Journal:** Advances in Civil Engineering
(2023)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0... | 1005985-2023-03-21_1005985-2023-03-21.md | 45,206 | Simulation Analysis of the Effect of Pile Spacing on the Compressive Load-Bearing Performance of CEP Double Piles | Yongmei Qian; Xun Li; Lishuang Ai; Yaling Jiang; Yu Dong | Advances in Civil Engineering
(2023) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1005985 | 1005985-2023-03-21.xml | ---
## Abstract
Under the action of vertical pressure, the variation of pile spacing affects the bearing performance of concrete expanded-plate (CEP) double pile and the damaged state of soil around the pile. In this study, finite element simulation analysis is performed by ANSYS software, and six sets of semisection... | 2023 |
# HCDRNN-NMPC: A New Approach to Design Nonlinear Model Predictive Control (NMPC) Based on the Hyper Chaotic Diagonal Recurrent Neural Network (HCDRNN)
**Authors:** Samira Johari; Mahdi Yaghoobi; Hamid R. Kobravi
**Journal:** Complexity
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/license... | 1006197-2022-10-18_1006197-2022-10-18.md | 66,809 | HCDRNN-NMPC: A New Approach to Design Nonlinear Model Predictive Control (NMPC) Based on the Hyper Chaotic Diagonal Recurrent Neural Network (HCDRNN) | Samira Johari; Mahdi Yaghoobi; Hamid R. Kobravi | Complexity
(2022) | Mathematical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1006197 | 1006197-2022-10-18.xml | ---
## Abstract
In industrial applications, Stewart platform control is especially important. Because of the Stewart platform’s inherent delays and high nonlinear behavior, a novel nonlinear model predictive controller (NMPC) and new chaotic neural network model (CNNM) are proposed. Here, a novel NMPC based on hyper ... | 2022 |
# Construction of a New 3D Cu(II) Compound with Photocatalytic Activity and Therapeutic Effect on Ventriculitis
**Authors:** Xingzhao Chen; Qing Mao
**Journal:** Journal of Chemistry
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1006203
---
## Abs... | 1006203-2022-05-17_1006203-2022-05-17.md | 29,640 | Construction of a New 3D Cu(II) Compound with Photocatalytic Activity and Therapeutic Effect on Ventriculitis | Xingzhao Chen; Qing Mao | Journal of Chemistry
(2022) | Chemistry and Chemical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1006203 | 1006203-2022-05-17.xml | ---
## Abstract
A new Cu(II) compound, that is {[Cu(L)(ClO4)(H2O)(CH3CN)](ClO4)}n (1, L is 4-(furan-2-yl)-2,6-bis(pyridin-2-yl)pyridine), was solvothermally synthesized and structurally characterized. It features a 0D isolated unit, and these 0D isolated units are further hydrogen-bonded into a 3D supramolecular fram... | 2022 |
# Drug Reaction with Eosinophilia and Systemic Symptoms Syndrome in a Child with Cystic Fibrosis
**Authors:** Ahmed Abushahin; Haneen Toma; Sara G. Hamad; Mutasim Abu-Hasan
**Journal:** Case Reports in Immunology
(2023)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1... | 1006376-2023-02-02_1006376-2023-02-02.md | 18,357 | Drug Reaction with Eosinophilia and Systemic Symptoms Syndrome in a Child with Cystic Fibrosis | Ahmed Abushahin; Haneen Toma; Sara G. Hamad; Mutasim Abu-Hasan | Case Reports in Immunology
(2023) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1006376 | 1006376-2023-02-02.xml | ---
## Abstract
Background. Drug reaction with eosinophilia and systemic symptoms (DRESSs) syndrome is an idiosyncratic drug-induced reaction that rarely occurs in children but can lead to serious complications. It manifests most commonly with fever, extensive skin eruptions, and eosinophilia. Symptoms typically deve... | 2023 |
# Mitochondrial Transplantation Attenuates Cerebral Ischemia-Reperfusion Injury: Possible Involvement of Mitochondrial Component Separation
**Authors:** Qiang Xie; Jun Zeng; Yongtao Zheng; Tianwen Li; Junwei Ren; Kezhu Chen; Quan Zhang; Rong Xie; Feng Xu; Jianhong Zhu
**Journal:** Oxidative Medicine and Cellular Long... | 1006636-2021-11-20_1006636-2021-11-20.md | 75,571 | Mitochondrial Transplantation Attenuates Cerebral Ischemia-Reperfusion Injury: Possible Involvement of Mitochondrial Component Separation | Qiang Xie; Jun Zeng; Yongtao Zheng; Tianwen Li; Junwei Ren; Kezhu Chen; Quan Zhang; Rong Xie; Feng Xu; Jianhong Zhu | Oxidative Medicine and Cellular Longevity
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1006636 | 1006636-2021-11-20.xml | ---
## Abstract
Background. Mitochondrial dysfunctions play a pivotal role in cerebral ischemia-reperfusion (I/R) injury. Although mitochondrial transplantation has been recently explored for the treatment of cerebral I/R injury, the underlying mechanisms and fate of transplanted mitochondria are still poorly underst... | 2021 |
# A Distributed Online Newton Step Algorithm for Multi-Agent Systems
**Authors:** Xiaofei Chu
**Journal:** Mathematical Problems in Engineering
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1007032
---
## Abstract
Most of the current algorithms f... | 1007032-2022-10-28_1007032-2022-10-28.md | 39,079 | A Distributed Online Newton Step Algorithm for Multi-Agent Systems | Xiaofei Chu | Mathematical Problems in Engineering
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1007032 | 1007032-2022-10-28.xml | ---
## Abstract
Most of the current algorithms for solving distributed online optimization problems are based on the first-order method, which are simple in computation but slow in convergence. Newton’s algorithm with fast convergence speed needs to calculate the Hessian matrix and its inverse, leading to computation... | 2022 |
# The Emerging Roles of Tripartite Motif Proteins (TRIMs) in Acute Lung Injury
**Authors:** Yingjie Huang; Yue Xiao; Xuekang Zhang; Xuan Huang; Yong Li
**Journal:** Journal of Immunology Research
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2021/1007126
... | 1007126-2021-10-19_1007126-2021-10-19.md | 42,326 | The Emerging Roles of Tripartite Motif Proteins (TRIMs) in Acute Lung Injury | Yingjie Huang; Yue Xiao; Xuekang Zhang; Xuan Huang; Yong Li | Journal of Immunology Research
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1007126 | 1007126-2021-10-19.xml | ---
## Abstract
Acute lung injury (ALI) is an inflammatory disorder of the lung that causes high mortality and lacks any pharmacological intervention. Ubiquitination plays a critical role in the pathogenesis of ALI as it regulates the alveolocapillary barrier and the inflammatory response. Tripartite motif (TRIM) pro... | 2021 |
# Spherical Fuzzy Soft Topology and Its Application in Group Decision-Making Problems
**Authors:** Harish Garg; Fathima Perveen P A; Sunil Jacob John; Luis Perez-Dominguez
**Journal:** Mathematical Problems in Engineering
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DO... | 1007133-2022-04-26_1007133-2022-04-26.md | 42,861 | Spherical Fuzzy Soft Topology and Its Application in Group Decision-Making Problems | Harish Garg; Fathima Perveen P A; Sunil Jacob John; Luis Perez-Dominguez | Mathematical Problems in Engineering
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1007133 | 1007133-2022-04-26.xml | ---
## Abstract
The spherical fuzzy soft set is a generalized soft set model, which is more realistic, practical, and accurate. It is an extended version of existing fuzzy soft set models that can be used to describe imprecise data in real-world scenarios. The paper seeks to introduce the new concept of spherical fuz... | 2022 |
# The Coal Pillar Width Effect of Principal Stress Deflection and Plastic Zone Form of Surrounding Rock Roadway in Deep Excavation
**Authors:** Ji Li; Rongguang Zhang; Xubo Qiang
**Journal:** Geofluids
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/10... | 1007222-2022-03-18_1007222-2022-03-18.md | 66,547 | The Coal Pillar Width Effect of Principal Stress Deflection and Plastic Zone Form of Surrounding Rock Roadway in Deep Excavation | Ji Li; Rongguang Zhang; Xubo Qiang | Geofluids
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1007222 | 1007222-2022-03-18.xml | ---
## Abstract
In order to explore the influence of coal pillar width on the principal stress deflection and plastic zone form of surrounding rock in deep roadway excavation, taking 11030 working face transportation roadway of Zhaogu No. 2 Coal Mine as engineering background, theoretical analysis, numerical simulati... | 2022 |
# Microbiota, Prostatitis, and Fertility: Bacterial Diversity as a Possible Health Ally
**Authors:** Jenniffer Puerta Suárez; Walter D. Cardona Maya
**Journal:** Advances in Urology
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2021/1007366
---
## Abst... | 1007366-2021-09-28_1007366-2021-09-28.md | 27,138 | Microbiota, Prostatitis, and Fertility: Bacterial Diversity as a Possible Health Ally | Jenniffer Puerta Suárez; Walter D. Cardona Maya | Advances in Urology
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1007366 | 1007366-2021-09-28.xml | ---
## Abstract
Background. In health, microorganisms have been associated with the disease, although the current knowledge shows that the microbiota present in various anatomical sites is associated with multiple benefits. Objective. This study aimed to evaluate and compare the genitourinary microbiota of chronic pr... | 2021 |
# Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining
**Authors:** Jintan Zhu
**Journal:** Journal of Robotics
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1007464
---
## Abstract
With the rapid a... | 1007464-2022-09-16_1007464-2022-09-16.md | 50,305 | Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining | Jintan Zhu | Journal of Robotics
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1007464 | 1007464-2022-09-16.xml | ---
## Abstract
With the rapid advancement of big data, it is becoming a great problem for people to find objective information in the database. The relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects:... | 2022 |
# An Improved BP Neural Network Algorithm for the Evaluation System of Innovation and Entrepreneurship Education in Colleges and Universities
**Authors:** Xuying Sun; Yu Zhang
**Journal:** Mobile Information Systems
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 1... | 1007538-2022-06-30_1007538-2022-06-30.md | 57,352 | An Improved BP Neural Network Algorithm for the Evaluation System of Innovation and Entrepreneurship Education in Colleges and Universities | Xuying Sun; Yu Zhang | Mobile Information Systems
(2022) | Computer Science | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1007538 | 1007538-2022-06-30.xml | ---
## Abstract
The ability of college students has important index to evaluate the training quality of universities. Domestic scholars begin to cultivate spirit and ability of students. With the rapid development of tourism industry and the continuous emergence of “tourism +” new business forms, there is an urgent d... | 2022 |
# A Mini Review on Flotation Techniques and Reagents Used in Graphite Beneficiation
**Authors:** N. Vasumathi; Anshuli Sarjekar; Hrishikesh Chandrayan; K. Chennakesavulu; G. Ramanjaneya Reddy; T. V. Vijaya Kumar; Nour Sh. El-Gendy; S. J. Gopalkrishna
**Journal:** International Journal of Chemical Engineering
(2023)
... | 1007689-2023-03-21_1007689-2023-03-21.md | 71,471 | A Mini Review on Flotation Techniques and Reagents Used in Graphite Beneficiation | N. Vasumathi; Anshuli Sarjekar; Hrishikesh Chandrayan; K. Chennakesavulu; G. Ramanjaneya Reddy; T. V. Vijaya Kumar; Nour Sh. El-Gendy; S. J. Gopalkrishna | International Journal of Chemical Engineering
(2023) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1007689 | 1007689-2023-03-21.xml | ---
## Abstract
Due to its numerous and major industrial uses, graphite is one of the significant carbon allotropes. Refractories and batteries are only a couple of the many uses for graphite. A growing market wants high-purity graphite with big flakes. Since there are fewer naturally occurring high-grade graphite or... | 2023 |
# Analytical Method for Capped Pile–Soil Interaction considering the Load Action of Soil under the Pile Cap
**Authors:** Shilin Luo; Mingquan Liu; Jianqing Jiang; Ailifeila Aierken; Jin Chang; Xuewen Zhang; Rui Zhang
**Journal:** Geofluids
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/lice... | 1007731-2022-03-24_1007731-2022-03-24.md | 33,747 | Analytical Method for Capped Pile–Soil Interaction considering the Load Action of Soil under the Pile Cap | Shilin Luo; Mingquan Liu; Jianqing Jiang; Ailifeila Aierken; Jin Chang; Xuewen Zhang; Rui Zhang | Geofluids
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1007731 | 1007731-2022-03-24.xml | ---
## Abstract
To study the pile–soil interaction mechanism of capped pile, an analytical method considering the load action of soil under the pile cap is proposed. The shearing displacement method is used to derive the lateral friction of pile body under the pile top load, and the Boussinesq solutions is used to de... | 2022 |
# Realization of Music-Assisted Interactive Teaching System Based on Virtual Reality Technology
**Authors:** Yan Gao; Lin Gao
**Journal:** Occupational Therapy International
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1007954
---
## Abstract
Vi... | 1007954-2022-06-11_1007954-2022-06-11.md | 64,245 | Realization of Music-Assisted Interactive Teaching System Based on Virtual Reality Technology | Yan Gao; Lin Gao | Occupational Therapy International
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1007954 | 1007954-2022-06-11.xml | ---
## Abstract
Virtual reality technology has attracted researchers’ attention because it can provide users with a virtual interactive learning environment. Based on the theory of virtual reality technology, this paper proposes the system model design and architecture of virtual interactive music-assisted interactiv... | 2022 |
# Influence of Process Parameters and Convective Heat Transfer on Thermophysical Properties of SiO2 Nanofluids by Multiobjective Function Analysis (DFA)
**Authors:** R. Suprabha; C. R. Mahesha; C. E. Nanjundappa
**Journal:** Journal of Nanomaterials
(2023)
**Publisher:** Hindawi
**License:** http://creativecommon... | 1008046-2023-01-27_1008046-2023-01-27.md | 37,315 | Influence of Process Parameters and Convective Heat Transfer on Thermophysical Properties of SiO2 Nanofluids by Multiobjective Function Analysis (DFA) | R. Suprabha; C. R. Mahesha; C. E. Nanjundappa | Journal of Nanomaterials
(2023) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2023/1008046 | 1008046-2023-01-27.xml | ---
## Abstract
From the requirements, high-performance cooling systems in the applications of solar, heat exchangers, and chemical industries are needed to improve efficiency. The metal-oxides-based nanofluids composed the better thermal properties with exchange of heat-transfer mechanisms. Therefore, this study mai... | 2023 |
# Application of Image Recognition Method Based on Diffusion Equation in Film and Television Production
**Authors:** Liyuan Guo
**Journal:** Advances in Mathematical Physics
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2021/1008281
---
## Abstract
On... | 1008281-2021-10-04_1008281-2021-10-04.md | 36,486 | Application of Image Recognition Method Based on Diffusion Equation in Film and Television Production | Liyuan Guo | Advances in Mathematical Physics
(2021) | Mathematical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1008281 | 1008281-2021-10-04.xml | ---
## Abstract
On the basis of studying the basic theory of anisotropic diffusion equation, this paper focuses on the application of anisotropic diffusion equation in image recognition film production. In order to further improve the application performance of P-M (Perona-Malik) anisotropic diffusion model, an impro... | 2021 |
# Ciprofloxacin-ResistantPseudomonas aeruginosa Lung Abscess Complicating COVID-19 Treated with the Novel Oral Fluoroquinolone Delafloxacin
**Authors:** Jürgen Panholzer; Matthias Neuboeck; Guangyu Shao; Sven Heldt; Markus Winkler; Paul Greiner; Norbert Fritsch; Bernd Lamprecht; Helmut Salzer
**Journal:** Case Report... | 1008330-2022-02-16_1008330-2022-02-16.md | 16,048 | Ciprofloxacin-ResistantPseudomonas aeruginosa Lung Abscess Complicating COVID-19 Treated with the Novel Oral Fluoroquinolone Delafloxacin | Jürgen Panholzer; Matthias Neuboeck; Guangyu Shao; Sven Heldt; Markus Winkler; Paul Greiner; Norbert Fritsch; Bernd Lamprecht; Helmut Salzer | Case Reports in Pulmonology
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1008330 | 1008330-2022-02-16.xml | ---
## Abstract
Purpose. We report the development of a lung abscess caused by a ciprofloxacin-resistant Pseudomonas aeruginosa in a patient with COVID-19 on long-term corticosteroid therapy. Successful antimicrobial treatment included the novel oral fluoroquinolone delafloxacin suggesting an oral administration opti... | 2022 |
# The Application of Transcranial Electrical Stimulation in Sports Psychology
**Authors:** Shuzhi Chang
**Journal:** Computational and Mathematical Methods in Medicine
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1008346
---
## Abstract
The prob... | 1008346-2022-07-13_1008346-2022-07-13.md | 68,784 | The Application of Transcranial Electrical Stimulation in Sports Psychology | Shuzhi Chang | Computational and Mathematical Methods in Medicine
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1008346 | 1008346-2022-07-13.xml | ---
## Abstract
The problem of sports psychological fatigue has become one of the focal points of common concern among scholars at home and abroad. Athletes will face many problems and challenges in competition or training, and if they are not handled properly, they will have negative experiences, which will affect t... | 2022 |
# A Survey on Deep Learning for Building Load Forecasting
**Authors:** Ioannis Patsakos; Eleni Vrochidou; George A. Papakostas
**Journal:** Mathematical Problems in Engineering
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1008491
---
## Abstract
... | 1008491-2022-06-17_1008491-2022-06-17.md | 158,530 | A Survey on Deep Learning for Building Load Forecasting | Ioannis Patsakos; Eleni Vrochidou; George A. Papakostas | Mathematical Problems in Engineering
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1008491 | 1008491-2022-06-17.xml | ---
## Abstract
Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focu... | 2022 |
# Exposure to Ambient Air Pollutant PM10 in the Second Trimester of Pregnancy Is Associated with Preterm Birth: A Birth-Based Health Information Cohort Study
**Authors:** Pengying Xiao; Lijun Wang; Yingjie Yao; Yu Chang; Jianghou Hou
**Journal:** BioMed Research International
(2022)
**Publisher:** Hindawi
**Licen... | 1008538-2022-06-22_1008538-2022-06-22.md | 30,118 | Exposure to Ambient Air Pollutant PM10 in the Second Trimester of Pregnancy Is Associated with Preterm Birth: A Birth-Based Health Information Cohort Study | Pengying Xiao; Lijun Wang; Yingjie Yao; Yu Chang; Jianghou Hou | BioMed Research International
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1008538 | 1008538-2022-06-22.xml | ---
## Abstract
Objectives. We evaluated the effects of exposure to high concentrations of particulate matter (PM)10 on preterm birth (PTB) and identified a critical concentration of PM10 that could lead to PTB via a birth-based health information cohort study. Methods. We conducted a birth-based cohort study consist... | 2022 |
# Solving Traveling Salesman Problems Based on Artificial Cooperative Search Algorithm
**Authors:** Guangjun Liu; Xiaoping Xu; Feng Wang; Yangli Tang
**Journal:** Computational Intelligence and Neuroscience
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/20... | 1008617-2022-04-12_1008617-2022-04-12.md | 44,405 | Solving Traveling Salesman Problems Based on Artificial Cooperative Search Algorithm | Guangjun Liu; Xiaoping Xu; Feng Wang; Yangli Tang | Computational Intelligence and Neuroscience
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1008617 | 1008617-2022-04-12.xml | ---
## Abstract
The traveling salesman problem is a typical NP hard problem and a typical combinatorial optimization problem. Therefore, an improved artificial cooperative search algorithm is proposed to solve the traveling salesman problem. For the basic artificial collaborative search algorithm, firstly, the sigmoi... | 2022 |
# Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach
**Authors:** Piyush Kumar Pareek; Prasath Alais Surendhar S; Ram Prasad; Govindaraj Ramkumar; Ekta Dixit; R. Subbiah; Saleh H. Salmen; Hesham S. Almoallim; S. S. Priya; S. Arockia Jayadhas
**... | 1008652-2022-09-28_1008652-2022-09-28.md | 62,862 | Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach | Piyush Kumar Pareek; Prasath Alais Surendhar S; Ram Prasad; Govindaraj Ramkumar; Ekta Dixit; R. Subbiah; Saleh H. Salmen; Hesham S. Almoallim; S. S. Priya; S. Arockia Jayadhas | Advances in Materials Science and Engineering
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1008652 | 1008652-2022-09-28.xml | ---
## Abstract
Novel methods and materials are used in healthcare applications for finding cancer in various parts of the human system. To select the most suitable therapy plan for individuals with domestically progressed cervical cancer, robustness metrics are required to estimate their early phase. The goal of the... | 2022 |
# Conferring the Midas Touch on Integrative Taxonomy: A Nanogold-Oligonucleotide Conjugate-Based Quick Species Identification Tool
**Authors:** Rahul Kumar; Ajay Kumar Sharma
**Journal:** International Journal of Ecology
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI... | 1009066-2022-08-23_1009066-2022-08-23.md | 45,021 | Conferring the Midas Touch on Integrative Taxonomy: A Nanogold-Oligonucleotide Conjugate-Based Quick Species Identification Tool | Rahul Kumar; Ajay Kumar Sharma | International Journal of Ecology
(2022) | Earth and Environmental Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1009066 | 1009066-2022-08-23.xml | ---
## Abstract
Nanogold or functionalized gold nanoparticles (GNPs) have myriad applications in medical sciences. GNPs are widely used in the area of nanodiagnostics and nanotherapeutics. Applications of GNPs in taxonomic studies have not been studied vis-à-vis its extensive medical applications. GNPs have great pot... | 2022 |
# Influence of Pathogen Type on Neonatal Sepsis Biomarkers
**Authors:** Lyudmila Akhmaltdinova; Svetlana Kolesnichenko; Alyona Lavrinenko; Irina Kadyrova; Olga Avdienko; Lyudmila Panibratec
**Journal:** International Journal of Inflammation
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org/lic... | 1009231-2021-11-19_1009231-2021-11-19.md | 29,333 | Influence of Pathogen Type on Neonatal Sepsis Biomarkers | Lyudmila Akhmaltdinova; Svetlana Kolesnichenko; Alyona Lavrinenko; Irina Kadyrova; Olga Avdienko; Lyudmila Panibratec | International Journal of Inflammation
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1009231 | 1009231-2021-11-19.xml | ---
## Abstract
Understanding immunoregulation in newborns can help to determine the pathophysiology of neonatal sepsis and will contribute to improve the diagnosis, prognosis, and treatment and remains an urgent and unmet medical need to understand hyperinflammation or hypoinflammation associated with sepsis in newb... | 2021 |
# Dynamic Data Scheduling of a Flexible Industrial Job Shop Based on Digital Twin Technology
**Authors:** Juan Li; Xianghong Tian; Jing Liu
**Journal:** Discrete Dynamics in Nature and Society
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1009507
-... | 1009507-2022-08-03_1009507-2022-08-03.md | 51,738 | Dynamic Data Scheduling of a Flexible Industrial Job Shop Based on Digital Twin Technology | Juan Li; Xianghong Tian; Jing Liu | Discrete Dynamics in Nature and Society
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1009507 | 1009507-2022-08-03.xml | ---
## Abstract
Aiming at the problems of premature convergence of existing workshop dynamic data scheduling methods and the decline in product output, a flexible industrial job shop dynamic data scheduling method based on digital twin technology is proposed. First, digital twin technology is proposed, which provides... | 2022 |
# Network Pharmacology-Based Systematic Analysis of Molecular Mechanisms ofGeranium wilfordii Maxim for HSV-2 Infection
**Authors:** Hao Zhang; Ming-Huang Gao; Yang Chen; Tao Liu
**Journal:** Evidence-Based Complementary and Alternative Medicine
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.or... | 1009551-2021-11-03_1009551-2021-11-03.md | 30,549 | Network Pharmacology-Based Systematic Analysis of Molecular Mechanisms ofGeranium wilfordii Maxim for HSV-2 Infection | Hao Zhang; Ming-Huang Gao; Yang Chen; Tao Liu | Evidence-Based Complementary and Alternative Medicine
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1009551 | 1009551-2021-11-03.xml | ---
## Abstract
Background. Being a traditional Chinese medicine, Geranium wilfordii Maxim (GWM) is used for the treatment of various infectious diseases, and its main active ingredients are the polyphenolic substances such as polyphenols quercetin, corilagin, and geraniin. Previous studies have demonstrated the anti... | 2021 |
# Fluid Geochemistry within the North China Craton: Spatial Variation and Genesis
**Authors:** Lu Chang; Li Ying; Chen Zhi; Liu Zhaofei; Zhao Yuanxin; Hu Le
**Journal:** Geofluids
(2021)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2021/1009766
---
## Abstra... | 1009766-2021-10-25_1009766-2021-10-25.md | 71,884 | Fluid Geochemistry within the North China Craton: Spatial Variation and Genesis | Lu Chang; Li Ying; Chen Zhi; Liu Zhaofei; Zhao Yuanxin; Hu Le | Geofluids
(2021) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1009766 | 1009766-2021-10-25.xml | ---
## Abstract
The North China Craton (NCC) is a typical representative of the ancient destruction craton. Numerous studies have shown that extensive destruction of the NCC occurred in the east, whereas the western part was only partially modified. The Bohai Bay Basin is in the center of the destruction area in the ... | 2021 |
# The Construction of Accurate Recommendation Model of Learning Resources of Knowledge Graph under Deep Learning
**Authors:** Xia Yang; Leping Tan
**Journal:** Scientific Programming
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/1010122
---
## Abs... | 1010122-2022-01-25_1010122-2022-01-25.md | 81,861 | The Construction of Accurate Recommendation Model of Learning Resources of Knowledge Graph under Deep Learning | Xia Yang; Leping Tan | Scientific Programming
(2022) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1010122 | 1010122-2022-01-25.xml | ---
## Abstract
With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second,... | 2022 |
# Analysis of Interaction of Multiple Cracks Based on Tip Stress Field Using Extended Finite Element Method
**Authors:** Yuxiao Wang; Akbar A. Javadi; Corrado Fidelibus
**Journal:** Journal of Applied Mathematics
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1... | 1010174-2022-12-22_1010174-2022-12-22.md | 44,929 | Analysis of Interaction of Multiple Cracks Based on Tip Stress Field Using Extended Finite Element Method | Yuxiao Wang; Akbar A. Javadi; Corrado Fidelibus | Journal of Applied Mathematics
(2022) | Mathematical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1010174 | 1010174-2022-12-22.xml | ---
## Abstract
A new method is presented to study the interaction of multiple cracks, especially for the areas near crack tips by using the extended finite element method. In order to track the cracks, a new geometric tracking technique is proposed to track enriched elements and nodes along the crack instead of usin... | 2022 |
# Heat Shock Protein 72 Expressing Stress in Sepsis: Unbridgeable Gap between Animal and Human Studies—A Hypothetical “Comparative” Study
**Authors:** George Briassoulis; Efrossini Briassouli; Diana-Michaela Fitrolaki; Ioanna Plati; Kleovoulos Apostolou; Theonymfi Tavladaki; Anna-Maria Spanaki
**Journal:** BioMed Res... | 101023-2014-01-12_101023-2014-01-12.md | 88,917 | Heat Shock Protein 72 Expressing Stress in Sepsis: Unbridgeable Gap between Animal and Human Studies—A Hypothetical “Comparative” Study | George Briassoulis; Efrossini Briassouli; Diana-Michaela Fitrolaki; Ioanna Plati; Kleovoulos Apostolou; Theonymfi Tavladaki; Anna-Maria Spanaki | BioMed Research International
(2014) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2014/101023 | 101023-2014-01-12.xml | ---
## Abstract
Heat shock protein 72 (Hsp72) exhibits a protective role during times of increased risk of pathogenic challenge and/or tissue damage. The aim of the study was to ascertain Hsp72 protective effect differences between animal and human studies in sepsis using a hypothetical “comparative study” model.
For... | 2014 |
# Surgical Resection for Small Cell Lung Cancer: Pneumonectomy versus Lobectomy
**Authors:** Jiang Yuequan; Zhang Zhi; Xie Chenmin
**Journal:** ISRN Surgery
(2012)
**Publisher:** International Scholarly Research Network
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.5402/2012/101024
---
... | 101024-2012-05-30_101024-2012-05-30.md | 24,204 | Surgical Resection for Small Cell Lung Cancer: Pneumonectomy versus Lobectomy | Jiang Yuequan; Zhang Zhi; Xie Chenmin | ISRN Surgery
(2012) | Medical & Health Sciences | International Scholarly Research Network | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.5402/2012/101024 | 101024-2012-05-30.xml | ---
## Abstract
Background. There are some patients with SCLC that are diagnosed in the operating room by cryosection and surgeons had to perform surgical resection for these patients. The aim of this study is to compare the effective of pneumonectomy with lobectomy for SCLC.
Methods. A retrospective study was undert... | 2012 |
# Takotsubo Syndrome Associated with ST Elevation Myocardial Infarction
**Authors:** Saad Ezad; Michael McGee; Andrew J. Boyle
**Journal:** Case Reports in Cardiology
(2019)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2019/1010243
---
## Abstract
Backgroun... | 1010243-2019-05-16_1010243-2019-05-16.md | 10,043 | Takotsubo Syndrome Associated with ST Elevation Myocardial Infarction | Saad Ezad; Michael McGee; Andrew J. Boyle | Case Reports in Cardiology
(2019) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2019/1010243 | 1010243-2019-05-16.xml | ---
## Abstract
Background. Takotsubo syndrome is a reversible heart failure syndrome which often presents with symptoms and ECG changes that mimic an acute myocardial infarction. Obstructive coronary artery disease has traditionally been seen as exclusion criteria for the diagnosis of takotsubo; however, recent repo... | 2019 |
# Rapamycin and FTY720 Alleviate Atherosclerosis by Cross Talk of Macrophage Polarization and Autophagy
**Authors:** Rui-zhen Sun; Ying Fan; Xiao Liang; Tian-tian Gong; Qi Wang; Hui Liu; Zhi-yan Shan; Lei Lei
**Journal:** BioMed Research International
(2018)
**Publisher:** Hindawi
**License:** http://creativecomm... | 1010248-2018-12-06_1010248-2018-12-06.md | 43,706 | Rapamycin and FTY720 Alleviate Atherosclerosis by Cross Talk of Macrophage Polarization and Autophagy | Rui-zhen Sun; Ying Fan; Xiao Liang; Tian-tian Gong; Qi Wang; Hui Liu; Zhi-yan Shan; Lei Lei | BioMed Research International
(2018) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2018/1010248 | 1010248-2018-12-06.xml | ---
## Abstract
Foam cell formation and macrophage polarization are involved in the pathologic development of atherosclerosis, one of the most important human diseases affecting large and medium artery walls. This study was designed to assess the effects of rapamycin and FTY720 (fingolimod) on macrophages and foam ce... | 2018 |
# Immunology and Cell Biology of Parasitic Diseases 2014
**Authors:** Luis I. Terrazas; Abhay R. Satoskar; Miriam Rodriguez-Sosa; Abraham Landa-Piedra
**Journal:** BioMed Research International
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** ... | 101025-2015-05-20_101025-2015-05-20.md | 393 | Immunology and Cell Biology of Parasitic Diseases 2014 | Luis I. Terrazas; Abhay R. Satoskar; Miriam Rodriguez-Sosa; Abraham Landa-Piedra | BioMed Research International
(2015) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101025 | 101025-2015-05-20.xml | ---
## Body
---
*Source: 101025-2015-05-20.xml* | 2015 |
# Mitochondrial Dysfunction and Diabetic Nephropathy: Nontraditional Therapeutic Opportunities
**Authors:** Ping Na Zhang; Meng Qi Zhou; Jing Guo; Hui Juan Zheng; Jingyi Tang; Chao Zhang; Yu Ning Liu; Wei Jing Liu; Yao Xian Wang
**Journal:** Journal of Diabetes Research
(2021)
**Publisher:** Hindawi
**License:** ... | 1010268-2021-12-09_1010268-2021-12-09.md | 61,183 | Mitochondrial Dysfunction and Diabetic Nephropathy: Nontraditional Therapeutic Opportunities | Ping Na Zhang; Meng Qi Zhou; Jing Guo; Hui Juan Zheng; Jingyi Tang; Chao Zhang; Yu Ning Liu; Wei Jing Liu; Yao Xian Wang | Journal of Diabetes Research
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1010268 | 1010268-2021-12-09.xml | ---
## Abstract
Diabetic nephropathy (DN) is a progressive microvascular diabetic complication. Growing evidence shows that persistent mitochondrial dysfunction contributes to the progression of renal diseases, including DN, as it alters mitochondrial homeostasis and, in turn, affects normal kidney function. Pharmaco... | 2021 |
# The Influence of Age on Interaction between Breath-Holding Test and Single-Breath Carbon Dioxide Test
**Authors:** Nikita Trembach; Igor Zabolotskikh
**Journal:** BioMed Research International
(2017)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2017/1010289
... | 1010289-2017-01-31_1010289-2017-01-31.md | 15,862 | The Influence of Age on Interaction between Breath-Holding Test and Single-Breath Carbon Dioxide Test | Nikita Trembach; Igor Zabolotskikh | BioMed Research International
(2017) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2017/1010289 | 1010289-2017-01-31.xml | ---
## Abstract
Introduction. The aim of the study was to compare the breath-holding test and single-breath carbon dioxide test in evaluation of the peripheral chemoreflex sensitivity to carbon dioxide in healthy subjects of different age.Methods. The study involved 47 healthy volunteers between ages of 25 and 85 yea... | 2017 |
# Imaging Diagnosis of Splanchnic Venous Thrombosis
**Authors:** S. Rajesh; Amar Mukund; Ankur Arora
**Journal:** Gastroenterology Research and Practice
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2015/101029
---
## Abstract
S... | 101029-2015-10-12_101029-2015-10-12.md | 151,437 | Imaging Diagnosis of Splanchnic Venous Thrombosis | S. Rajesh; Amar Mukund; Ankur Arora | Gastroenterology Research and Practice
(2015) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101029 | 101029-2015-10-12.xml | ---
## Abstract
Splanchnic vein thrombosis (SVT) is a broad term that includes Budd-Chiari syndrome and occlusion of veins that constitute the portal venous system. Due to the common risk factors involved in the pathogenesis of these clinically distinct disorders, concurrent involvement of two different regions is qu... | 2015 |
# Association of FTO Mutations with Risk and Survival of Breast Cancer in a Chinese Population
**Authors:** Xianxu Zeng; Zhenying Ban; Jing Cao; Wei Zhang; Tianjiao Chu; Dongmei Lei; Yanmin Du
**Journal:** Disease Markers
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org... | 101032-2015-06-04_101032-2015-06-04.md | 23,736 | Association of FTO Mutations with Risk and Survival of Breast Cancer in a Chinese Population | Xianxu Zeng; Zhenying Ban; Jing Cao; Wei Zhang; Tianjiao Chu; Dongmei Lei; Yanmin Du | Disease Markers
(2015) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101032 | 101032-2015-06-04.xml | ---
## Abstract
Recently, several studies have reported associations between fat mass and obesity-associated (FTO) gene mutations and cancer susceptibility. But little is known about their association with risk and survival of breast cancer in Chinese population. The aim of this study is to examine whether cancer-rel... | 2015 |
# Brittle Creep Failure, Critical Behavior, and Time-to-Failure Prediction of Concrete under Uniaxial Compression
**Authors:** Yingchong Wang; Na Zhou; Fuqing Chang; Shengwang Hao
**Journal:** Advances in Materials Science and Engineering
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://cr... | 101035-2015-10-01_101035-2015-10-01.md | 32,758 | Brittle Creep Failure, Critical Behavior, and Time-to-Failure Prediction of Concrete under Uniaxial Compression | Yingchong Wang; Na Zhou; Fuqing Chang; Shengwang Hao | Advances in Materials Science and Engineering
(2015) | Engineering & Technology | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101035 | 101035-2015-10-01.xml | ---
## Abstract
Understanding the time-dependent brittle deformation behavior of concrete as a main building material is fundamental for the lifetime prediction and engineering design. Herein, we present the experimental measures of brittle creep failure, critical behavior, and the dependence of time-to-failure, on t... | 2015 |
# Using Noninvasive Brain Measurement to Explore the Psychological Effects of Computer Malfunctions on Users during Human-Computer Interactions
**Authors:** Leanne M. Hirshfield; Philip Bobko; Alex Barelka; Stuart H. Hirshfield; Mathew T. Farrington; Spencer Gulbronson; Diane Paverman
**Journal:** Advances in Human-C... | 101038-2014-04-30_101038-2014-04-30.md | 100,851 | Using Noninvasive Brain Measurement to Explore the Psychological Effects of Computer Malfunctions on Users during Human-Computer Interactions | Leanne M. Hirshfield; Philip Bobko; Alex Barelka; Stuart H. Hirshfield; Mathew T. Farrington; Spencer Gulbronson; Diane Paverman | Advances in Human-Computer Interaction
(2014) | Engineering & Technology | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2014/101038 | 101038-2014-04-30.xml | ---
## Abstract
In today’s technologically driven world, there is a need to better understand the ways that common computer malfunctions affect computer users. These malfunctions may have measurable influences on computer user’s cognitive, emotional, and behavioral responses. An experiment was conducted where partici... | 2014 |
# Approximate Analytical Solution for a Coupled System of Fractional Nonlinear Integrodifferential Equations by the RPS Method
**Authors:** Ayed Al e’damat
**Journal:** Complexity
(2020)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2020/1010382
---
## Abstra... | 1010382-2020-08-13_1010382-2020-08-13.md | 19,586 | Approximate Analytical Solution for a Coupled System of Fractional Nonlinear Integrodifferential Equations by the RPS Method | Ayed Al e’damat | Complexity
(2020) | Mathematical Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2020/1010382 | 1010382-2020-08-13.xml | ---
## Abstract
In this work, a modified residual power series method is implemented for providing efficient analytical and approximate solutions for a class of coupled system of nonlinear fractional integrodifferential equations. The proposed algorithm is based on the concept of residual error functions and generali... | 2020 |
# Evolutionary Algorithms Applied to Antennas and Propagation: A Review of State of the Art
**Authors:** Sotirios K. Goudos; Christos Kalialakis; Raj Mittra
**Journal:** International Journal of Antennas and Propagation
(2016)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/l... | 1010459-2016-08-25_1010459-2016-08-25.md | 31,486 | Evolutionary Algorithms Applied to Antennas and Propagation: A Review of State of the Art | Sotirios K. Goudos; Christos Kalialakis; Raj Mittra | International Journal of Antennas and Propagation
(2016) | Engineering & Technology | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2016/1010459 | 1010459-2016-08-25.xml | ---
## Abstract
A review of evolutionary algorithms (EAs) with applications to antenna and propagation problems is presented. EAs have emerged as viable candidates for global optimization problems and have been attracting the attention of the research community interested in solving real-world engineering problems, a... | 2016 |
# Discoidin Domain-Containing Receptor 2 Is Present in Human Atherosclerotic Plaques and Involved in the Expression and Activity of MMP-2
**Authors:** Qi Yu; Ruihan Liu; Ying Chen; Ahmed Bilal Waqar; Fuqiang Liu; Juan Yang; Ting Lian; Guangwei Zhang; Hua Guan; Yuanyuan Cui; Cangbao Xu
**Journal:** Oxidative Medicine ... | 1010496-2021-12-16_1010496-2021-12-16.md | 43,265 | Discoidin Domain-Containing Receptor 2 Is Present in Human Atherosclerotic Plaques and Involved in the Expression and Activity of MMP-2 | Qi Yu; Ruihan Liu; Ying Chen; Ahmed Bilal Waqar; Fuqiang Liu; Juan Yang; Ting Lian; Guangwei Zhang; Hua Guan; Yuanyuan Cui; Cangbao Xu | Oxidative Medicine and Cellular Longevity
(2021) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2021/1010496 | 1010496-2021-12-16.xml | ---
## Abstract
Discoidin domain-containing receptor 2 (DDR2) has been suggested to be involved in atherosclerotic progression, but its pathological role remains unknown. Using immunochemical staining, we located and compared the expression of DDR2 in the atherosclerotic plaques of humans and various animal models. T... | 2021 |
# Computer-Aided Modelling and Analysis of PV Systems: A Comparative Study
**Authors:** Charalambos Koukouvaos; Dionisis Kandris; Maria Samarakou
**Journal:** The Scientific World Journal
(2014)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.115... | 101056-2014-01-02_101056-2014-01-02.md | 46,137 | Computer-Aided Modelling and Analysis of PV Systems: A Comparative Study | Charalambos Koukouvaos; Dionisis Kandris; Maria Samarakou | The Scientific World Journal
(2014) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2014/101056 | 101056-2014-01-02.xml | ---
## Abstract
Modern scientific advances have enabled remarkable efficacy for photovoltaic systems with regard to the exploitation of solar energy, boosting them into having a rapidly growing position among the systems developed for the production of renewable energy. However, in many cases the design, analysis, an... | 2014 |
# Rhabdomyolysis and Dengue Fever: A Case Report and Literature Review
**Authors:** Tanya Sargeant; Tricia Harris; Rohan Wilks; Sydney Barned; Karen Galloway-Blake; Trevor Ferguson
**Journal:** Case Reports in Medicine
(2013)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/li... | 101058-2013-01-08_101058-2013-01-08.md | 16,129 | Rhabdomyolysis and Dengue Fever: A Case Report and Literature Review | Tanya Sargeant; Tricia Harris; Rohan Wilks; Sydney Barned; Karen Galloway-Blake; Trevor Ferguson | Case Reports in Medicine
(2013) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2013/101058 | 101058-2013-01-08.xml | ---
## Abstract
The medical literature contains only a few reports of rhabdomyolysis occurring in patients with dengue fever. We report the case of a 25-year-old Jamaican man who was admitted to a private hospital four days after the onset of an acute febrile illness with fever, myalgia, and generalized weakness. Den... | 2013 |
# Analysis of Road Traffic Network Cascade Failures with Coupled Map Lattice Method
**Authors:** Yanan Zhang; Yingrong Lu; Guangquan Lu; Peng Chen; Chuan Ding
**Journal:** Mathematical Problems in Engineering
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licenses/by/... | 101059-2015-10-25_101059-2015-10-25.md | 29,382 | Analysis of Road Traffic Network Cascade Failures with Coupled Map Lattice Method | Yanan Zhang; Yingrong Lu; Guangquan Lu; Peng Chen; Chuan Ding | Mathematical Problems in Engineering
(2015) | Engineering & Technology | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101059 | 101059-2015-10-25.xml | ---
## Abstract
In recent years, there is growing literature concerning the cascading failure of network characteristics. The object of this paper is to investigate the cascade failures on road traffic network, considering the aeolotropism of road traffic network topology and road congestion dissipation in traffic fl... | 2015 |
# Acute Hepatocellular Drug Induced Liver Injury Probably by Alfuzosin
**Authors:** Tufan Cicek; Huseyin Savas Gokturk; Gulhan Kanat Unler
**Journal:** Case Reports in Urology
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2015/10106... | 101062-2015-02-22_101062-2015-02-22.md | 8,969 | Acute Hepatocellular Drug Induced Liver Injury Probably by Alfuzosin | Tufan Cicek; Huseyin Savas Gokturk; Gulhan Kanat Unler | Case Reports in Urology
(2015) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101062 | 101062-2015-02-22.xml | ---
## Abstract
Alpha blockers are the drugs that exert their effects by binding to alpha receptors and relaxing smooth muscles and are currently used for treatment of benign prostate hyperplasia (BPH). These drugs are often tolerated well by the patients. However, they also possess some common side effects. Hepatoto... | 2015 |
# Recent Theory and Applications on Numerical Algorithms and Special Functions
**Authors:** Ali H. Bhrawy; Robert A. Van Gorder; Dumitru Baleanu; Guo-Cheng Wu
**Journal:** Abstract and Applied Analysis
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licenses/by/4.0/
*... | 101063-2015-03-24_101063-2015-03-24.md | 401 | Recent Theory and Applications on Numerical Algorithms and Special Functions | Ali H. Bhrawy; Robert A. Van Gorder; Dumitru Baleanu; Guo-Cheng Wu | Abstract and Applied Analysis
(2015) | Mathematical Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101063 | 101063-2015-03-24.xml | ---
## Body
---
*Source: 101063-2015-03-24.xml* | 2015 |
# A Challenging Case of Acute Mercury Toxicity
**Authors:** Ali Nayfeh; Thamer Kassim; Noor Addasi; Faysal Alghoula; Christopher Holewinski; Zachary Depew
**Journal:** Case Reports in Medicine
(2018)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2018/1010678
-... | 1010678-2018-02-18_1010678-2018-02-18.md | 19,335 | A Challenging Case of Acute Mercury Toxicity | Ali Nayfeh; Thamer Kassim; Noor Addasi; Faysal Alghoula; Christopher Holewinski; Zachary Depew | Case Reports in Medicine
(2018) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2018/1010678 | 1010678-2018-02-18.xml | ---
## Abstract
Background. Mercury exists in multiple forms: elemental, organic, and inorganic. Its toxic manifestations depend on the type and magnitude of exposure. The role of colonoscopic decompression in acute mercury toxicity is still unclear. We present a case of acute elemental mercury toxicity secondary to ... | 2018 |
# Lifetime Analysis of Rubber Gasket Composed of Methyl Vinyl Silicone Rubber with Low-Temperature Resistance
**Authors:** Young-Doo Kwon; Seong-Hwa Jun; Ji-Min Song
**Journal:** Mathematical Problems in Engineering
(2015)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licen... | 101068-2015-10-11_101068-2015-10-11.md | 33,968 | Lifetime Analysis of Rubber Gasket Composed of Methyl Vinyl Silicone Rubber with Low-Temperature Resistance | Young-Doo Kwon; Seong-Hwa Jun; Ji-Min Song | Mathematical Problems in Engineering
(2015) | Engineering & Technology | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2015/101068 | 101068-2015-10-11.xml | ---
## Abstract
Most machines and instruments constantly require elastomeric materials like rubber for the purposes of shock absorption, noise attenuation, and sealing. The material properties and accurate lifetime prediction of rubber are closely related to the quality of machines, especially their durability and re... | 2015 |
# Ultrasound of Primary Aneurysmal Bone Cyst
**Authors:** Katrina N. Glazebrook; Gary L. Keeney; Michael G. Rock
**Journal:** Case Reports in Radiology
(2014)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2014/101069
---
## Abstract
An... | 101069-2014-01-23_101069-2014-01-23.md | 12,764 | Ultrasound of Primary Aneurysmal Bone Cyst | Katrina N. Glazebrook; Gary L. Keeney; Michael G. Rock | Case Reports in Radiology
(2014) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2014/101069 | 101069-2014-01-23.xml | ---
## Abstract
Aneurysmal bone cysts (ABC) are rare, benign, expansile lesions of bone often found in the metaphyses of long bones in pediatric and young adult population. Multiple fluid levels are typically seen on imaging with magnetic resonance imaging (MRI) or computed tomography (CT). We describe a case of a pr... | 2014 |
# An Analytical Solution for Predicting the Vibration-Fatigue-Life in Bimodal Random Processes
**Authors:** Chaoshuai Han; Yongliang Ma; Xianqiang Qu; Mindong Yang
**Journal:** Shock and Vibration
(2017)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2017/1010726... | 1010726-2017-01-31_1010726-2017-01-31.md | 54,869 | An Analytical Solution for Predicting the Vibration-Fatigue-Life in Bimodal Random Processes | Chaoshuai Han; Yongliang Ma; Xianqiang Qu; Mindong Yang | Shock and Vibration
(2017) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2017/1010726 | 1010726-2017-01-31.xml | ---
## Abstract
Predicting the vibration-fatigue-life of engineering structures subjected to random loading is a critical issue for. Frequency methods are generally adopted to deal with this problem. This paper focuses on bimodal spectra methods, including Jiao-Moan method, Fu-Cebon method, and Modified Fu-Cebon meth... | 2017 |
# Comparison of Raw Dairy Manure Slurry and Anaerobically Digested Slurry as N Sources for Grass Forage Production
**Authors:** Olivia E. Saunders; Ann-Marie Fortuna; Joe H. Harrison; Elizabeth Whitefield; Craig G. Cogger; Ann C. Kennedy; Andy I. Bary
**Journal:** International Journal of Agronomy
(2012)
**Publish... | 101074-2012-05-06_101074-2012-05-06.md | 58,386 | Comparison of Raw Dairy Manure Slurry and Anaerobically Digested Slurry as N Sources for Grass Forage Production | Olivia E. Saunders; Ann-Marie Fortuna; Joe H. Harrison; Elizabeth Whitefield; Craig G. Cogger; Ann C. Kennedy; Andy I. Bary | International Journal of Agronomy
(2012) | Agricultural Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2012/101074 | 101074-2012-05-06.xml | ---
## Abstract
We conducted a 3-year field study to determine how raw dairy slurry and anaerobically digested slurry (dairy slurry and food waste) applied via broadcast and subsurface deposition to reed canarygrass (Phalaris arundinacea) affected forage biomass, N uptake, apparent nitrogen recovery (ANR), and soil n... | 2012 |
# A Passenger-Oriented Model for Train Rescheduling on an Urban Rail Transit Line considering Train Capacity Constraint
**Authors:** Wenkai Xu; Peng Zhao; Liqiao Ning
**Journal:** Mathematical Problems in Engineering
(2017)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** ... | 1010745-2017-04-05_1010745-2017-04-05.md | 42,644 | A Passenger-Oriented Model for Train Rescheduling on an Urban Rail Transit Line considering Train Capacity Constraint | Wenkai Xu; Peng Zhao; Liqiao Ning | Mathematical Problems in Engineering
(2017) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2017/1010745 | 1010745-2017-04-05.xml | ---
## Abstract
The major objective of this work is to present a train rescheduling model with train capacity constraint from a passenger-oriented standpoint for a subway line. The model expects to minimize the average generalized delay time (AGDT) of passengers. The generalized delay time is taken into consideration... | 2017 |
# Scene-Specialized Multitarget Detector with an SMC-PHD Filter and a YOLO Network
**Authors:** Qianli Liu; Yibing Li; Qianhui Dong; Fang Ye
**Journal:** Computational Intelligence and Neuroscience
(2022)
**Publisher:** Hindawi
**License:** http://creativecommons.org/licenses/by/4.0/
**DOI:** 10.1155/2022/101076... | 1010767-2022-04-28_1010767-2022-04-28.md | 84,458 | Scene-Specialized Multitarget Detector with an SMC-PHD Filter and a YOLO Network | Qianli Liu; Yibing Li; Qianhui Dong; Fang Ye | Computational Intelligence and Neuroscience
(2022) | Medical & Health Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2022/1010767 | 1010767-2022-04-28.xml | ---
## Abstract
You only look once (YOLO) is one of the most efficient target detection networks. However, the performance of the YOLO network decreases significantly when the variation between the training data and the real data is large. To automatically customize the YOLO network, we suggest a novel transfer learn... | 2022 |
# Methane Source and Turnover in the Shallow Sediments to the West of Haima Cold Seeps on the Northwestern Slope of the South China Sea
**Authors:** Junxi Feng; Shengxiong Yang; Hongbin Wang; Jinqiang Liang; Yunxin Fang; Min Luo
**Journal:** Geofluids
(2019)
**Publisher:** Hindawi
**License:** http://creativecomm... | 1010824-2019-08-05_1010824-2019-08-05.md | 92,735 | Methane Source and Turnover in the Shallow Sediments to the West of Haima Cold Seeps on the Northwestern Slope of the South China Sea | Junxi Feng; Shengxiong Yang; Hongbin Wang; Jinqiang Liang; Yunxin Fang; Min Luo | Geofluids
(2019) | Engineering & Technology | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2019/1010824 | 1010824-2019-08-05.xml | ---
## Abstract
The Haima cold seeps are active cold seep areas that were recently discovered on the northwestern slope of the South China Sea (SCS). Three piston cores (CL30, CL44, and CL47) were collected within an area characterized by bottom simulating reflectors to the west of Haima cold seeps. Porewater profile... | 2019 |
# Regulation Effect of Zinc Fingers and Homeoboxes 2 on Alpha-Fetoprotein in Human Hepatocellular Carcinoma
**Authors:** Shao Wei Hu; Meng Zhang; Ling Xue; Jian Ming Wen
**Journal:** Gastroenterology Research and Practice
(2013)
**Publisher:** Hindawi Publishing Corporation
**License:** http://creativecommons.org... | 101083-2013-02-26_101083-2013-02-26.md | 28,358 | Regulation Effect of Zinc Fingers and Homeoboxes 2 on Alpha-Fetoprotein in Human Hepatocellular Carcinoma | Shao Wei Hu; Meng Zhang; Ling Xue; Jian Ming Wen | Gastroenterology Research and Practice
(2013) | Medical & Health Sciences | Hindawi Publishing Corporation | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2013/101083 | 101083-2013-02-26.xml | ---
## Abstract
Aim. To investigate the relationship between alpha-fetoprotein and zinc fingers and homeoboxes 2 in hepatocellular carcinoma. Materials and Methods. The expressions of zinc fingers and homeoboxes 2, nuclear factor-YA, and alpha-fetoprotein mRNA in 63 hepatocellular carcinoma were detected by reverse t... | 2013 |
# Impacts of Different Physical Parameterization Configurations on Widespread Heavy Rain Forecast over the Northern Area of Vietnam in WRF-ARW Model
**Authors:** Tien Du Duc; Cuong Hoang Duc; Lars Robert Hole; Lam Hoang; Huyen Luong Thi Thanh; Hung Mai Khanh
**Journal:** Advances in Meteorology
(2019)
**Publisher:... | 1010858-2019-08-18_1010858-2019-08-18.md | 94,294 | Impacts of Different Physical Parameterization Configurations on Widespread Heavy Rain Forecast over the Northern Area of Vietnam in WRF-ARW Model | Tien Du Duc; Cuong Hoang Duc; Lars Robert Hole; Lam Hoang; Huyen Luong Thi Thanh; Hung Mai Khanh | Advances in Meteorology
(2019) | Earth and Environmental Sciences | Hindawi | CC BY 4.0 | http://creativecommons.org/licenses/by/4.0/ | 10.1155/2019/1010858 | 1010858-2019-08-18.xml | ---
## Abstract
This study investigates the impacts of different physical parameterization schemes in the Weather Research and Forecasting model with the ARW dynamical core (WRF-ARW model) on the forecasts of heavy rainfall over the northern part of Vietnam (Bac Bo area). Various physical model configurations generat... | 2019 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.