Bias correction of satellite and reanalysis products for daily rainfall occurrence and intensity

TL;DR

This study evaluates bias correction methods for satellite and reanalysis rainfall estimates in data-sparse regions, finding statistical methods generally outperform machine learning, but challenges remain in accurately detecting heavy rainfall events.

Contribution

Introduces a constrained LOCI bias correction method and compares traditional and machine learning approaches across multiple satellite products in Africa.

Findings

Statistical bias correction methods outperform machine learning in most cases.

Corrected SREs effectively detect dry days with high POD.

Most methods fail to improve detection of heavy rainfall events.

Abstract

In data-sparse regions, satellite and reanalysis rainfall estimates (SREs) are vital but limited by inherent biases. This study evaluates bias correction (BC) methods, including traditional statistical (LOCI, QM) and machine learning (SVR, GPR), applied to seven SREs across 38 stations in Ghana and Zambia. We introduce a constrained LOCI method to prevent the unrealistically high rainfall values produced by the original approach. Results indicate that statistical methods generally outperformed machine learning, though QM tended to inflate rainfall. Corrected SREs showed high capability in detecting dry days (POD $\ge$ 0.80). The ENACTS product, which integrates numerous station records, was the most amenable to correction in Zambia; most BC methods reduced mean error at >70% of stations. However, ENACTS performed less reliably at an independent station (Moorings), highlighting the need for broader validation at locations not incorporated into the product. Crucially, even after correction, most SREs (except ENACTS) failed to improve the detection of heavy and violent rainfall (POD $\le$ 0.2). This limits their utility for flood risk assessment and highlights a vital research gap regarding extreme event estimation.