Short-range Rainfall Prediction over Egypt using the Weather Research and Forecasting Model

Abstract

Rainfall is the main source for fresh water on the earth and rainwater is now one of the most important commodities in the world. Improved rainfall prediction will help people in different communities to be more prepared for adverse heavy rainfall events to save lives and minimize infrastructure damage. Rainfall prediction can also help convert rainfall disasters into benefits through prediction assisted rainfall harvesting, especially for many countries in Africa. The main aim of this study is to evaluate the performance of Weather Research and Forecasting (WRF) Model in heavy rainfall prediction over Egypt. WRF was used to simulate and evaluate heavy rain events as specific case studies over Egypt on December 10 th , 2013. During this case study we conducted multi-nested domains experiments for three domains with different horizontal resolutions of 30 km, 10 km, and 3.33 km respectively. The model was adopted to produce its output as daily average in order to be compatible and comparable with the rain gauges observations. Comparisons between the modeled rainfall from WRF and rain gauge stations were in Egypt. During this research we evaluated the model through statistical calculations of the root mean square error (RMSE) and the mean bias (MB). The results revealed that the values of the (RMSE) and the (MB) over Egypt were 2.21 and -0.81. Most of the results showed good agreements between the predicted rainfall by WRF and measurements from rain gauge stations Egypt. It was also noticed that the WRF model was able to simulate the synoptic situation which invaded the study area and accompanied by heavy rainfall over Egypt. The model demonstrated a good ability to predict the heavy rainfall events over the study areas. Hence it can be concluded that, based on the results of this study, the performance of the WRF model demonstrated a good and reasonable rainfall prediction capability when the predicted data were compared to the actual rain gauge measurements at all stations over the study areas. WRF is therefore highly recommended for use in heavy rainfall prediction.