Advances in Meteorology
Volume 2019 (2019), Article ID 1010858, 24 pages
https://doi.org/10.1155/2019/1010858

Research Article

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

Tien Du Duc,¹ Cuong Hoang Duc,¹ Lars Robert Hole,² Lam Hoang,¹ Huyen Luong Thi Thanh,¹ and Hung Mai Khanh¹

¹Viet Nam National Center for Hydro-Meteorological Forecasting-NCHMF, 8 Phao Dai Lang Str., Hanoi, Vietnam
²Norwegian Meteorological Institute, Bergen, Norway

Correspondence should be addressed to Tien Du Duc; duductien@gmail.com

Received 29 March 2019; Revised 12 July 2019; Accepted 18 July 2019; Published 18 August 2019

Academic Editor: Pedro Jiménez-Guerrero

Copyright © 2019 Tien Du Duc et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 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)).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

Figure 1: 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.

Figure 2: SYNOP 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.

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:

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

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.

(a)

(b)

(c)

Figure 3: Histogram 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.

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.

(a)

(b)

Figure 4: The 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.

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.

(a)

(b)

(c)

Figure 5: Skill 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.

(a)

(b)

(c)

Figure 6: Skill 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.

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).

Figure 7: Brief 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), 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.

(a)

(b)

(c)

(d)

Figure 8: TSs 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.

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.

Data Availability

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

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.

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