Abstract 219

Abstract ID: 219

Predictability of Sub-seasonal Rainfall in the Arabian Peninsula

Lead Author: Luong Thang
King Abdullah University of Science and Technology, Saudi Arabia

Keywords: Arabian Peninsula, Convection-permitting model, Precipitation

Abstract: Despite being one of the driest places in the world, the Kingdom of Saudi Arabia (KSA) occasionally experiences extreme precipitation events associated with organized convections that might lead to flooding. Rainfall forecasts at lead times on the sub-seasonal to seasonal (S2S) timescale can potentially assist disaster risk mitigation, and water resource management. Here, model skills of predicting precipitation at sub-seasonal scale (from 2 to 4 weeks ahead) are benchmarked over the Arabian Peninsula (AP). We utilized the Weather Research and Forecasting Model (WRF) at convection-permitting resolution (4 km) to dynamically downscale ensemble of 11 members of the European Centre of Medium-range Weather Forecasts (ECMWF) S2S reforecast product over a 20-year period (1998-2018). Representation of precipitation is assessed with a regional reanalysis over the AP and in-situ rain gauge measurements.
Precipitation mostly occurs over the AP during cooler months (November to April). Mesoscale convective systems (MCSs) are important factors in producing rainfall over the region during this period when extratropical systems are dominant. The majority of rainfall events in November to February are associated with extratropical forcing, while March and April rainfalls are associated with tropical-extratropical interactions.
Our results indicate that the WRF convection-permitting model adequately describes the precipitation patterns over the AP up to 4-week forecast-range and statistically improves the forecast skill with regard to its driving ECMWF fields over the studied 20-year period. WRF reduces the dry bias by 30-40% and overall improve the rainfall forecast skill by 34% on average. Large-scale circulation signatures are reproduced better in the spring wide-spread rainfall events for both WRF and ECMWF. The regional model (WRF) adds more values in simulating winter mesoscale convective systems improving rainfall forecast skill 46% on average.

Hsin-I Chang (University of Arizona)
Hari P. Dasari (King Abdullah University of Science and Technology)
C. Bayu Risanto (University of Arizona)
Matteo Zampieri (King Abdullah University of Science and Technology)
Christopher L. Castro (University of Arizona)
Ibrahim Hoteit (King Abdullah University of Science and Technology)