{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T02:40:58Z","timestamp":1771728058822,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF0801704"],"award-info":[{"award-number":["2022YFF0801704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite rainfall estimates (SREs) play a crucial role in weather monitoring, forecasting and modeling, particularly in regions where ground-based observations may be limited. This study presents a comprehensive evaluation of three commonly used SREs\u2014African Rainfall Climatology version 2 (ARC2), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and Tropical Application of Meteorology using SATellite data and ground-based observation (TAMSAT)\u2014 with respect to their performance in detecting rainfall patterns in Nigeria at daily scales from 2002 to 2022. Observed data obtained from the Nigeria Meteorological Agency (NiMet) are used as reference data. Evaluation metrics such as correlation coefficient, root mean square error, mean error, bias, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are employed to assess the performance of the SREs. The results show that all the SREs exhibit low bias during the major rainfall season from May to October, and the products significantly overestimate observed rainfall during the dry period from November to March in the Sahel and Savannah Zones. Similarly, over the Guinea Zone, all the products indicate overestimation in the dry season. The underperformance of SREs in dry seasons could be attributed to the rainfall retrieval algorithms, intensity of rainfall occurrence and spatial-temporal resolution. These factors could potentially lead to the accuracy of the rainfall retrieval being reduced due to intense stratiform clouds. However, all the SREs indicated better detection capabilities and less false alarms during the wet season than in dry periods. CHIRPS and TAMSAT exhibited high POD and CSI values with the least FAR across agro-climatic zones during dry periods. Generally, CHIRPS turned out to be the best SRE and, as such, would provide a useful dataset for research and operational use in Nigeria.<\/jats:p>","DOI":"10.3390\/rs16101755","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T11:31:52Z","timestamp":1715772712000},"page":"1755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Evaluation of Satellite-Based Rainfall Estimates against Rain Gauge Observations across Agro-Climatic Zones of Nigeria, West Africa"],"prefix":"10.3390","volume":"16","author":[{"given":"Aminu Dalhatu","family":"Datti","sequence":"first","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Nigerian Meteorological Agency (NiMet), Nnamdi Azikiwe International Airport, Abuja 900105, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3331-2337","authenticated-orcid":false,"given":"Gang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Elena","family":"Tarnavsky","sequence":"additional","affiliation":[{"name":"Department of Meteorology, University of Reading, Reading RG6 6UR, UK"}]},{"given":"Rosalind","family":"Cornforth","sequence":"additional","affiliation":[{"name":"Department of Meteorology, University of Reading, Reading RG6 6UR, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1766-2898","authenticated-orcid":false,"given":"Florian","family":"Pappenberger","sequence":"additional","affiliation":[{"name":"European Centre for Medium-Range Weather Forecasts (ECMWF), Reading RG2 9AX, UK"}]},{"given":"Bello Ahmad","family":"Abdullahi","sequence":"additional","affiliation":[{"name":"Nigerian Meteorological Agency (NiMet), Nnamdi Azikiwe International Airport, Abuja 900105, Nigeria"}]},{"given":"Anselem","family":"Onyejuruwa","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Nigerian Meteorological Agency (NiMet), Nnamdi Azikiwe International Airport, Abuja 900105, Nigeria"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"D02115","DOI":"10.1029\/2010JD014741","article-title":"Evaluation of Satellite-Retrieved Extreme Precipitation Rates across the Central United States","volume":"116","author":"Aghakouchak","year":"2011","journal-title":"J. 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