Preprints
https://doi.org/10.5194/egusphere-2025-912
https://doi.org/10.5194/egusphere-2025-912
03 Apr 2025
 | 03 Apr 2025

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

Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck

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.

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Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-912', Anonymous Referee #1, 24 Apr 2025
    • RC3: 'Reply on RC3', Abhishek Lodh, 16 May 2025
    • AC1: 'Reply on RC1', Rajesh Kumar Sahu, 15 Jul 2025
    • AC2: 'Reply on RC1', Rajesh Kumar Sahu, 15 Jul 2025
  • RC2: 'Comment on egusphere-2025-912', Anonymous Referee #2, 24 Apr 2025
  • RC4: 'Comment on egusphere-2025-912', Abhishek Lodh, 16 May 2025
  • RC5: 'Comment on egusphere-2025-912', Abhishek Lodh, 16 May 2025
Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck
Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck

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Short summary
This study evaluates 36 microphysics (MP) and boundary layer (BL) scheme combinations in the Weather Research and Forecasting (WRF) model for extreme rainfall over Saudi Arabia. Using Kling-Gupta Efficiency (KGE), results show YSU (BL1) and Thompson (MP8) perform best, while Morrison-MYNN (MP10_BL6) ranks lowest. The mean temporal KGE is 0.37, and the spatial KGE is 0.26, highlighting spatial prediction challenges. Findings aid model evaluation and forecasting in arid regions.
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