Saudi Arabia extreme weather ensemble forecast evaluation at sub-seasonal timescale

Abstract:

The Arabian Peninsula (AP) is an arid climate region but often experiences extreme rainfall events between October to March that cause numerous hazards. Severe weather associated with convective thunderstorms is becoming more intense globally and is also observed in the AP. Improvements in extreme weather forecast for sub-seasonal to seasonal forecast increase the preparedness of convective extremes and related hazards. We designed a series of ensemble forecast downscaling using the Weather Research and Forecasting model (WRF) at convective-permitting scale. The driving global sub-seasonal to seasonal reforecast is provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).

Sub-seasonal WRF simulations are performed on the AP’s top 20 extreme precipitation events in the last 20 years, downscaling from the 11 ECMWF hindcast ensemble members. Each of the events recorded at least 20 mm/day rainfall in the Jeddah station. Several aspects of the simulations are evaluated: (1) WRF and ECMWF precipitation forecast capability: determine forecast window of opportunity in the regional climate model, identify the value added using convective-permitting type modeling; (2) Teleconnection pattern forecast capability: determine forecast skill for the dominant large scale pattern related to the convective extremes in the driving ECMWF reforecasts and ERA-Interim reanalysis data; (3) Mesoscale convective system (MCS) tracking: objectively tracking the MCS clouds in satellite observation and WRF downscaled reforecasts. Sub-seasonal forecast evaluations will be performed with statistical analysis tools commonly used in operational forecast evaluation, such as Probability of Detection (POD) and Relative Operating Characteristics (ROC). Through the designed analyses, we will be able to collectively show the ensemble forecast skills for the largest convective events in the AP and assess the predictability of the extremes in the existing operational forecast model.