{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T01:48:01Z","timestamp":1768009681088,"version":"3.49.0"},"reference-count":84,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1902209"],"award-info":[{"award-number":["U1902209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of Yunnan Province","award":["202201AS070069"],"award-info":[{"award-number":["202201AS070069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019 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 &gt;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.<\/jats:p>","DOI":"10.3390\/rs14091964","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:22:43Z","timestamp":1650414163000},"page":"1964","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6636-9554","authenticated-orcid":false,"given":"Isaac Kwesi","family":"Nooni","sequence":"first","affiliation":[{"name":"Binjiang College, Nanjing University of Information Science and Technology, Wuxi 214105, China"}]},{"given":"Guirong","family":"Tan","sequence":"additional","affiliation":[{"name":"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"}]},{"given":"Yan","family":"Hongming","sequence":"additional","affiliation":[{"name":"Yunnan Climate Center, Kunming 650034, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0560-7050","authenticated-orcid":false,"given":"Abdoul Aziz","family":"Saidou Chaibou","sequence":"additional","affiliation":[{"name":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-0398","authenticated-orcid":false,"given":"Birhanu Asmerom","family":"Habtemicheal","sequence":"additional","affiliation":[{"name":"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"},{"name":"Department of Physics, Wollo University, Dessie P.O. Box 1145, Ethiopia"}]},{"given":"Gnim Tchalim","family":"Gnitou","sequence":"additional","affiliation":[{"name":"Binjiang College, Nanjing University of Information Science and Technology, Wuxi 214105, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8328-8745","authenticated-orcid":false,"given":"Kenny T. C.","family":"Lim Kam Sian","sequence":"additional","affiliation":[{"name":"Binjiang College, Nanjing University of Information Science and Technology, Wuxi 214105, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2021). Summary for Policymakers. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_2","unstructured":"FLOODLIST (2021, December 12). Home Page. 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