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 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.

1. Introduction

Elongated 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.). Table 1 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. Station 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.

Station name	Long.	Lat.	Annual rain	#50 mm	#100 mm
Muong Te	102.83	22.37	2423	11	2
Sin Ho	103.23	22.37	2787	13	2
Tam Duong	103.48	22.42	2355	9	2
Muong La	104.03	21.52	1412	5	1
Than Uyen	103.88	21.95	1920	8	1
Quynh Nhai	103.57	21.85	1678	7	1
Mu Cang Chai	104.05	21.87	1741	5	1
Tuan Giao	103.42	21.58	1586	5	1
Pha Din	103.5	21.57	1840	6	1
Van Chan	104.52	21.58	1481	6	1
Song Ma	103.75	21.5	1118	2	0
Co Noi	104.15	21.13	1308	4	0
Yen Chau	104.3	21.05	1229	3	0
Bac Yen	104.42	21.23	1519	4	0
Phu Yen	104.63	21.27	1496	5	1
Minh Dai	105.05	21.17	1695	6	1
Moc Chau	104.68	20.83	1581	5	1
Mai Chau	105.05	20.65	1698	7	1
Pho Rang	104.47	22.23	1648	7	1
Bac Ha	104.28	22.53	1611	4	0
Hoang Su Phi	104.68	22.75	1739	5	1
Bac Me	105.37	22.73	1753	6	0
Bao Lac	105.67	22.95	1179	3	0
Bac Quang	104.87	22.5	4295	24	9
Luc Yen	104.78	22.1	1917	8	1
Ham Yen	105.03	22.07	1858	8	1
Chiem Hoa	105.27	22.15	1631	6	1
Cho Ra	105.73	22.45	1417	5	0
Nguyen Binh	105.9	22.65	1686	6	1
Ngan Son	105.98	22.43	1853	8	1
Trung Khanh	106.57	22.83	1821	7	2
Dinh Hoa	105.63	21.92	1749	8	2
Bac Son	106.32	21.9	1693	7	2
Huu Lung	106.35	21.5	1572	7	1
Dinh Lap	107.1	21.53	1865	7	2
Quang Ha	107.75	21.45	2883	16	5
Phu Ho	105.23	21.45	1480	6	1
Tam Dao	105.65	21.47	2585	13	4
Hiep Hoa	105.97	21.35	1601	6	1
Bac Ninh	106.08	21.18	1608	7	2
Luc Ngan	106.55	21.38	1406	6	1
Son Dong	106.85	21.33	1686	8	2
Ba Vi	105.42	21.15	1942	9	2
Ha Dong	105.75	20.97	1635	7	1
Chi Linh	106.38	21.08	1536	7	1
Uong Bi	106.75	21.03	1824	9	2
Kim Boi	105.53	20.33	2115	9	2
Chi Ne	105.78	20.48	1841	8	2
Lac Son	105.45	20.45	2040	9	2
Cuc Phuong	105.72	20.25	1918	9	2
Yen Dinh	105.67	19.98	1509	7	1
Sam Son	105.9	19.75	1759	9	3
Do Luong	105.3	18.89	1871	9	2
Lai Chau	103.15	22.07	2178	10	2
Sa Pa	103.82	22.35	2581	10	2
Lao Cai	103.97	22.5	1704	7	1
Ha Giang	104.97	22.82	2372	12	2
Son La	103.9	21.33	1456	4	1
That Khe	106.47	22.25	1482	6	1
Cao Bang	106.25	22.67	1439	6	1
Bac Giang	106.22	21.3	1518	7	1
Hon Ngu	105.77	18.8	2007	11	4
Bac Can	105.83	22.15	1421	5	1
Dien Bien Phu	103	21.37	1536	5	1
Tuyen Quang	105.22	21.82	1654	8	2
Viet Tri	105.42	21.3	1601	8	2
Vinh Yen	105.6	21.32	1489	6	1
Yen Bai	104.87	21.7	1768	8	1
Son Tay	105.5	21.13	1612	7	1
Hoa Binh	105.33	20.82	1828	8	2
Huong Son	105.43	18.52	2135	10	4
Ha Noi	105.8	21.03	1647	8	2
Phu Ly	105.92	20.55	1731	8	2
Hung Yen	106.05	20.65	1381	6	1
Nam Dinh	106.15	20.39	1564	7	1
Ninh Binh	105.97	20.23	1662	8	2
Phu Lien	106.63	20.8	1572	7	1
Hai Duong	106.3	20.93	1561	7	1
Hon Dau	106.8	20.67	1501	8	1
Van Ly	106.3	20.12	1620	9	2
Lang Son	106.77	21.83	2874	12	2
Thai Nguyen	105.83	21.6	1266	5	1
Nho Quan	105.73	20.32	1728	8	2
Bai Chay	107.07	20.97	1700	8	2
Co To	107.77	20.98	1933	10	3
Thai Binh	106.35	20.45	1930	10	3
Cua Ong	107.35	21.02	1591	8	1
Tien Yen	107.4	21.33	2189	12	3
Mong Cai	107.97	21.52	2168	10	3
Bach Long Vi	107.72	20.13	2670	15	5
Huong Khe	105.72	18.18	1216	6	1
Thanh Hoa	105.78	19.75	2516	12	5
Hoi Xuan	105.12	20.37	1701	8	3
Tuong Duong	104.43	19.28	1720	6	1
Vinh	105.7	18.67	1305	5	1
Ha Tinh	105.9	18.35	1896	10	4
Ky Anh	106.28	18.07	2507	13	5
Bai Thuong	105.38	19.9	1830	7	2
Nhu Xuan	105.57	19.63	1758	8	3
Tinh Gia	105.78	19.45	1777	9	3
Quy Chau	105.12	19.57	1661	7	1
Quy Hop	105.15	19.32	1579	7	2
Tay Hieu	105.4	19.32	1517	7	2
Quynh Luu	105.63	19.17	1559	8	2
Con Cuong	104.88	19.05	1647	7	2

In 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 link https://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 Figure 1(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)).

Details are in the caption following the image — **Figure 1 (a)**
Open in figure viewer PowerPoint

Example 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 Table 2.

Figure 1 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 Figures 1(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 Figures 1(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 Section 2, 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.

2. Experiments

2.1. Model Description

This 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. Details of physical parameterization configurations in different experiments.

Abbreviation	Microphysics	Shortwave radiation	Boundary layer
Betts–Miller–Janjic (BMJ) cumulus parameterization
BMJ-Lin-Duh-MYJ	Lin	Duhia	MYJ
BMJ-Lin-Duh-YSU	Lin	Duhia	YSU
BMJ-Lin-God-MYJ	Lin	Goddard	MYJ
BMJ-Lin-God-YSU	Lin	Goddard	YSU
BMJ-WSM3-Duh-MYJ	WSM3	Duhia	MYJ
BMJ-WSM3-Duh-YSU	WSM3	Duhia	YSU
BMJ-WSM3-God-MYJ	WSM3	Goddard	MYJ
BMJ-WSM3-God-YSU	WSM3	Goddard	YSU
BMJ-WSM5-Duh-MYJ	WSM5	Duhia	MYJ
BMJ-WSM5-Duh-YSU	WSM5	Duhia	YSU
BMJ-WSM5-God-MYJ	WSM5	Duhia	MYJ
BMJ-WSM5-God-YSU	WSM5	Goddard	YSU
BMJ-WSM6-Duh-MYJ	WSM6	Duhia	MYJ
BMJ-WSM6-Duh-YSU	WSM6	Duhia	YSU
BMJ-WSM6-God-MYJ	WSM6	Goddard	MYJ
BMJ-WSM6-God-YSU	WSM6	Goddard	YSU

Kain–Fritsch (KF) cumulus parameterization
KF-Lin-Duh-MYJ	Lin	Duhia	MYJ
KF-Lin-Duh-YSU	Lin	Duhia	YSU
KF-Lin-God-MYJ	Lin	Goddard	MYJ
KF-Lin-God-YSU	Lin	Goddard	YSU
KF-WSM3-Duh-MYJ	WSM3	Duhia	MYJ
KF-WSM3-Duh-YSU	WSM3	Duhia	YSU
KF-WSM3-God-MYJ	WSM3	Goddard	MYJ
KF-WSM3-God-YSU	WSM3	Goddard	YSU
KF-WSM5-Duh-MYJ	WSM5	Duhia	MYJ
KF-WSM5-Duh-YSU	WSM5	Duhia	YSU
KF-WSM5-God-MYJ	WSM5	Goddard	MYJ
KF-WSM5-God-YSU	WSM5	Goddard	YSU
KF-WSM6-Duh-MYJ	WSM6	Duhia	MYJ
KF-WSM6-Duh-YSU	WSM6	Duhia	YSU
KF-WSM6-God-MYJ	WSM6	Goddard	MYJ
KF-WSM6-God-YSU	WSM6	Goddard	YSU

The 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.

2.2. Boundary Conditions

The 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 link https://rda.ucar.edu/datasets/ds335.0. More information on GFS data can be found at https://www.nco.ncep.noaa.gov/pmb/products/gfs/.

2.3. Observation Data

The 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 Figure 2), 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 Table 3 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. List 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.

Forecast cycle (year month day hour)	Maximum 24 h accumulation observation (mm)	Station with maximum observation	Types of rain events
2012 05 18 00	37	Bac Yen	Type I
2012 05 19 00	44	Lac Son	Type I
2012 05 20 00	114	Bac Quang	Type I
2012 05 21 00	195	Bac Quang	Type I
2012 05 22 00	131	Ha Dong	Type I
2012 05 23 00	186	Quang Ha	Type I
2012 07 20 00	41	Bac Quang	Type I
2012 07 21 00	24.6	Lai Chau	Type II
2012 07 22 00	116	Vinh Yen	Type II
2012 07 23 00	75	Vinh	Type II
2012 07 25 00	229	Tuyen Quang	Type II
2012 07 26 00	104	Ham Yen	Type II
2012 07 27 00	112	Bac Quang	Type I
2012 08 03 00	58.1	Con Cuong	Type I
2012 08 04 00	34.1	Tinh Gia	Type I
2012 08 05 00	70	Lang Son	Type I
2012 08 06 00	153	Muong La	Type I
2012 08 07 00	163	Quang Ha	Type I
2012 08 13 00	63.6	Muong La	Type II
2012 08 14 00	64.5	Nho Quan	Type II
2012 08 15 00	74	Do Luong	Type II
2012 08 16 00	76	Tam Dao	Type II
2012 08 31 00	49.1	Sin Ho	Type IV
2012 09 01 00	67	Nhu Xuan	Type IV
2012 09 02 00	124	Quynh Luu	Type IV
2012 09 03 00	84	Moc Chau	Type IV
2012 09 04 00	157	Huong Khe	Type IV
2012 09 16 00	151.4	Muong Te	Type IV
2012 09 17 00	104.6	Tam Duong	Type III
2013 06 19 00	109	Bac Quang	Type II
2013 06 20 00	72.2	Huong Khe	Type II
2013 06 21 00	59	Nam Dinh	Type II
2013 06 22 00	218	Ha Tinh	Type II
2014 08 25 00	91	Lao Cai	Type II
2014 08 26 00	89	Van Ly	Type II
2014 08 27 00	157	Hon Ngu	Type II
2015 05 18 00	90	Hoi Xuan	Type III
2015 05 19 00	58	Sin Ho	Type III
2015 05 20 00	106	That Khe	Type III
2015 05 21 00	72	Tuyen Quang	Type III
2015 06 21 00	124	Luc Yen	Type II
2015 06 22 00	72.7	Do Luong	Type II
2015 07 01 00	84.5	Cao Bang	Type I
2015 07 02 00	123.0	Van Chan	Type I
2015 07 03 00	74.0	Huong Khe	Type I
2015 07 21 00	54.5	Hoa Binh	Type I
2015 07 22 00	19	Sin Ho	Type I
2015 07 23 00	92	Sin Ho	Type I
2015 07 24 00	180	Quynh Nhai	Type I
2015 07 25 00	181	Cua Ong	Type I
2015 07 26 00	432	Cua Ong	Type I
2015 07 27 00	347	Quang Ha	Type I
2015 07 28 00	224	Mong Cai	Type I
2015 07 29 00	247	Cua Ong	Type I
2015 07 30 00	145	Quang Ha	Type I
2015 07 31 00	239	Quang Ha	Type I
2015 08 01 00	157	Phu Lien	Type I
2015 09 18 00	73	Hai Duong	Type IV
2015 09 19 00	78.0	Dinh Hoa	Type IV
2016 05 20 00	118	Tinh Gia	Type I
2016 05 21 00	107.0	Ha Nam	Type I
2016 05 22 00	92	Sa Pa	Type I
2016 05 23 00	190.4	Yen Bai	Type I
2016 07 24 00	60	Phu Lien	Type II
2016 07 25 00	17	Thai Binh	Type II
2016 07 26 00	46	Dinh Hoa	Type II
2016 07 30 00	18.1	Mu Cang Chai	Type II
2016 08 01 00	19	Sin Ho	Type I
2016 08 02 00	150	Ninh Binh	Type I
2016 08 10 00	81	Quynh Nhai	Type I
2016 08 11 00	117	Thanh Hoa	Type I
2016 08 12 00	87	Tay Hieu	Type I

Type 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.

2.4. Validation Methods

By finding the nearest grids to each station position (listed in Table 1), 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)

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)

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.

3. Results

3.1. General Performance

The histogram charts (Figure 3) 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 4 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.

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 Tables 4, 5, and 6.

Table 4. Skill scores (TS, ETS, BIAS, POD, and FAR) and hit rates H, 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.

	>25 mm/24 h									>50 mm/24 h
	TS	ETS	BIAS	POD	FAR	H	F	M	T	TS	ETS	BIAS	POD	FAR	H	F	M	T
BMJ-Lin-Duh-MYJ	0.207	0.1517	0.5854	0.2719	0.5355	301	347	806	6106	0.1049	0.0874	0.4139	0.1342	0.6757	60	125	387	6988
BMJ-Lin-Duh-YSU	0.2087	0.1551	0.5592	0.2692	0.5186	298	321	809	6132	0.1072	0.0899	0.4094	0.1365	0.6667	61	122	386	6991
BMJ-Lin-God-MYJ	0.2283	0.1613	0.7986	0.3342	0.5814	370	514	737	5939	0.1225	0.0996	0.6197	0.1767	0.7148	79	198	368	6915
BMJ-Lin-God-YSU	0.2304	0.1649	0.7706	0.3315	0.5698	367	486	740	5967	0.122	0.1	0.5839	0.1723	0.705	77	184	370	6929
BMJ-WSM3-Duh-MYJ	0.2051	0.1433	0.6929	0.2882	0.5841	319	448	788	6005	0.1066	0.0846	0.5794	0.1521	0.7375	68	191	379	6922
BMJ-WSM3-Duh-YSU	0.2201	0.1595	0.6775	0.3026	0.5533	335	415	772	6038	0.1153	0.0928	0.6018	0.1655	0.7249	74	195	373	6918
BMJ-WSM3-God-MYJ	0.2388	0.1649	0.9539	0.3767	0.6051	417	639	690	5814	0.1364	0.1089	0.8456	0.2215	0.7381	99	279	348	6834
BMJ-WSM3-God-YSU	0.2408	0.1709	0.8663	0.3622	0.5819	401	558	706	5895	0.1391	0.1116	0.8501	0.226	0.7342	101	279	346	6834
BMJ-WSM5-Duh-MYJ	0.2147	0.153	0.692	0.299	0.5679	331	435	776	6018	0.1166	0.0951	0.5638	0.1633	0.7103	73	179	374	6934
BMJ-WSM5-Duh-YSU	0.2156	0.1553	0.6703	0.2963	0.558	328	414	779	6039	0.1319	0.1092	0.613	0.1879	0.6934	84	190	363	6923
BMJ-WSM5-God-MYJ	0.2408	0.1743	0.7967	0.3487	0.5624	386	496	721	5957	0.1273	0.1044	0.6242	0.1834	0.7061	82	197	365	6916
BMJ-WSM5-God-YSU	0.2449	0.1766	0.8365	0.3613	0.568	400	526	707	5927	0.1386	0.112	0.8009	0.2192	0.7263	98	260	349	6853
BMJ-WSM6-Duh-MYJ	0.2079	0.1419	0.7687	0.3044	0.604	337	514	770	5939	0.1166	0.0925	0.6711	0.1745	0.74	78	222	369	6891
BMJ-WSM6-Duh-YSU	0.2126	0.1469	0.7669	0.3098	0.596	343	506	764	5947	0.1386	0.1135	0.7271	0.2103	0.7108	94	231	353	6882
BMJ-WSM6-God-MYJ	0.2552	0.1793	1.0126	0.4092	0.5959	453	668	654	5785	0.1554	0.1269	0.9128	0.2573	0.7181	115	293	332	6820
BMJ-WSM6-God-YSU	0.2477	0.1747	0.9386	0.3848	0.59	426	613	681	5840	0.1644	0.1355	0.9485	0.2752	0.7099	123	301	324	6812
KF-Lin-Duh-MYJ	0.2399	0.162	1.0497	0.3966	0.6222	439	723	668	5730	0.1525	0.1262	0.7919	0.2371	0.7006	106	248	341	6865
KF-Lin-Duh-YSU	0.2656	0.1884	1.0533	0.4309	0.5909	477	689	630	5764	0.1457	0.1168	0.9351	0.2461	0.7368	110	308	337	6805
KF-Lin-God-MYJ	0.2507	0.165	1.2755	0.4562	0.6424	505	907	602	5546	0.1564	0.1233	1.2327	0.302	0.755	135	416	312	6697
KF-Lin-God-YSU	0.2649	0.1795	1.2818	0.4779	0.6272	529	890	578	5563	0.162	0.128	1.311	0.3221	0.7543	144	442	303	6671
KF-WSM3-Duh-MYJ	0.2442	0.1627	1.1491	0.4219	0.6329	467	805	640	5648	0.1581	0.1266	1.1141	0.2886	0.741	129	369	318	6744
KF-WSM3-Duh-YSU	0.2664	0.1843	1.1861	0.4598	0.6123	509	804	598	5649	0.1667	0.135	1.1298	0.3043	0.7307	136	369	311	6744
KF-WSM3-God-MYJ	0.2743	0.1867	1.3668	0.5095	0.6272	564	949	543	5504	0.172	0.1368	1.4385	0.3579	0.7512	160	483	287	6630
KF-WSM3-God-YSU	0.2739	0.1852	1.4029	0.5167	0.6317	572	981	535	5472	0.1778	0.1413	1.5638	0.387	0.7525	173	526	274	6587
KF-WSM5-Duh-MYJ	0.2583	0.1766	1.1653	0.4444	0.6186	492	798	615	5655	0.1648	0.1333	1.1186	0.2998	0.732	134	366	313	6747
KF-WSM5-Duh-YSU	0.2656	0.1828	1.2042	0.4625	0.6159	512	821	595	5632	0.1588	0.1269	1.1387	0.2931	0.7426	131	378	316	6735
KF-WSM5-God-MYJ	0.2693	0.1812	1.3758	0.5041	0.6336	558	965	549	5488	0.1773	0.1421	1.4362	0.3669	0.7445	164	478	283	6635
KF-WSM5-God-YSU	0.2826	0.1939	1.4146	0.5321	0.6239	589	977	518	5476	0.178	0.1415	1.5615	0.387	0.7521	173	525	274	6588
KF-WSM6-Duh-MYJ	0.2598	0.1758	1.234	0.4607	0.6266	510	856	597	5597	0.1663	0.1326	1.2908	0.3266	0.747	146	431	301	6682
KF-WSM6-Duh-YSU	0.28	0.1952	1.2836	0.4995	0.6108	553	868	554	5585	0.1958	0.1611	1.4049	0.3937	0.7197	176	452	271	6661
KF-WSM6-God-MYJ	0.26	0.1698	1.43	0.5014	0.6494	555	1028	552	5425	0.1926	0.156	1.5906	0.4183	0.737	187	524	260	6589
KF-WSM6-God-YSU	0.2792	0.1888	1.467	0.5384	0.633	596	1028	511	5425	0.1844	0.1463	1.7584	0.4295	0.7557	192	594	255	6519

Table 5. Skill scores (TS, ETS, BIAS, POD, and FAR) and hit rates H, 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.

	>25 mm/24 h									>50 mm/24 h
	TS	ETS	BIAS	POD	FAR	H	F	M	T	TS	ETS	BIAS	POD	FAR	H	F	M	T
BMJ-Lin-Duh-MYJ	0.1791	0.1054	0.4842	0.2254	0.5344	365	419	1254	5522	0.0962	0.0703	0.3463	0.1181	0.6589	88	170	657	6645
BMJ-Lin-Duh-YSU	0.1853	0.1069	0.5287	0.239	0.5479	387	469	1232	5472	0.1105	0.0803	0.4295	0.1423	0.6687	106	214	639	6601
BMJ-Lin-God-MYJ	0.234	0.1345	0.769	0.3354	0.5639	543	702	1076	5239	0.1225	0.0846	0.5987	0.1745	0.7085	130	316	615	6499
BMJ-Lin-God-YSU	0.243	0.1408	0.8073	0.3533	0.5624	572	735	1047	5206	0.1357	0.0971	0.6174	0.1933	0.687	144	316	601	6499
BMJ-WSM3-Duh-MYJ	0.2332	0.1353	0.7505	0.3311	0.5588	536	679	1083	5262	0.152	0.109	0.7396	0.2295	0.6897	171	380	574	6435
BMJ-WSM3-Duh-YSU	0.2579	0.1573	0.7956	0.3681	0.5373	596	692	1023	5249	0.1587	0.1142	0.7839	0.2443	0.6884	182	402	563	6413
BMJ-WSM3-God-MYJ	0.2616	0.1452	1.0167	0.4182	0.5887	677	969	942	4972	0.1546	0.1034	1.0054	0.2685	0.733	200	549	545	6266
BMJ-WSM3-God-YSU	0.2641	0.148	1.0136	0.4206	0.585	681	960	938	4981	0.1539	0.1034	0.9826	0.2644	0.7309	197	535	548	6280
BMJ-WSM5-Duh-MYJ	0.2425	0.1455	0.7437	0.3403	0.5424	551	653	1068	5288	0.1582	0.1171	0.6899	0.2309	0.6654	172	342	573	6473
BMJ-WSM5-Duh-YSU	0.2545	0.154	0.7931	0.3638	0.5413	589	695	1030	5246	0.1698	0.1257	0.7758	0.2577	0.6678	192	386	553	6429
BMJ-WSM5-God-MYJ	0.2687	0.1601	0.9074	0.404	0.5548	654	815	965	5126	0.174	0.1286	0.8201	0.2698	0.671	201	410	544	6405
BMJ-WSM5-God-YSU	0.2677	0.1522	1.0068	0.4237	0.5791	686	944	933	4997	0.1674	0.1166	1.0027	0.2872	0.7135	214	533	531	6282
BMJ-WSM6-Duh-MYJ	0.2282	0.1261	0.7986	0.3342	0.5816	541	752	1078	5189	0.1414	0.097	0.7772	0.2201	0.7168	164	415	581	6400
BMJ-WSM6-Duh-YSU	0.2471	0.1426	0.8394	0.3644	0.5659	590	769	1029	5172	0.169	0.1198	0.9409	0.2805	0.7019	209	492	536	6323
BMJ-WSM6-God-MYJ	0.2484	0.1255	1.1081	0.4194	0.6215	679	1115	940	4826	0.1471	0.0931	1.1141	0.2711	0.7566	202	628	543	6187
BMJ-WSM6-God-YSU	0.283	0.1642	1.0723	0.4571	0.5737	740	996	879	4945	0.1588	0.1053	1.0966	0.2872	0.7381	214	603	531	6212
KF-Lin-Duh-MYJ	0.2144	0.1147	0.7634	0.3113	0.5922	504	732	1115	5209	0.1299	0.0905	0.6349	0.1879	0.704	140	333	605	6482
KF-Lin-Duh-YSU	0.2312	0.136	0.7171	0.3224	0.5504	522	639	1097	5302	0.124	0.0847	0.6309	0.1799	0.7149	134	336	611	6479
KF-Lin-God-MYJ	0.2584	0.1355	1.1174	0.4348	0.6108	704	1105	915	4836	0.1304	0.0781	1.0362	0.2349	0.7733	175	597	570	6218
KF-Lin-God-YSU	0.2806	0.1647	1.0241	0.4435	0.5669	718	940	901	5001	0.1611	0.1097	1.0215	0.2805	0.7254	209	552	536	6263
KF-WSM3-Duh-MYJ	0.266	0.149	1.0284	0.4262	0.5856	690	975	929	4966	0.1735	0.1212	1.0604	0.3047	0.7127	227	563	518	6252
KF-WSM3-Duh-YSU	0.2839	0.1719	0.9691	0.4355	0.5507	705	864	914	5077	0.1948	0.1437	1.0255	0.3302	0.678	246	518	499	6297
KF-WSM3-God-MYJ	0.2845	0.1503	1.3484	0.5201	0.6143	842	1341	777	4600	0.1766	0.1161	1.4416	0.3664	0.7458	273	801	472	6014
KF-WSM3-God-YSU	0.3018	0.1706	1.3125	0.5361	0.5915	868	1257	751	4684	0.1877	0.1271	1.455	0.3879	0.7334	289	795	456	6020
KF-WSM5-Duh-MYJ	0.2801	0.1622	1.055	0.4497	0.5738	728	980	891	4961	0.1785	0.1262	1.0644	0.3128	0.7062	233	560	512	6255
KF-WSM5-Duh-YSU	0.2948	0.1812	1.0019	0.4558	0.545	738	884	881	5057	0.2037	0.1509	1.102	0.3557	0.6772	265	556	480	6259
KF-WSM5-God-MYJ	0.284	0.1477	1.3904	0.5287	0.6197	856	1395	763	4546	0.1685	0.1064	1.5221	0.3638	0.761	271	863	474	5952
KF-WSM5-God-YSU	0.2998	0.1683	1.3162	0.5343	0.5941	865	1266	754	4675	0.1996	0.1388	1.4846	0.4134	0.7215	308	798	437	6017
KF-WSM6-Duh-MYJ	0.2799	0.1571	1.1353	0.467	0.5887	756	1082	863	4859	0.1973	0.1415	1.2242	0.3664	0.7007	273	639	472	6176
KF-WSM6-Duh-YSU	0.3002	0.1833	1.0574	0.475	0.5508	769	943	850	4998	0.211	0.157	1.157	0.3758	0.6752	280	582	465	6233
KF-WSM6-God-MYJ	0.2917	0.155	1.4095	0.5442	0.6139	881	1401	738	4540	0.1788	0.1154	1.6107	0.396	0.7542	295	905	450	5910
KF-WSM6-God-YSU	0.3113	0.1785	1.357	0.5596	0.5876	906	1291	713	4650	0.2101	0.1475	1.6054	0.4523	0.7182	337	859	408	5956

Table 6. Skill scores (TS, ETS, BIAS, POD, and FAR) and hit rates H, 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.

	>25 mm/24 h									>50 mm/24 h
	TS	ETS	BIAS	POD	FAR	H	F	M	T	TS	ETS	BIAS	POD	FAR	H	F	M	T
BMJ-Lin-Duh-MYJ	0.154	0.0672	0.3964	0.1863	0.53	400	451	1747	4962	0.0797	0.0443	0.3412	0.099	0.7098	101	247	919	6293
BMJ-Lin-Duh-YSU	0.1539	0.0595	0.442	0.1924	0.5648	413	536	1734	4877	0.0766	0.0373	0.3912	0.099	0.7469	101	298	919	6242
BMJ-Lin-God-MYJ	0.1755	0.0611	0.5752	0.2352	0.5911	505	730	1642	4683	0.0719	0.0285	0.4471	0.0971	0.7829	99	357	921	6183
BMJ-Lin-God-YSU	0.1893	0.0645	0.653	0.2632	0.597	565	837	1582	4576	0.0832	0.0342	0.5314	0.1176	0.7786	120	422	900	6118
BMJ-WSM3-Duh-MYJ	0.1802	0.0656	0.5771	0.2408	0.5827	517	722	1630	4691	0.0867	0.0391	0.5108	0.1206	0.7639	123	398	897	6142
BMJ-WSM3-Duh-YSU	0.1988	0.0737	0.6572	0.2748	0.5819	590	821	1557	4592	0.0935	0.0407	0.5941	0.1363	0.7706	139	467	881	6073
BMJ-WSM3-God-MYJ	0.2273	0.0777	0.871	0.3465	0.6021	744	1126	1403	4287	0.0992	0.0404	0.7049	0.1539	0.7816	157	562	863	5978
BMJ-WSM3-God-YSU	0.2239	0.0691	0.9171	0.3507	0.6176	753	1216	1394	4197	0.1022	0.0373	0.8294	0.1696	0.7955	173	673	847	5867
BMJ-WSM5-Duh-MYJ	0.1788	0.0634	0.5817	0.2399	0.5877	515	734	1632	4679	0.0944	0.0482	0.4892	0.1284	0.7375	131	368	889	6172
BMJ-WSM5-Duh-YSU	0.2041	0.0796	0.6544	0.2804	0.5715	602	803	1545	4610	0.0966	0.0427	0.6137	0.1422	0.7684	145	481	875	6059
BMJ-WSM5-God-MYJ	0.2169	0.0787	0.7667	0.3149	0.5893	676	970	1471	4443	0.0908	0.037	0.6127	0.1343	0.7808	137	488	883	6052
BMJ-WSM5-God-YSU	0.2278	0.0779	0.8728	0.3475	0.6019	746	1128	1401	4285	0.0947	0.031	0.802	0.1559	0.8056	159	659	861	5881
BMJ-WSM6-Duh-MYJ	0.1712	0.0601	0.5515	0.2268	0.5887	487	697	1660	4716	0.0834	0.0379	0.4775	0.1137	0.7618	116	371	904	6169
BMJ-WSM6-Duh-YSU	0.1971	0.069	0.68	0.2767	0.5932	594	866	1553	4547	0.0992	0.0432	0.652	0.149	0.7714	152	513	868	6027
BMJ-WSM6-God-MYJ	0.2191	0.069	0.8714	0.3363	0.6141	722	1149	1425	4264	0.1	0.0385	0.7578	0.1598	0.7891	163	610	857	5930
BMJ-WSM6-God-YSU	0.215	0.0632	0.885	0.3335	0.6232	716	1184	1431	4229	0.0824	0.0185	0.8039	0.1373	0.8293	140	680	880	5860
KF-Lin-Duh-MYJ	0.2144	0.0871	0.6782	0.2962	0.5632	636	820	1511	4593	0.1162	0.0632	0.601	0.1667	0.7227	170	443	850	6097
KF-Lin-Duh-YSU	0.2063	0.0825	0.6502	0.2823	0.5659	606	790	1541	4623	0.1069	0.052	0.6343	0.1578	0.7512	161	486	859	6054
KF-Lin-God-MYJ	0.2399	0.0805	0.9693	0.381	0.6069	818	1263	1329	4150	0.1392	0.0702	0.9333	0.2363	0.7468	241	711	779	5829
KF-Lin-God-YSU	0.2427	0.0831	0.9725	0.3852	0.6039	827	1261	1320	4152	0.1309	0.0605	0.9647	0.2275	0.7642	232	752	788	5788
KF-WSM3-Duh-MYJ	0.2377	0.0889	0.8677	0.3586	0.5867	770	1093	1377	4320	0.1242	0.0613	0.7922	0.198	0.75	202	606	818	5934
KF-WSM3-Duh-YSU	0.2618	0.1129	0.8812	0.3903	0.5571	838	1054	1309	4359	0.1675	0.0983	0.948	0.2794	0.7053	285	682	735	5858
KF-WSM3-God-MYJ	0.2596	0.0844	1.1495	0.4429	0.6147	951	1517	1196	3896	0.1334	0.0538	1.2235	0.2618	0.7861	267	981	753	5559
KF-WSM3-God-YSU	0.2817	0.1073	1.1593	0.4746	0.5906	1019	1470	1128	3943	0.1631	0.081	1.3216	0.3255	0.7537	332	1016	688	5524
KF-WSM5-Duh-MYJ	0.2508	0.1005	0.8882	0.3787	0.5737	813	1094	1334	4319	0.1333	0.0694	0.8167	0.2137	0.7383	218	615	802	5925
KF-WSM5-Duh-YSU	0.2671	0.1193	0.8738	0.395	0.548	848	1028	1299	4385	0.158	0.0902	0.9118	0.2608	0.714	266	664	754	5876
KF-WSM5-God-MYJ	0.2723	0.0967	1.1635	0.463	0.6021	994	1504	1153	3909	0.1354	0.0571	1.1863	0.2608	0.7802	266	944	754	5596
KF-WSM5-God-YSU	0.2652	0.0932	1.1178	0.4439	0.6029	953	1447	1194	3966	0.1507	0.0709	1.2382	0.2931	0.7633	299	964	721	5576
KF-WSM6-Duh-MYJ	0.2545	0.0952	0.9767	0.401	0.5894	861	1236	1286	4177	0.1237	0.0546	0.9324	0.2127	0.7718	217	734	803	5806
KF-WSM6-Duh-YSU	0.2615	0.1084	0.9208	0.3982	0.5675	855	1122	1292	4291	0.156	0.0841	1.0127	0.2716	0.7318	277	756	743	5784
KF-WSM6-God-MYJ	0.2693	0.0883	1.2259	0.4723	0.6147	1014	1618	1133	3795	0.1398	0.0571	1.3265	0.2853	0.7849	291	1062	729	5478
KF-WSM6-God-YSU	0.2853	0.1072	1.2054	0.4895	0.5939	1051	1537	1096	3876	0.1513	0.0661	1.4245	0.3186	0.7763	325	1128	695	5412

3.2. Skill Score Validation

The charts for the skill scores at the two thresholds in Figures 5 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.

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 Figure 7 for comparison of the change in TSs and ETSs with the cloud microphysics scheme).

For the skill comparisons of different event types (I, II, III, and IV), Figure 8 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.

Table 7. TSs 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.

	>25 mm/24 h				>50 mm/24 h
	Type I	Type II	Type III	Type IV	Type I	Type II	Type III	Type IV
BMJ-Lin-Duh-MYJ	0.2546	0.1309	0.15	0.0682	0.124	0.0791	0.0769	0
BMJ-Lin-Duh-YSU	0.2625	0.1019	0.2346	0.0706	0.1244	0.0692	0.16	0
BMJ-Lin-God-MYJ	0.2644	0.1638	0.2235	0.1011	0.139	0.1064	0.0882	0
BMJ-Lin-God-YSU	0.2761	0.1519	0.1939	0.093	0.1369	0.087	0.1562	0.0333
BMJ-WSM3-Duh-MYJ	0.2503	0.1237	0.1818	0.09	0.1279	0.0621	0.129	0
BMJ-WSM3-Duh-YSU	0.2745	0.1179	0.2439	0.0652	0.1379	0.0699	0.1471	0
BMJ-WSM3-God-MYJ	0.2834	0.1651	0.21	0.09	0.1556	0.1104	0.1136	0
BMJ-WSM3-God-YSU	0.2943	0.1505	0.202	0.0745	0.167	0.0798	0.1304	0.0312
BMJ-WSM5-Duh-MYJ	0.2618	0.1349	0.1977	0.0707	0.1429	0.0563	0.1515	0
BMJ-WSM5-Duh-YSU	0.263	0.1351	0.2073	0.0652	0.1603	0.0789	0.1389	0
BMJ-WSM5-God-MYJ	0.3032	0.1376	0.1978	0.0714	0.1565	0.0496	0.1515	0.0345
BMJ-WSM5-God-YSU	0.3028	0.1525	0.1939	0.0737	0.1677	0.0696	0.15	0.0312
BMJ-WSM6-Duh-MYJ	0.2571	0.133	0.1304	0.0926	0.1403	0.0872	0.0732	0
BMJ-WSM6-Duh-YSU	0.2643	0.1281	0.1828	0.06	0.1714	0.0719	0.1212	0
BMJ-WSM6-God-MYJ	0.2991	0.1781	0.2574	0.0784	0.1706	0.1391	0.15	0.0256
BMJ-WSM6-God-YSU	0.3043	0.1595	0.1944	0.0652	0.1916	0.1104	0.1429	0
KF-Lin-Duh-MYJ	0.2881	0.1673	0.2136	0.1449	0.1844	0.0683	0.1026	0.1818
KF-Lin-Duh-YSU	0.3125	0.1951	0.2474	0.1667	0.1778	0.0698	0.06	0.1892
KF-Lin-God-MYJ	0.2917	0.1914	0.2255	0.1558	0.1725	0.1237	0.0889	0.1471
KF-Lin-God-YSU	0.3129	0.183	0.2653	0.1769	0.1839	0.1162	0.093	0.1316
KF-WSM3-Duh-MYJ	0.2872	0.1776	0.2212	0.1655	0.1893	0.0753	0.1224	0.1667
KF-WSM3-Duh-YSU	0.309	0.1881	0.2551	0.219	0.1858	0.1176	0.1111	0.2059
KF-WSM3-God-MYJ	0.3264	0.1924	0.2526	0.1656	0.2057	0.0909	0.0889	0.1579
KF-WSM3-God-YSU	0.3188	0.1841	0.3069	0.2014	0.2027	0.1185	0.098	0.1795
KF-WSM5-Duh-MYJ	0.3062	0.1836	0.25	0.1655	0.2048	0.0688	0.0851	0.1795
KF-WSM5-Duh-YSU	0.3116	0.1892	0.2653	0.1838	0.1823	0.1111	0.08	0.1818
KF-WSM5-God-MYJ	0.3141	0.1996	0.25	0.1772	0.2047	0.1122	0.1064	0.1622
KF-WSM5-God-YSU	0.3317	0.2025	0.2642	0.1875	0.2063	0.1085	0.1228	0.1579
KF-WSM6-Duh-MYJ	0.3063	0.1823	0.2233	0.1812	0.1963	0.0952	0.0833	0.1538
KF-WSM6-Duh-YSU	0.3339	0.1927	0.2476	0.1898	0.2244	0.1179	0.1071	0.2812
KF-WSM6-God-MYJ	0.3138	0.1781	0.2268	0.1465	0.2207	0.1268	0.1429	0.125
KF-WSM6-God-YSU	0.3323	0.1793	0.2843	0.2	0.2078	0.1255	0.1538	0.1429

Table 8. TSs 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.

	>25 mm/24 h				>50 mm/24 h
	Type I	Type II	Type III	Type IV	Type I	Type II	Type III	Type IV
BMJ-Lin-Duh-MYJ	0.1629	0.2426	0.0854	0.0922	0.0768	0.1533	0.0526	0.0333
BMJ-Lin-Duh-YSU	0.1755	0.2271	0.0909	0.1438	0.0971	0.1559	0.0833	0.0164
BMJ-Lin-God-MYJ	0.2276	0.3104	0.075	0.0798	0.1139	0.1631	0.0333	0.0303
BMJ-Lin-God-YSU	0.2482	0.2723	0.1429	0.12	0.1405	0.1543	0.0385	0.0167
BMJ-WSM3-Duh-MYJ	0.2235	0.3055	0.0833	0.1311	0.1147	0.2848	0.0571	0
BMJ-WSM3-Duh-YSU	0.2365	0.3419	0.1538	0.1617	0.1136	0.3072	0.0556	0.0116
BMJ-WSM3-God-MYJ	0.2583	0.3448	0.0979	0.1029	0.1386	0.2466	0.0182	0
BMJ-WSM3-God-YSU	0.2587	0.3338	0.1176	0.1272	0.1541	0.2125	0.02	0
BMJ-WSM5-Duh-MYJ	0.2301	0.3206	0.0928	0.1228	0.1287	0.2706	0.0741	0.0115
BMJ-WSM5-Duh-YSU	0.243	0.3157	0.1373	0.172	0.1397	0.2813	0.0312	0.0241
BMJ-WSM5-God-MYJ	0.2594	0.344	0.1327	0.1337	0.1479	0.2928	0.0286	0
BMJ-WSM5-God-YSU	0.2588	0.3329	0.1797	0.1364	0.1383	0.2918	0	0.0115
BMJ-WSM6-Duh-MYJ	0.217	0.304	0.1418	0.1027	0.1214	0.2212	0.0784	0.0235
BMJ-WSM6-Duh-YSU	0.2331	0.3216	0.1368	0.1587	0.1348	0.2968	0.0714	0.0103
BMJ-WSM6-God-MYJ	0.2522	0.3055	0.1474	0.0955	0.132	0.2314	0.0541	0.0215
BMJ-WSM6-God-YSU	0.2739	0.3707	0.2	0.1026	0.1333	0.2658	0.0484	0.0319
KF-Lin-Duh-MYJ	0.2259	0.2093	0.0811	0.225	0.1331	0.1208	0.027	0.1899
KF-Lin-Duh-YSU	0.2714	0.172	0.0957	0.2199	0.1347	0.1017	0.0667	0.1395
KF-Lin-God-MYJ	0.268	0.2537	0.1043	0.2771	0.1267	0.1545	0.0385	0.1237
KF-Lin-God-YSU	0.3125	0.2384	0.1058	0.2788	0.1704	0.1608	0.0488	0.1327
KF-WSM3-Duh-MYJ	0.2689	0.2805	0.1298	0.2783	0.156	0.241	0.0577	0.1327
KF-WSM3-Duh-YSU	0.3066	0.2739	0.069	0.285	0.1971	0.2068	0.0444	0.2088
KF-WSM3-God-MYJ	0.2945	0.2976	0.1286	0.2595	0.1651	0.2451	0.0526	0.1111
KF-WSM3-God-YSU	0.3202	0.2956	0.1407	0.2822	0.1872	0.2136	0.0923	0.1569
KF-WSM5-Duh-MYJ	0.2924	0.2921	0.1176	0.2546	0.1586	0.238	0.0727	0.1765
KF-WSM5-Duh-YSU	0.3217	0.2847	0.0976	0.26	0.1995	0.2403	0.0755	0.1753
KF-WSM5-God-MYJ	0.2946	0.2953	0.1192	0.2672	0.1499	0.25	0.038	0.1293
KF-WSM5-God-YSU	0.3265	0.2768	0.1361	0.2881	0.201	0.2344	0.0959	0.1404
KF-WSM6-Duh-MYJ	0.2922	0.2874	0.1389	0.2597	0.1874	0.25	0.0685	0.1667
KF-WSM6-Duh-YSU	0.3291	0.2706	0.1311	0.2995	0.208	0.2378	0.0893	0.2083
KF-WSM6-God-MYJ	0.3002	0.3115	0.1465	0.2556	0.1675	0.2537	0.0435	0.1333
KF-WSM6-God-YSU	0.3426	0.2798	0.1595	0.2888	0.2091	0.2565	0.0674	0.177

4. Conclusions

For 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.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This paper describes the results of the National KC.08.06/16-20 Project “Developing an operational heavy rainfall forecast system for the northern part of Vietnam” funded by the Vietnamese Ministry of Science and Technology and the Project VT-CN.04/17-20 funded by the National Program on Space Science and Technology, Vietnam Academy of Science and Technology. L. R. Hole was sponsored by the Norwegian Agency for Development Cooperation and the Norwegian Ministry of Foreign Affairs. The authors would like to thank Ms. Huyen Khanh Luu for her data preparations.

Open Research

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

1 Phan V. T., Ngo-Duc T., and Ho T. M. H., Seasonal and interannual variations of surface climate elements over Vietnam, Climate Research. (2009) 40, 49–60, https://doi.org/10.3354/cr00824, 2-s2.0-70349590331.
10.3354/cr00824
Web of Science® Google Scholar
2 Tran V. T., Variation of some atmospheric circulation factors affecting Vietnam climate, Earth Sciences. (2011) 27, no. 2, 107–111.
Google Scholar
3 Ngo-Duc T., Kieu C., Thatcher M., Nguyen-Le D., and Phan-Van T., Climate projections for Vietnam based on regional climate models, Climate Research. (2014) 60, no. 3, 199–213, https://doi.org/10.3354/cr01234, 2-s2.0-84905987322.
10.3354/cr01234
Web of Science® Google Scholar
4 Nguyen D.-Q., Renwick J., and McGregor J., Variations of surface temperature and rainfall in Vietnam from 1971 to 2010, International Journal of Climatology. (2014) 34, no. 1, 249–264, https://doi.org/10.1002/joc.3684, 2-s2.0-84891746535.
10.1002/joc.3684
Web of Science® Google Scholar
5 Nguyen K. C., Katzfey J. J., and McGregor J. L., Downscaling over Vietnam using the stretched-grid CCAM: verification of the mean and interannual variability of rainfall, Climate Dynamics. (2014) 43, no. 3-4, 861–879, https://doi.org/10.1007/s00382-013-1976-5, 2-s2.0-84905103265.
10.1007/s00382-013-1976-5
Web of Science® Google Scholar
6 Yokoi S. and Matsumoto J., Collaborative effects of cold surge and tropical depression-type disturbance on heavy rainfall in central Vietnam, Monthly Weather Review. (2008) 136, no. 9, 3275–3287, https://doi.org/10.1175/2008mwr2456.1, 2-s2.0-53649099054.
10.1175/2008MWR2456.1
Web of Science® Google Scholar
7 Chen T.-C., Yen M.-C., Tsay J.-D., Tan Thanh N. T., and Alpert J., Synoptic development of the Hanoi heavy rainfall event of 30-31 october 2008: multiple-scale processes, Monthly Weather Review. (2012) 140, no. 4, 1219–1240, https://doi.org/10.1175/MWR-D-11-00111.1, 2-s2.0-84861539786.
10.1175/MWR-D-11-00111.1
Web of Science® Google Scholar
8 Linden R. V. D., Fink A. H., Pinto J. G., and Phan-Van T., The dynamics of an extreme precipitation event in northeastern Vietnam in 2015 and its predictability in the ECMWF ensemble prediction system, Weather and Forecasting. (2017) 32, 1041–1056.
10.1175/WAF-D-16-0142.1
Web of Science® Google Scholar
9 Härter F. P., Silveira R. B., and Bonatti G. R., An assessment of the DFI on DWD-HRM, Far East Journal of Applied Mathematics. (2010) 48, 25–38.
Google Scholar
10 Majewski D., Contributions of the DWD/Germany to SWFDP in Southeast Asia, Proceedings of the Meeting the Regional Subproject Management Team (RSMT) of the Severe Weather Forecasting Demonstration Project (SWFDP) in Southeast Asia, November 2017, Colombo, Sri Lanka, World Meteorological Organization, https://www.wmo.int/pages/prog/www/DPFS/Meetings/RAII-SeA-SWFDP-RSMT_Hanoi2017/documents/Doc-4-1-5-DWD_Majewski.doc.
Google Scholar
11 Majewski D., Contributions of the Germany to SWFDP in Southeast Asia, Proceedings of the World Meteorological Organization meeting Regional Subproject Management Team in the Severe Weather Forecasting Demonstration Project (SWFDP) for Southeast Asia, August 2015, Viet Nam, Ha Noi, https://www.wmo.int/pages/prog/www/DPFS/Meetings/RAII-SeA-SWFDP-RSMT_HaNoi2015/linkedfiles/Doc-5-1-DWD_Germany.docx.
Google Scholar
12 Tien D. D., Lars robert hole, duc tran anh, cuong Hoang duc, and thuy nguyen Ba, verification of forecast weather surface variables over Vietnam using the national numerical weather prediction system, Advances in Meteorology. (2016) 2016, 11, 8152413, https://doi.org/10.1155/2016/8152413, 2-s2.0-84975230219.
10.1155/2016/8152413
Web of Science® Google Scholar
13 Lorenz E. N., Atmospheric predictability as revealed by naturally occurring analogues, Journal of the Atmospheric Sciences. (1969) 26, no. 4, 636–646, https://doi.org/10.1175/1520-0469(1969)26<636:aparbn>2.0.co;2.
10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2
Web of Science® Google Scholar
14 Pérez-Bello A., Mitrani-Arenal I., Díaz-Rodríguez O. O., Wettre C., and Hole L. R., A numerical prediction system combining ocean, waves and atmosphere models in the inter-American Seas and Cuba, Revista Cubana de Meteorología. (2019) 25, no. 1.
Google Scholar
15 Zeyaeyan S., Fattahi E., Ranjbar A., Azadi M., and Vazifedoust M., Evaluating the effect of physics schemes in WRF simulations of summer rainfall in north west Iran, Climate. (2017) 5, no. 3, https://doi.org/10.3390/cli5030048, 2-s2.0-85027435915.
10.3390/cli5030048
PubMed Google Scholar
16 Tan E., Microphysics parameterization sensitivity of the WRF model version 3.1.7 to extreme precipitation: evaluation of the 1997 new year’s flood of California, Geoscientific Model Development Discussions. (2016) 1–29.
Google Scholar
17 Nasrollahi N., AghaKouchak A., Li J., Gao X., Hsu K., and Sorooshian S., Assessing the impacts of different WRF precipitation physics in hurricane simulations, Weather and Forecasting. (2012) 27, no. 4, 1003–1016, https://doi.org/10.1175/waf-d-10-05000.1, 2-s2.0-84868315987.
10.1175/WAF-D-10-05000.1
Web of Science® Google Scholar
18 Pattanayak S., Mohanty U. C., and Osuri K. K., Impact of parameterization of physical processes on simulation of track and intensity of tropical cyclone nargis (2008) with WRF-NMM model, The Scientific World Journal. (2012) 2012, 18, 671437, https://doi.org/10.1100/2012/671437, 2-s2.0-84863754888.
10.1100/2012/671437
Web of Science® Google Scholar
19 Gilliland E. and Rowe C., A comparison of cumulus parameterization scheme in the WRF model, Proceedings of the 21st AMS Conference on Hydrology, October 2007, San Antonio, TX, USA, https://ams.confex.com/ams/pdfpapers/120591.pdf.
Google Scholar
20 Kieu C. Q., Truong N. M., Mai H. T., and Ngo-Duc T., Sensitivity of the track and intensity forecasts of typhoon megi (2010) to satellite-derived atmospheric motion vectors with the ensemble kalman filter, Journal of Atmospheric and Oceanic Technology. (2012) 29, no. 12, 1794–1810, https://doi.org/10.1175/jtech-d-12-00020.1, 2-s2.0-84872260455.
10.1175/JTECH-D-12-00020.1
Web of Science® Google Scholar
21 Kieu C., Minh P. T., and Mai H. T., An application of the multi-physics ensemble kalman filter to typhoon forecast, Pure and Applied Geophysics. (2013) 171, no. 7, 1473–1497, https://doi.org/10.1007/s00024-013-0681-y, 2-s2.0-84905706907.
10.1007/s00024-013-0681-y
Web of Science® Google Scholar
22 Skamarock W., Klemp J., Dudhia J. et al., A description of the advanced research WRF version 3, NCAR Technical Note. (2008) 475.
Google Scholar
23 Chen S.-H. and Sun W.-Y., A one-dimensional time dependent cloud model, Journal of the Meteorological Society of Japan. Ser. II. (2002) 80, no. 1, 99–118, https://doi.org/10.2151/jmsj.80.99, 2-s2.0-0036463560.
10.2151/jmsj.80.99
Web of Science® Google Scholar
24 Chou M.-D. and Suarez M. J., An efficient thermal infrared radiation parameterization for use in general circulation models, NASA Technical Memorandum No. 104606. (1994) 3.
Google Scholar
25 Dudhia J., Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, Journal of the Atmospheric Sciences. (1989) 46, no. 20, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:nsocod>2.0.co;2.
10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
Web of Science® Google Scholar
26 Hong S.-Y., Noh Y., and Dudhia J., A new vertical diffusion package with an explicit treatment of entrainment processes, Monthly Weather Review. (2006) 134, no. 9, 2318–2341, https://doi.org/10.1175/mwr3199.1, 2-s2.0-33749016495.
10.1175/MWR3199.1
Web of Science® Google Scholar
27 Hong S.-Y., Dudhia J., and Chen S.-H., A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation, Monthly Weather Review. (2004) 132, no. 1, 103–120, https://doi.org/10.1175/1520-0493(2004)132<0103:aratim>2.0.co;2.
10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
Web of Science® Google Scholar
28 Janjic Z. I., The Step–Mountain Eta Coordinate Model: further developments of the convection, viscous sublayer, and turbulence closure schemes, Monthly Weather Review. (1994) 122, no. 5, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:tsmecm>2.0.co;2.
10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2
Web of Science® Google Scholar
29 Kain J. S., The kain-fritsch convective parameterization: an update, Journal of Applied Meteorology. (2004) 43, no. 1, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:tkcpau>2.0.co;2.
10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
Web of Science® Google Scholar
30 Mlawer E. J., Taubman S. J., Brown P. D., Iacono M. J., and Clough S. A., Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, Journal of Geophysical Research: Atmospheres. (1997) 102, no. D14, 16663–16682, https://doi.org/10.1029/97jd00237.
10.1029/97JD00237
CAS Web of Science® Google Scholar
31 Wilks D. S., Statistical Methods in the Atmospheric Sciences, 2006, 2nd edition, Elsevier Academic Press, New York, NY, USA.
Google Scholar
32 Fujita T., Stensrud D. J., and Dowell D. C., Surface data assimilation using an ensemble kalman filter approach with initial condition and model physics uncertainties, Monthly Weather Review. (2007) 135, no. 5, 1846–1868, https://doi.org/10.1175/mwr3391.1, 2-s2.0-34249707809.
10.1175/MWR3391.1
Web of Science® Google Scholar
33 Hong S.-Y. and Lim J. O. J., The WRF single–moment 6–class microphysics scheme (WSM6), Journal of the Korean Meteorological Society. (2006) 42, 129–151.
Google Scholar
34 Meng Z. and Zhang F., Tests of an ensemble kalman filter for mesoscale and regional-scale data assimilation. Part II: imperfect model experiments, Monthly Weather Review. (2007) 135, no. 4, 1403–1423, https://doi.org/10.1175/mwr3352.1, 2-s2.0-34248364831.
10.1175/MWR3352.1
Web of Science® Google Scholar
35 UCAR, NCAR, 2019.
Google Scholar
36 The NCEP Products Inventory 2019.
Google Scholar
37 The HRM’s Model User Guide 2019.
Google Scholar

Citing Literature

All articles

Impacts of Different Physical Parameterization Configurations on Widespread Heavy Rain Forecast over the Northern Area of Vietnam in WRF-ARW Model

Abstract

1. Introduction