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

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.

Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach

“…However, we removed the CFSR data set from consideration, as it underwent a version change during the production run period. GFS also performs well in the region (Draxl et al 2014;Singh et al 2015;Du Duc et al 2019;Nuryanto et al 2019). Because FNL uses the same data assimilation procedures as GFS, but with 10% more observations, we chose FNL instead.…”

“…We chose the BMJ cumulus parameterization in the 27-km and 9-km domains. Although the Kain-Fritsch scheme returns better performance for isolated heavy rainfall events, BMJ may better resolve rainfall throughout longer periods, averaged over larger spatial extents, and in conjunction with the YSU scheme in both the wet and dry seasons in the region (Draxl et al 2014;Du Duc et al 2019;Athukorala et al 2021). We chose the WSM6 microphysics package for its reliability throughout Southeast Asia (Tolentino et al 2016;Nuryanto et al 2019;Rai and Pattnaik 2019) and performance in sensitivity analysis (Du Duc et al 2019).…”

“…Although the Kain-Fritsch scheme returns better performance for isolated heavy rainfall events, BMJ may better resolve rainfall throughout longer periods, averaged over larger spatial extents, and in conjunction with the YSU scheme in both the wet and dry seasons in the region (Draxl et al 2014;Du Duc et al 2019;Athukorala et al 2021). We chose the WSM6 microphysics package for its reliability throughout Southeast Asia (Tolentino et al 2016;Nuryanto et al 2019;Rai and Pattnaik 2019) and performance in sensitivity analysis (Du Duc et al 2019). We chose the Dudhia shortwave, the RRTMG longwave, the Noah Land Surface Model, and the MM5 Surface Layer parameterizations for their regular use by modeling experts in the region and performance in sensitivity evaluation (Tolentino et al 2016;Jacobson et al 2018;Du Duc et al 2019;Nuryanto et al 2019;Rai and Pattnaik 2019).…”

“…We chose the WSM6 microphysics package for its reliability throughout Southeast Asia (Tolentino et al 2016;Nuryanto et al 2019;Rai and Pattnaik 2019) and performance in sensitivity analysis (Du Duc et al 2019). We chose the Dudhia shortwave, the RRTMG longwave, the Noah Land Surface Model, and the MM5 Surface Layer parameterizations for their regular use by modeling experts in the region and performance in sensitivity evaluation (Tolentino et al 2016;Jacobson et al 2018;Du Duc et al 2019;Nuryanto et al 2019;Rai and Pattnaik 2019). The MM5 surface layer scheme is not compatible with the MYJ PBL scheme, so we selected the Eta similarity scheme instead.…”

Sensitivity of simulating Typhoon Haiyan (2013) using WRF: the role of cumulus convection, surface flux parameterizations, spectral nudging, and initial and boundary conditions

“…Previous studies have also indicated the sensitivity of TC-associated rainfall to different physics parameterizations in WRF. Satya et al (2019) andDu Duc et al (2019) found that KF better predicts rainfall than TK, but both generally perform poorly in simulating rainfall, and WRF TC-associated rain is underestimated (Bagtasa, 2021).…”