Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model

Heavy precipitation events (HPEs) can lead to nat- ural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rain- fall patterns is crucial for coping with these events. Informa- tion from rain gauges is generally limited due to the sparse- ness of the networks, especially in the presence of sharp cli- matic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the perfor- mance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were charac- terised by the highest rain intensities; however, for short du- rations, the highest rain intensities were found for the in- land desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrela- tion distance) and time (less than 5 min). WRF model simu- lations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited er-rors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipita- tion patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location er- rors.