Evaluation of Satellite Rainfall Products for Nowcasting Over Southern Africa Using Rain-Gauges and Lightning Data

Abstract

Satellite-based ‘nowcasting’, which extrapolates the latest observations to provide forecasts from minutes to 6 h ahead, offers an accurate and timely approach for monitoring extreme weather. We evaluate the widely used NWCSAF Convective Rainfall Rate (CRR) nowcasting product using instantaneous (non-propagated) estimates against a network of high-temporal-resolution rain gauges in Zambia during the wet seasons from 2019 to 2024. We also assess two alternative satellite-based rainfall products: IMERG microwave-only (IMERG-HQ) and Rain over Africa (RoA), a machine learning–generated nowcasting product. Additionally, lightning data from the Meteosat Third Generation mission and cloud-top temperature data were used to evaluate the products. These datasets formed part of the products used by forecasters during nowcasting testbeds conducted under the WISER EWSA project and positive feedback from users was widely reported. Our analysis shows that the products have distributions similar to the in situ data, but all exhibit biases, with CRR and IMERG-HQ generally overestimating rainfall rates while RoA tends to underestimate. Across different thresholds and aggregation windows, RoA outperforms IMERG-HQ and CRR for rain occurrence on most metrics, including hit rate, probability of false alarm and Heidke skill score. However, all products show reduced skill for extreme rainfall. CRR also shows weak correspondence with lightning activity and does not detect rainfall from cloud tops warmer than −30°C. In contrast, RoA and IMERG-HQ exhibit greater sensitivity to cloud top temperature, including warmer cloud regimes. The generally superior performance of RoA compared to IMERG-HQ is remarkable, given that RoA only takes infrared satellite channels as input, whereas IMERG-HQ uses microwave channels. We attribute RoA's greater skill to the ability of ML to optimally exploit the data and bring spatial information into the algorithm. Our findings emphasise the value of satellite-based monitoring and nowcasting, while underscoring the need for training and calibration to improve accuracy.