P43 Predicting intermittent heavy rain in tropical desert climate with WRF model

Wu, Wanli, Yubao Liu, Paul Kucera, Yongxin Zhang, Linlin Pan, Yuewei Liu, National Center for Atmospheric Research, and Ayman S. Ghulam, Presidency of Meteorology and Environment, Saudi Arabia

Arabian peninsula is in tropical desert climate that is largely formed by the unique geography. Though hot and dry weather is prevailing year-around in the peninsula, intermittent heavy rainfall is still one of most damaging natural disasters along with sand and dust storms. Predicting such events with high skill has significant socioeconomic benefits, but is challenging because of limited observation network in this hazard desert climate. In this talk, we first introduce a WRF-based high-resolution NWP model (~2km) with advanced data assimilation capability jointly developed by the Research Applications Laboratory of NCAR and the Presidency of Meteorology and Environment, Saudi Arabia. This forecasting model is intended to provide routine weather forecast and sand-dust storm prediction for the region. We then take case studies to demonstrate the forecasting skills of the model and to discuss suitable model configurations for the tropical desert climate, especially on microphysics and cumulus cloud parameterizations. Finally we present the advanced data assimilation algorithms employed that address regional sparse observation network.