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Evaluation of satellite-based rainfall estimates against rain gauge observations across agro-climatic zones of Nigeria, West Africa

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Dalhatu Datti, A., Zeng, G., Tarnavsky, E. ORCID: https://orcid.org/0000-0003-3403-0411, Cornforth, R. ORCID: https://orcid.org/0000-0003-4379-9556, Pappenberger, F., Ahmad Abdullahi, B. and Onyejuruwa, A. (2024) Evaluation of satellite-based rainfall estimates against rain gauge observations across agro-climatic zones of Nigeria, West Africa. Remote Sensing, 16 (10). 1775. ISSN 2072-4292

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To link to this item DOI: 10.3390/rs16101755

Abstract/Summary

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—African 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)— 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.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary Research Centres (IDRCs) > Walker Institute
ID Code:117530
Publisher:MDPI
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