Performance Evaluation of High-Resolution WRF Forecasts with Multi-Source Observations over the Sahel

In the Sahel, the frequency and magnitude of floods caused by intense hydrometeorological events have been steadily increasing for more than three decades. These floods have significant impacts on populations and livelihoods, undermining efforts toward sustainable development. The Early Warnings for All (EW4ALL) initiative, launched by the World Meteorological Organization (WMO) and other UN agencies, identifies early warning systems as a key tool for reducing the impacts of floods and other natural disasters. One of the core components of such systems is numerical weather prediction.

In Niger and Burkina Faso, the respective National Meteorological Services (DMN and ANAM) have adopted the Weather Research and Forecasting (WRF) model within their operational forecasting chains, each using different model configurations. As part of the SLAPIS Sahel project, a capacity-building initiative was launched to support both institutions in improving the performance of their WRF-based systems for forecasting high-impact weather events.

This study presents the preliminary results of a verification process comparing the two operational WRF configurations over a computational domain covering both countries, with a horizontal resolution of 4 km. Simulations were initialized with both GFS and ECMWF global datasets and focused on the rainy seasons (July–August–September) of 2023 and 2024. Forecasts of precipitation and surface temperature were evaluated against observational data from ANAM and DMN’s official networks, satellite-based estimates (CHIRPS and TAMSAT), and ERA5-Land reanalysis.

Verification was conducted using standard point-based skill scores—such as Probability of Detection (POD), success ratio, bias, Critical Success Index (CSI), and Root Mean Square Error (RMSE)—as well as spatial metrics including the Fractional Skill Score (FSS). Moreover, significant case studies corresponding to flood events have been analyzed in detail.

The results show that high-resolution modeling allows for a more accurate spatial reconstruction of intense rainfall events compared to global-scale models. However, due to the predominantly convective nature of Sahelian rainfall, the quantitative accuracy remains insufficient for fully deterministic flood forecasting. Future work will focus on integrating the existing physically based models with post-processing techniques based on machine learning to enhance the operational prediction of extreme weather events and support early warning dissemination at local and national scales.