Evaluating Microphysics and Boundary Layer Schemes in WRF: Assessment of 36 Scheme Combinations for 17 Major Storms in Saudi Arabia

Abstract. Extreme rainfall events (EREs) and resulting flash floods in Saudi Arabia cause significant risks, including casualties and economic losses. Accurate simulations are crucial for forecasting, climate projections, and disaster management. This study evaluates boundary layer (BL) and cloud microphysics (MP) schemes to simulate EREs in the Arabian Peninsula (AP) using the Weather Research and Forecasting (WRF) model. Thirty-six combinations of four BL and nine MP schemes were tested across 17 EREs at a convective-permitting 3-km resolution, compared with IMERG gridded satellite data for rainfall and station observations for temperature, humidity, and wind speed. Performance was assessed using Kling-Gupta Efficiency (KGE) incorporates correlation, variability, and overall bias. We found good visual agreement between observed and simulated rainfall patterns despite some over- and underestimations. Among BL schemes, the Yonsei University (YSU) scheme stood out as the best performers in terms of rainfall, while Thompson (MP8) ranked the highest among the MP schemes. Goddard (MP7) also delivered strong results. The Thompson-YSU combination yielded the highest mean KGE, performing statistically significantly better than 21 other combinations. Furthermore, performance rankings varied across meteorological variables, suggesting that superior rainfall performance does not necessarily correlate with an overall more accurate simulation. This study highlights the challenges of scheme evaluation and the importance of analyzing many EREs while using reliable reference data. It offers guidance for selecting the most appropriate schemes and lays the foundation for future ERE forecasting and climate modeling improvements in arid regions.