Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 9; p. 1964
• Main Authors: Nooni, Isaac Kwesi, Tan, Guirong, Hongming, Yan, Saidou Chaibou, Abdoul Aziz, Habtemicheal, Birhanu Asmerom, Gnitou, Gnim Tchalim, Lim Kam Sian, Kenny T. C.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2022
• Subjects: Artificial neural networks; CHIRPS; Climate change; Climatic data; Datasets; Disasters; Fatalities; Floods; GPM IMERG; High resolution; Industrialized nations; Mathematical models; Neural networks; Performance assessment; PERSIANN-CCS-CDR; Precipitation; R&D; Rain; Remote sensing; Research & development; Root-mean-square errors; Satellite observation; Satellites; Simulation; Summer; synoptic analysis; TAMSAT; Validation studies; Weather forecasting; WRF; South Sudan; Sudan; Africa; Uganda; Kenya; Ethiopia; East Africa
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14091964

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used.