Do Convection-Permitting Ensembles Lead to More Skillful Short-Range Probabilistic Rainfall Forecasts over Tropical East Africa?

Abstract

Convection-permitting ensemble prediction systems (CP-ENS) have been implemented in the midlatitudes for weather forecasting time scales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the Met Office over tropical East Africa, for 24 cases in the period April–May 2019. The CP-ENS, an ensemble with parameterized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skillful forecast for both 3- and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighborhood approach, show that the CP-ENS is skillful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the midlatitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is underspread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance.