Verification of Operational Numerical Weather Prediction Model Forecasts of Precipitation Using Satellite Rainfall Estimates over Africa

TL;DR

This study evaluates the accuracy of ECMWF and NCEP-GFS short-term rainfall forecasts over Africa by comparing them with satellite-based rainfall estimates, revealing spatial and seasonal variations and improvements at weekly aggregation.

Contribution

It provides a comprehensive validation of major operational weather models against satellite rainfall estimates over Africa, highlighting their strengths and limitations.

Findings

Good spatial consistency of model forecasts with satellite data.

Significant seasonal and spatial variations in forecast accuracy.

Improved forecast skill at weekly aggregation compared to daily.

Abstract

Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate related diseases and public health. Numerical Weather Models (NWM) are an important component of this work, due to their complete spatial coverage, high resolution, and ability to forecast into the future. In this study, we seek to evaluate the spatiotemporal skill of short-term rainfall forecasts of rainfall across Africa. Specifically, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in-situ observations and are commonly used in analysis of African rainfall. We found that the model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter-tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at one week aggregation time are improved against daily forecasts.