WRF Supermodelling for Improved Simulation of Extreme Precipitation in West Africa

Mesoscale Convective Systems (MCSs) account for most of the rainfall over the Sahel and contribute substantially to extreme precipitation events (EPEs) that cause flooding in West Africa. However, the low density of ground-based observation networks limits the accurate monitoring and characterization of these events. Regional climate models, such as the Weather Research and Forecasting (WRF) model, help to compensate for this observational gap but still exhibit significant biases in simulating extreme precipitation.

This study aims to reduce these biases by integrating the WRF model into a Supermodelling framework based on the dynamic combination of multiple model configurations, designed to exploit their complementary strengths.

Two supermodels, SUPPERT-WRFA and SUPPERT-WRFB, are developed from three distinct WRF configurations that mainly differ in their convection schemes. SUPPERT-WRFA is trained using satellite-based IMERG precipitation, while SUPPERT-WRFB relies on atmospheric dynamical variables (U, V, and T) from ERA5. In both cases, the training strategy is based on a metric combining spatial correlation and root-mean-square error.

The performance of the supermodels is evaluated for the simulation of EPEs in West Africa through a case study of an extreme precipitation event that occurred on 5 September 2020 in Senegal. The results show a significant improvement in the representation of extreme precipitation compared to individual WRF configurations, highlighting the strong potential of Supermodelling to improve extreme event prediction in regions with sparse observational coverage.