Sensitivity of the Weather Research and Forecasting model (WRF) to downscaling extreme events over Northern Tunisia

Abstract

Abstract. Rainfall is one of the most important variables for water and flood management. We investigate the capacity of the Weather Research and Forecasting model (WRF) to dynamically downscale the ECMWF Re-Analysis data for Northern Tunisia. This study aims to examine the sensitivity of WRF rainfall estimates to different Planetary Boundary Layer (PBL) and Cumulus Physics (Cu) schemes. The verification scheme consists of three statistical criteria (Root Mean Square Error (RMSE), Pearson correlation, and the ratio bias coefficient). Moreover, the FSS coefficient (fraction skill score) and the quality coefficient SAL (structure amplitude latitude) are calculated. The database is composed of four heavy events covering an average of 318 rainfall stations. We mean by heavy event, each event occurred a rainfall of more than 50 mm per observed day at least in one rainfall station. The sensitivity study showed that there is not a best common combination scheme (PBL and Cu) for all the events. The average of the best 10 combinations for each event is adopted to get the ensemble map. We conclude that some schemes are sensitive and others less sensitive. The best three performing schemes for PBL and Cu parametrizations are selected for future rainfall estimation by WRF over Northern Tunisia.