Simulation of an unseasonal heavy rainfall event over southern Thailand using the Weather Research and Forecasting (WRF) model

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1 Simulation of an unseasonal heavy rainfall event over southern Thailand using the Weather Research and Forecasting (WRF) model Pramet Kaewmesri and Usa Humphries* Department of Mathematics, Faculty of Science, King Mongkut s University of Technology Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang mod, Thung Khru, Bangkok 10140, Thailand *Corresponding Author: Usa Humphries Abstract: The objective of the study was to consider sensitivity lead times initial conditions, physics parameterisation and Sea Surface Temperature (SST) boundary condition by using the Weather Research and Forecasting (WRF) model in unseasonal high-resolution simulation. In case of unseasonal heavy rainfall, this study was focused on March 28, The lead times initial condition depended on 0000UTC March 25, 2011, 0000UTC March 26, 2011 and 0000UTC March 27, In spatial difference of rainfall distribution, the 0000UTC March 26, 2011 lead time of initial condition was shown good spatial difference rainfall performance than other lead time. In statistical, the EXP 8 simulated better heavy rainfall area over Samui Island ( cm/day), the second lower RMSE (6.10), the first lower MAE (4.89) and the first highest value (CORR (0.80) than the other EXP. The Ensemble technique was shown the first lower RMSE (5.94), the second lower MAE (5.19) and the second higher CORR (0.78). In case of physics parameterisation the Lin scheme was shown a good performance than WSM6, especially EXP8. However, this study recommends, in lead time initial condition should be more than one day of the simulation rainfall event for the short-term case. Furthermore, Therefore, in the unseasonal regional rainfall simulation should be included the SST boundary condition. Keywords: Weather Research and Forecasting, Heavy rainfall, Unseasonal 1. Introduction Southern Thailand is in the tropical area and lies between longitude 97 east to longitude 104 east and latitudes 5 north to 12 north. The topography of this area is the peninsula between the Gulf of Thailand, which is on the east side and the west side which connects the Andaman Sea. Southern Thailand separates this in the two regions, Southern Thailand East Coast and Southern Thailand West Coast, as shown in Figure 1. Since southern Thailand is under the influence of the monsoon seasonal wind, especially in northeast monsoon wind. Therefore the effect of the seasonal wind can be divided into three seasonal every annual year. Therefore, this area has a unique characteristic of several of weather and different weather from another part of Thailand. In every annual year, rainfall is the one-factor characteristic effecting of weather and climate in southern Thailand. This area has many rainfalls almost all seasons, especially in the winter season and rainy season was followed by Thai Meteorological Department (Thai Meteorological Department., 2015) information. In the winter season, the northeast monsoon is influenced wind during middle October to middle February in every annual year. This period occurs annually heavy rainfall over southern Thailand. A cold surge occurs in the northeast monsoon. It has crossed the South China Sea and flows to the cyclonic circulation over east southern Thailand and the northern Malay Peninsula, this reason might be a main factor occurrence heavy rainfall in this area (Wangwongchai et al., 2005; Kirtsaeng et al., 2012; Kaewmesri et al., 2017a). In the rainy season, the southwest monsoon is influenced wind during middle May to middle October in every annual year. Since the wind blows the wet air from the Indian Ocean to the land. So this period occurs annually rainfall almost all parts of Thailand. But in the southern and east coast Thailand was

2 abundant rainfall remains until the end of the year (Kaewmesri et al., 2017b; Thai Meteorological Department., 2015). In the summer season, this is the transitional period from the northeast to the southwest monsoon. This weather in this period is very warmer over northern, northeastern, and western and central parts of Thailand (Kaewmesri et al., 2017b; Thai Meteorological Department., 2015). Table 1 was shown the seasonal rainfall and temperature of the west (east) of southern Thailand during period. Figure 1 The domain of southern Thailand, the orange color is southern Thailand east coast and the light green color is the southern west coast. From table 1, the rainfall in winter and rainy seasons more than the summer season. But in 2011 was different. Since 2011 in Thailand was the wettest year during (61-years). From Thai Meteorological Department (Thai Meteorological Department., 2011) information mainly from widespread rainfall in Thailand, especially during summer season (369% above normal rainfall in Mar 2011). The annual rainfall was about 24% above normal rainfall and 19% higher than the previous year. Figure 2 was shown the monthly and annual rainfall anomalies (%) in Figure 3 was shown the monthly and annual mean temperature anomalies (Degree Celsius) in Table 1 Seasonal rainfall (mm) and temperature (Degree Celsius) of west southern Thailand from TMD (Based on ) East Coast West Coast Mean Winter Summer Rainy Winter Summer Rainy Rainfall ,841.0 Temperature Max Temperature Min Temperature

3 The southern Thailand is one part which effects from this event that was experienced severe floods in several provinces in March The spatial heavy rainfall distribution event during from the Tropical Rainfall Measuring Mission (TRMM) satellite, shows rainfall for March 23 30, The accumulated rainfall range from 200 millimeters (20 centimeters) to more than 1,200 millimeters (120 centimeters) across southern Thailand with the Malay Peninsula. Surat Thani province in southern Thailand was measured the most rain immediately by TRMM, as shown in Figure 4. This event flooded 8 provinces, killing 13 and affecting 842,324 people as of April 1, 2011 (NASA-Earth Observation., 2014; Loo et al., 2015). In this study was used rainfall observation data by Thai Meteorological Department (TMD). Figure 5 shows the accumulated rainfall (shaded color) of March 23 30, 2011 from TMD with the location of 23 rainfall observation stations and Table 2 described detail of stations. The TMD observation (Figure 5) was shown the rainfall pattern and can measure heavy rainfall area over this area. The TMD recorded more than 100 centimeters in three stations: Samui Island station centimeters, Surat Thani Meteorological station centimeters and Nakhon Si Thammarat station centimeters. TMD can capture the heavy rainfall event in Surat Thani province similarly the TRMM. So this reason is support TMD observation data that can evaluate to compare with the results of model simulations. Figure 2. The Monthly and annual rainfall anomalies (%) in 2011 (Modified from Thai Metrological Department., 2011).

4 Figure 3. The monthly and mean temperature anomalies (Degree Celsius) in 2011 (Modified from Thai Metrological Department., 2011). Figure 4. The accumulated rainfall of March 23 30, 2011 in Southern Thailand, resulting a heavy flooding event (Reference from NASA-Earth Observation., 2014; Loo et al., 2015).

5 Figure 5 The accumulate rainfall (shaded color) of March 23 30, 2011 from TMD and red rectangular is rainfall station in Southern Thailand. This study was focused on the date of heavy rainfall during March 23 30, Thai Meteorological Department (TMD) observation data was recorded average rainfall in 23 (21.81 millimeters (2.181 centimeters)), 24 (41.07 millimeters (4.107 centimeters)), 25 (61.20 millimeters (6.120 centimeters)), 26 (63.71 millimeters (6.371 centimeters)), 27 (16.68 millimeters (1.668 centimeters)), 28 ( millimeters ( centimeters)), 29 (82.91 millimeters (8.291 centimeters)) and 30 (45.62 millimeters (4.562 centimeters)) over southern Thailand. March 28, 2011 was shown the highest average rainfall and measure the highest rainfall station at the Samui Island station ( millimeters (41.47 centimeters)), as shown figure 6. Therefore, this study was interested in the heavy rainfall event by using the Weather Research and Forecasting high-resolution model on March 28, The Weather Research and Forecasting (WRF) model is developed at the US National Centre for Atmospheric Research (NCAR), the Nation Centre for Environmental Prediction (NCEP) and the National Oceanic and Atmospheric Administration (NOAA), etc. This model is based on the Numerical Weather Prediction (NWP) and can be applied high-resolution configurations to resolve and understand the mechanisms responsible for heavy rainfall (Efstathiou et al., 2012; Kirtsaeng et al., 2012; Pennelly et al., 2014; Keawmesri et al., 2017b). In research of atmospheric, there have been few studies on the simulated heavy rainfall over Thailand in terms of uncertainties associated with the lead time of initial condition, physics parameterisations schemes and Sea Surface Temperature (SST) in high-resolution models. Many research was focused on the WRF simulation standalone over their area. Gamal et al (2013) used the WRF model to simulate heavy rainfall events in flash flood that occurred on January 18, 2010 over the Sinai Peninsula. Jee and Sangil, (2017) examined the sensitivity of heavy rainfall to domain size, lead time of initial condition and SST over Seoul in South Korea. Efstathiou et al., (2012) examine the impact of two different

6 microphysics parameterisation schemes in the simulation of a heavy rainfall event over Chalkidiki peninsula in northern Greece. Kirtsaeng et al., (2010) examine the cumulus convective schemes and lead time initial condition to simulate heavy rainfall over Mumbai. However, the summarized aim of this study as follows: (a) Sensitivity lead time of initial condition and physics parameterisations schemes to simulate the heavy rainfall unseasonal event over Southern Thailand (b) To compare the results between increase SST data and without SST data with the amount from the TMD observation data. Figure 6 Daily rainfall on March 23 (blue line), 24 (red line), 25 (light green line), 26 (light purple line), 27 (light blue line), 28 (orange line), 29 (blue line), 30 (purple line) and accumulated rainfall during (green line) March Methodology The high-resolution model used in this study is the Weather Research and Forecasting (WRF) model (version 3.6.0), which has been available since. The WRF model is famous and powerful dynamic atmospheric model. This model base on non-hydrostatic primitive equation model, Arakawa C-grid staggering for horizontal grids, sigma coordinates for vertical level grids, three dimensional real-data for initial condition, multiple level and integer ration for nesting option, Time-split integration using a 3-order Runge-Kutta method with small time step for acoustic and gravity-wave mode and with multiple options of physical parameterization schemes. In this research, the multiple three nested domain configurations included 36-km resolution outer domain, subdomain resolution was 12-km and 4-km resolution inner domain (the ratio of 1:3), with , and grid points, respectively (Figure 7). The inner domain was covering the southern Thailand and the Gulf of Thailand location between longitudes East and East

7 and latitudes North to North. The 27 levels direction with a maximum of 50 hpa is used for all domains. Table 2 The detail of 23 TMD rainfall observation station No Station id LONG LAT Station Name Prachuap Khiri Khan Chumpon Sawi Meterological station Ranong Surat Thani Samui Island Surat Thani Meterological station Nakhon Si Thammarat Nakhon Si Thammarat Meterological station Phatthalung Meterological station Takuapa Phuket Phuket Airport Lanta Island Krabi Trang Airport Khor Hong Ago Meteorological station Sadao Songkhla Hat Yai Airport Satun Pattani Airport Yala Meterological The sensitivity of different dynamic and physical option used in the high-resolution. In terms of microphysics parameterisation schemes, the two schemes used in this study were Purdue Lin (Lin et al. 1983) and WSM6 (Hong and Lim, 2006) schemes. The Purdue Lin scheme is six classes of hydrometeors. The hydrometeors are contained water vapor, cloud water, rain, cloud ice, snow, and graupel. This scheme is a relatively complicated microphysics scheme in WRF, and it is most appropriate for use in research studies. The scheme is taken from the Purdue cloud model, and the details can be found in Chen and Sun (2002). The WSM6 scheme is six classes of hydrometeors similarly Purdue Lin scheme. This scheme was developed from Purdue Lin scheme (Lin et al. 1983). But they used ice-phase from Hong et al. (2004) and a new mixed-phase particle fall speeds for the snow and graupel particles by assigning a single fall speed to both that is weighted by the mixing ratios, applying that fall speed to both sedimentation and accretion processes is introduced (Dudhia et al., 2008). The fixed another physics options used in this study consisted the Yonsei University planetary boundary layer (YSU) scheme for the Planetary Boundary Layer (PBL) (Hong et al., 2005), the Rapid Radiative Transfer Model (RRTM) scheme for the long wave radiation (Mlawer et al., 1997), the Dudhia scheme for the short wave radiation (Dudhia, 1989), the Kain Frisch scheme for the cumulus parameterisation scheme (Janjic, 1994) and the Noah Land-Surface Model for the land surface scheme (Tewari et al., 2004).

8 Figure 7. The domain configurations used an outer domain grid resolution of 36-km, subdomain grid resolution of 12-km and inner domain grid resolution 4-km. The Modis 20-category Land Use data was used in the terrestrial data for the terrain. For example the land use data in Modis 20-category that by mixed forest, water, snow, ice, permanent wetland and etc. This data was downloaded from the website by WRF user (WRFUSERSPAGE, 2017). The initial and boundary condition is obtained from National Center for Environmental Prediction (NCEP) Final Operational Global Analysis Data (NCEP-FNL). The resolution of this data was 1 degrees and 6- hourly intervals for standalone WRF. In case WRF increases SST data, the boundary condition is obtained the NCEP-FNL data and Real-Time, Global, Sea Surface Temperature (RTG_SST) data. RTG_SST has been developed at the National Centers for Environmental Prediction/Marine Modeling and Analysis Branch (NCEP / MMAB). The resolution of this data was 0.5 degrees and 24-hourly intervals. This study was provided three lead times of initial conditions. The first lead time, an initial condition obtains at 0000 UTC on March 25, 2011, the second lead time, an initial condition obtains at 0000 UTC on March 26, 2011 and the third lead time, and an initial condition obtains at 0000 UTC on March 27, Table 3 were summaries all of the design of the experiment in this research. The statistical methods were calculated using the Correlation Coefficient (CORR), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as given below: CORR RMSE n n xi x oi o i1 n 2 2 xi x oi o i1 i1 n i1 x o 2 i n i,,

9 n 1 MAE x o. n i 1 i i where x is defined as the simulation variable, i o is defined as the observation variable, i n is defined as the number of pair of observations and simulation values, i pair of observations and simulation values. In this study was used the CORR, RMSE and MAE to compare and validate performance simulation accuracy with observation data. Table 3. The summaries experiment design. EXP Start-time End-time Microphysics SST EXP UTC March 25, UTC March 29, 2011 Lin scheme N/A EXP UTC March 26, UTC March 29, 2011 Lin scheme N/A EXP UTC March 27, UTC March 29, 2011 Lin scheme N/A EXP UTC March 25, UTC March 29, 2011 WSM6 scheme N/A EXP UTC March 26, UTC March 29, 2011 WSM6 scheme N/A EXP UTC March 27, UTC March 29, 2011 WSM6 scheme N/A EXP UTC March 25, UTC March 29, 2011 Lin scheme RTG-SST EXP UTC March 26, UTC March 29, 2011 Lin scheme RTG-SST EXP UTC March 27, UTC March 29, 2011 Lin scheme RTG-SST EXP UTC March 25, UTC March 29, 2011 WSM6 scheme RTG-SST EXP UTC March 26, UTC March 29, 2011 WSM6 scheme RTG-SST EXP UTC March 27, UTC March 29, 2011 WSM6 scheme RTG-SST Figure 8 was show step of the sensitivity simulation in this study. The First step was created the domain configuration. The grid points of domain 1, domain 2 and domain 3 have , and grid points, respectively. The second step, to prepare the initial data for simulation. The initial condition was used in this simulation that is National Climate for Environment Prediction Final Operational Global Analysis data or (NCEP-FNL). The third step, to prepare the boundary data for simulation in case WRF increase Sea Surface Temperature (SST). Fourth step, to select the physics parameterisation scheme. But in this study was focused on the high resolution unseasonal heavy rainfall case. The Lin and WSM6 scheme use of simulation in sensitivity simulation in this study. The fifth step was the run model process by using three initial conditions. In this study was used 0000 UTC March 25, 2011 for the first initial condition, 0000 UTC March 26, 2011 for second initial condition and 0000 UTC March 27, 2011 for the third initial condition, respectively. The simulation of three cases was complete. The results from three case simulation were compared with the TMD station data. Three statistics method was used in this study. That is CORR, RMSE and MAE respectively, that was shown in six steps. The last step was post-processing. This step was summary and discuss the results of high resolution unseasonal heavy rainfall over southern Thailand.

10 Figure 8. The flow chart of the sensitivity simulation in this study. 3. Results The spatial difference of 24-hour accumulate rainfall distribution from TMD station valid on March 28, In case of simulation without SST boundary condition (EXP1, EXP2 and EXP3) were based on the Lin scheme to simulate rainfall with difference lead time initial condition. The pattern of the EXP1 shown match reasonably with TMD station. However, the magnitude of rainfall was overestimated the rainfall by about 2-6 cm over Chumphon province and underestimate the rainfall less than -10 cm over the heavy rainfall area that are Songkhla, Trang and Surat Thani provinces as shown in Figure 9(a). The EXP2 (Figure 9(b)) was shown a smooth pattern of rainfall over Chumphon province and capture area of heavy rainfall than EXP1. However, it overestimated the rainfall by more than 10 cm over Ranong, Phuket and Phangnga province and was underestimate rainfall less than -10 cm over Trang and eastern of Nakhon Si Thammarat provinces. Figure 9(c) shown results from EXP3. It showed good performance over heavy rainfall. But, the spatial difference rainfall of EXP3 was more overestimate rainfall over Ranong and Chumphon province.

11 (a) (b) (c) Figure 9. The different spatial patterns rainfall (cm/day) between the simulated between WRF and TMD from (a) EXP1, (b) EXP2 and (c) EXP3 on March 28, The spatial difference of 24-hour accumulate rainfall distribution from TMD station valid on March 28, In case of simulation without SST boundary condition (EXP4, EXP5 and EXP6) were based on the WSM6 scheme to simulate rainfall with difference lead time initial condition. The EXP4 (Figure 10(a)) was underestimated rainfall less than -10 cm over heavy rainfall area that is included Nakhon Si Thammarat, Trang and Surat Thani provinces. In EXP5, it was shown good pattern rainfall distribution over Chumphon province and capture area of heavy rainfall than EXP4. But, it was overestimated rainfall distribution by more than 10 cm over Ranong, Phuket and Phangnga province and was underestimate rainfall distribution less than -10 cm over Surat Thani and Trang provinces, as shown in Figure 10(b).

12 The EXP6 was overestimated rainfall distribution over Ranong and Chumphon province and was underestimate rainfall less than -10 cm over Surat Thani, Nakhon Si Thammarat, Phatthalung, Trang, Krabi and Songkhla. Figure 10. The different spatial patterns rainfall (cm/day) between the simulated between WRF and TMD from (a) EXP4, (b) EXP5 and (c) EXP6 on March 28, The spatial difference of 24-hour accumulate rainfall distribution from TMD station valid on March 28, In case simulation with SST boundary condition (EXP7, EXP8 and EXP9) were based on the Lin scheme to simulate rainfall with difference lead time initial condition. The spatial difference of EXP7 shown match reasonably with TMD station. However, it underestimated rainfall less than -10 cm over the heavy rainfall area, especially over Songkhla, Trang and Surat Thani provinces as shown in Figure 11(a).

13 The EXP8 (Figure 11(b)) showed a smooth pattern of rainfall over an area of heavy rainfall than EXP7. However, it has to overestimate rainfall by more than 10 cm over Ranong, Phuket and Phangnga provinces. Figure 11(c) was shown results from EXP9. It was shown overestimate rainfall over Ranong and Chumphon provinces and underestimate rainfall over Trang less than -10 cm. (a) (b) (c) Figure 11. The different spatial patterns rainfall (cm/day) between the simulated between WRF and TMD from (a) EXP7, (b) EXP8 and (c) EXP9 on March 28, The spatial difference of 24-hour accumulate rainfall distribution from TMD station valid on March 28, In case simulation with SST boundary condition (EXP10, EXP11 and EXP12) were based on the WSM6 scheme to simulate rainfall with difference lead time initial condition. The EXP10 was overestimated the rainfall by about 2-6 cm over Chumphon province and underestimated the rainfall less

14 than -10 cm over the heavy rainfall area that are Songkhla, Trang and Surat Thani provinces as shown in Figure 12(a). The EXP11 was show smooth pattern of rainfall over Chumphon province and capture area of heavy rainfall than EXP11. However, it was overestimating rainfall by more than 10 cm over Ranong, Phuket and Phangnga provinces, as shown in Figure 12(b). Figure 12(c) was shown results from EXP12. It was shown overestimate rainfall over Ranong and Chumphon province and underestimate rainfall less than -10 cm over Surat Thani, Nakhon Si Thammarat, Phatthalung, Trang, Krabi and Songkhla. (a) (b) (c) Figure 12. The deferent spatial patterns rainfall (cm/day) between the simulated between WRF and TMD from (a) EXP10, (b) EXP11 and (c) EXP12 on March 28, The Ensemble method was difference rainfall spatial pattern simulated 24-hour accumulated rainfall between Ensemble mean and TMD station valid on March 28, Overall of twelve EXP was

15 shown by Ensemble method. It has overestimated the rainfall by about 2-8 cm over Phangnga province and underestimated the rainfall less than -10 cm over the heavy rainfall area that are Songkhla, Trang and Surat Thani provinces as shown in Figure 13. Figure 13. The different spatial patterns rainfall (cm/day) between the simulated between WRF and TMD from Ensemble mean on March 28, The different spatial pattern rainfall was shown between all EXP and TMD station. The 0000UTC March 26, 2011 lead time of initial condition was shown good spatial difference rainfall performance and can capture difference rainfall over heavy season area than 0000UTC March 25, 2011 and 0000UTC March 27, 2011 as shown in Figure 9, 10, 11, 12 and 13. However, the spatial difference rainfall pattern wasn t to confirm the accuracy of the model and station data. Next step, to use the statistical method (CORR, RMSE and MAE) analyze accuracy between the results from the model with TMD data. To accuracy the ability of all EXP to produce over Southern Thailand, heavy rainfall over Samui Island, average accumulated rainfall, Correlation Coefficient, Root Mean Square Error and Mean Absolute Error values are shown in Table 4. In heavy rainfall case, the EXP 1, 3, 5 and 7 were shown overestimated heavy rainfall over Samui Island. But EXP 2, 4, 6, 8, 9, 10, 11 and 12 were shown underestimated heavy rainfall over Samui Island. However, the EXP 1 ( cm/day), 7 ( cm/day) and 8 ( cm/day) were shown good performance closely the Samui Island station. In average accumulated rainfall case, the EXP8 was shown closely value at cm/day that was a better value than another value. The RMSE was compared with the TMD station data. The Ensemble mean supported the lowest RMSE value at 5.94, with the EXP8 the next closest at The highest RMSE value at 9.09 was supported by EXP12. For MAE, the EXP8 shown the lowest MAE values at 4.89, with the Ensemble mean the next lowest at The highest MAE value at 8.33 was shown by EXP6. In case of CORR, the highest value was shown by EXP8 at 0.80 and the next highest value was shown by the Ensemble mean at On the other hand, the lowest CORR was shown by EXP1 at The EXP 8 simulated lower values (RMSE, MAE) and highest value (CORR) than the other EXP. However, the EXP8 was based on Lin scheme, 0000UTC March 26, 2011 and SST boundary condition that means the lead time of initial condition and SST boundary condition has effect rainfall unseasonal over Southern Thailand. The Ensemble mean show the second lowest value (RMSE, MAE) and the second

16 highest value (CORR). Thus, the Lin scheme, 0000UTC March 26, 2011 and SST boundary condition was able to simulate rainfall more accurately compared with the other EXP used in this study. Table 4. Heavy rainfall, Average rainfall, RMSE, MAE and CORR on March 28, 2011 TMD TMD (Heavy rainfall = cm/day) (Average Rainfall= cm/day) EXP Samui Island station Average RMSE MAE CORR EXP EXP EXP EXP EXP EXP EXP EXP EXP EXP EXP EXP Ensemble mean Conclusion This study investigated WRF model simulations with several twelve experiments (EXP) for the simulation of heavy rainfall unseasonal events over the Southern part of Thailand. The high domain resolution (4 km) was used prediction for sensitivity lead times initial conditions, physics parameterization and SST boundary condition to get a good prediction of unseasonal heavy rainfall. The difference spatial rainfall pattern was shown between all EXP and TMD station. The 0000UTC March 26, 2011 lead time of initial condition was shown good spatial difference rainfall performance and can capture difference rainfall over heavy season area than 0000UTC March 25, 2011 and 0000UTC March 27, 201. So this study can confirm the lead time initial condition that was more affected and importance in simulated unseasonal heavy rainfall. In the statistical method, the EXP 8 simulated lower values (RMSE, MAE) and highest value (CORR) than the other EXP. However the EXP8 was based on Lin scheme, 0000UTC March 26, 2011 and include SST boundary condition that means the lead time of initial condition and SST boundary condition has effect rainfall unseasonal over Southern Thailand. The Ensemble mean show the second lowest value (RMSE, MAE) and second highest value (CORR). Thus, the Lin scheme, 0000UTC March

17 26, 2011 and SST boundary condition was able to simulate rainfall more accurately compared with the other EXP used in this study. However, in case of lead time initial condition should be more than one day for simulation rainfall event for the short-term case. The physics parameterization, in this research, was focused on two microphysics scheme, that is Lin scheme and WSM6 scheme. The results of Lin scheme, especially EXP8, was shown a good estimated than WSM6. Since, the simulation with SST boundary condition was shown better performance in different spatial rainfall pattern and statistical method, compared with the simulation without SST boundary condition. Therefore, in the unseasonal regional rainfall simulation should be included the SST boundary condition. Acknowledgement The authors acknowledge NCAR for the WRF model and Thai Metrological Department for information and station data. The authors are also very grateful to the Department of Mathematics, Faculty of Science, King Mongkut s University of Technology Thonburi (KMUTT) for the support. The authors acknowledge the financial support provided by the King Mongkut s University of Technology Thonburi through the KMUTT 55th Anniversary Commemorative Fund. This research was fully supported by the International Research Network (IRN) (IRN5701PDW0002). References 1. Wangwongchai A, Sixiong Z, and Qingcun Z (2005). "A case study on a strong tropical disturbance and record heavy rainfall in Hat Yai Thailand during the winter monsoon". Advance in Atmospheric Sciences, Vol.22(3), pp Kirtsaeng S, Kreasueun J, Chantara S, Kirtsaeng S, Sukthawee P, and Masthawee F (2012). Weather research and forecasting (WRF) model performance for a simulation of the 5 November 2009 heavy rainfall over southeast of Thailand. Chiang Mai Journal Sciences, 39(3): Kaewmesri, P., Humphries, U., and Sooktawee, S. (2017a). Simulation on High-Resolution WRF Model for an Extreme Rainfall Event over the Southern Part of Thailand. International Journal of Advanced and Applied Sciences, 4(9), pp Kaewmesri, P., Humphries, U., Wangwongchai, A., Wongwises, P., Archevarahuprok, B., and Sooktawee, S. (2017b). The Simulation of Heavy Rainfall Events over Thailand Using Microphysics Schemes in Weather Research and Forecasting (WRF) Model. World Applied Science Journal. 32(5), pp Thai Meteorological Department (2015), The Climate of Thailand [Online], Available: 6. Thai Meteorological Department (2011), Annual Weather Summary of Thailand in 2011 [Online], Available: 7. NASA-Earth Observation (2014), Unseasonably Heavy Rain Floods Thailand. Retrieved [Online], Available: 8. Loo, Y.Y., Billa, L., and Singh, A. (2015). Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geoscience Frontiers, 6(6), pp Efstathiou GA, Zoumakis NM, Melas D, and Kassomenos P (2012). Impact of precipitation ice on the simulation of a heavy rainfall event with advanced research WRF using two bulk microphysical schemes. Asia-Pacific Journal Atmospheric Science, 48(4): Pennelly C, Reuter G, and Flesch T (2014). Verification of the WRF model for simulating heavy precipitation in Alberta. Atmospheric Research, 135:

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