Validation of satellite and reanalysis rainfall products against rain gauge observations in Ghana and Zambia

TL;DR

This study assesses the accuracy of satellite and reanalysis rainfall products in Ghana and Zambia, revealing strengths in dry day detection but limitations in heavy rain identification, emphasizing the need for bias correction and improved heavy rainfall detection.

Contribution

It provides a comprehensive validation of eight rainfall products against ground observations in Ghana and Zambia, highlighting their strengths and weaknesses for different rainfall events.

Findings

High detection probability for dry days in both countries

Limited skill in detecting heavy and violent rains

Products with station data outperform others in many contexts

Abstract

Accurate rainfall data are crucial for effective climate services, especially in Sub-Saharan Africa, where agriculture depends heavily on rain-fed systems. The sparse distribution of rain-gauge networks necessitates reliance on satellite and reanalysis rainfall products (REs). This study evaluated eight REs -- CHIRPS, TAMSAT, CHIRP, ENACTS, ERA5, AgERA5, PERSIANN-CDR, and PERSIANN-CCS-CDR -- in Zambia and Ghana using a point-to-pixel validation approach. The analysis covered spatial consistency, annual rainfall summaries, seasonal patterns, and rainfall intensity detection across 38 ground stations. Results showed no single product performed optimally across all contexts, highlighting the need for application-specific recommendations. All products exhibited a high probability of detection (POD) for dry days in Zambia and northern Ghana (70% < POD < 100%, and 60% < POD < 85%, respectively), suggesting their utility for drought-related studies. However, all products showed limited skill in detecting heavy and violent rains (POD close to 0%), making them unsuitable for analyzing such events (e.g., floods) in their current form. Products integrated with station data (ENACTS, CHIRPS, and TAMSAT) outperformed others in many contexts, emphasizing the importance of local observation calibration. Bias correction is strongly recommended due to varying bias levels across rainfall summaries. A critical area for improvement is the detection of heavy and violent rains, with which REs currently struggle. Future research should focus on this aspect.