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Remote Sensing
Volume 14
Issue 9
10.3390/rs14091964
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Article

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

by
Isaac Kwesi Nooni
1,
Guirong Tan
2,
Yan Hongming
3,*,
Abdoul Aziz Saidou Chaibou
4,
Birhanu Asmerom Habtemicheal
4,5,
Gnim Tchalim Gnitou
1 and
Kenny T. C. Lim Kam Sian
1
1
Binjiang College, Nanjing University of Information Science and Technology, Wuxi 214105, China
2
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
3
Yunnan Climate Center, Kunming 650034, China
4
Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
5
Department of Physics, Wollo University, Dessie P.O. Box 1145, Ethiopia
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(9), 1964; https://doi.org/10.3390/rs14091964
Submission received: 21 March 2022 / Revised: 15 April 2022 / Accepted: 17 April 2022 / Published: 19 April 2022
(This article belongs to the Special Issue Photogrammetry and Remote Sensing in Environmental and Engineering Applications)
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Abstract

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.
Keywords: synoptic analysis; WRF; GPM IMERG; PERSIANN-CCS-CDR; CHIRPS; TAMSAT; extreme precipitation events; fuzzy method synoptic analysis; WRF; GPM IMERG; PERSIANN-CCS-CDR; CHIRPS; TAMSAT; extreme precipitation events; fuzzy method

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MDPI and ACS Style

Nooni, I.K.; Tan, G.; Hongming, Y.; Saidou Chaibou, A.A.; Habtemicheal, B.A.; Gnitou, G.T.; Lim Kam Sian, K.T.C. Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product. Remote Sens. 2022, 14, 1964. https://doi.org/10.3390/rs14091964

AMA Style

Nooni IK, Tan G, Hongming Y, Saidou Chaibou AA, Habtemicheal BA, Gnitou GT, Lim Kam Sian KTC. Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product. Remote Sensing. 2022; 14(9):1964. https://doi.org/10.3390/rs14091964

Chicago/Turabian Style

Nooni, Isaac Kwesi, Guirong Tan, Yan Hongming, Abdoul Aziz Saidou Chaibou, Birhanu Asmerom Habtemicheal, Gnim Tchalim Gnitou, and Kenny T. C. Lim Kam Sian. 2022. "Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product" Remote Sensing 14, no. 9: 1964. https://doi.org/10.3390/rs14091964

APA Style

Nooni, I. K., Tan, G., Hongming, Y., Saidou Chaibou, A. A., Habtemicheal, B. A., Gnitou, G. T., & Lim Kam Sian, K. T. C. (2022). Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product. Remote Sensing, 14(9), 1964. https://doi.org/10.3390/rs14091964

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