Evaluating the Performance of a WRF Physics Ensemble in Simulating Rainfall over Lao PDR during Wet and Dry Seasons

Abstract

Dynamical downscaling of General Circulation Model (GCM) data for any region has been made possible due to a set of physics options and model dynamics within the Weather Research and Forecasting (WRF) model. This study evaluated the performance of an ensemble of physics options in simulating rainfall during wet and dry seasons of Lao PDR. The model evaluation criteria focused on identifying the optimum physics options for a range of scenarios. No single combination of physics options performed well in all scenarios reflecting the importance of using different parameterizations according to the geographic location and the intended application of the results. For the dry season, none of the ensemble members performed satisfactorily for the southern region of Lao PDR, while all the ensemble members performed well for the northern and central regions. While almost all the WRF simulations overestimated the rainfall during the wet season, BMJ for cumulus physics performed better in the northern and central regions, and KF performed better in the south region. The YSU scheme performed best as the planetary boundary layer for both wet and dry seasons, while WSM5 for the wet season and Lin for the dry season gave the best model performance as the microphysics option.

Sensitivity of Real‐Time Forecast for Typhoons Around Korea to Cumulus and Cloud Microphysics Schemes

“…The main issue with the BMJ scheme was the reference profiles, which were already determined based on mean structures observed in the deep convection. The BMJ scheme has been reported to have good performance (Athukorala et al., 2021; Choi & Ahn, 2017; Doutreloup et al., 2019; Evans et al., 2012; Kumar et al., 2014; Ratna et al., 2014) and widely used within the WRF community since it requires fewer computational resources due to its simplicity. However, our results show that the predetermined reference profiles could cause issues even in short‐term typhoon forecasts.…”

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

“…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).…”

The Skill Assessment of Weather and Research Forecasting and WAVEWATCH-III Models During Recent Meteotsunami Event in the Persian Gulf

“…The Goddard scheme for shortwave (Chou and Suarez, 1994) and the Rapid Radiative Transfer Model [RRTM; Mlawer et al (1997)] for longwave radiation was used. Since domain resolution was less than 5 km, the cumulus parameterization was switched off for the local domain in all simulations according to Athukorala et al (2021). The parameterization schemes incorporated into the model are summarized in Table 1.…”