Post-processing output from ensembles with and without parametrised convection, to create accurate, blended, high-fidelity rainfall forecasts

TL;DR

This study combines post-processed global and limited-area ensemble rainfall forecasts to produce high-fidelity, probabilistic 6-hour rainfall predictions, demonstrating improved accuracy and usefulness for flash flood forecasting.

Contribution

It introduces a novel method of blending global and limited-area ensemble outputs with post-processing to enhance rainfall forecast accuracy.

Findings

ecPoint ensemble is most skilful overall

Post-processed COSMO adds value during summer convective events

Merged forecasts outperform individual ensemble products

Abstract

Flash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address this, one can try to use output from convection-parametrising (global) ensembles, post-processed to forecast at point-scale, or convection-resolving limited area ensembles. In this study, we combine both. First, we apply the "ecPoint-rainfall" post-processing to the ECMWF global ensemble. Then, we use 2.2km COSMO LAM ensemble output (centred on Italy), and also post-process it using a scale-selective neighbourhood approach to compensate for insufficient members. The two components then undergo lead-time-weighted blending, to create the final probabilistic 6h rainfall forecasts. Product creation for forecasters constituted the "Italy Flash Flood use case" within the EU-funded MISTRAL project and it will be a real-time open-access product. One year of verification shows that ecPoint is the most skilful ensemble product. The post-processed COSMO ensemble adds most value to summer convective events in the evening, when the global model has an underprediction bias. In two heavy rainfall case studies we observed underestimation of the largest point totals in the raw ECMWF ensemble, and overestimation in the raw COSMO ensemble. However, ecPoint increase the value and highlighted best the most affected areas, whilst post-processing of COSMO diminished extremes by eradicating unreliable detail. The final merged products looked best from a user perspective and seemed to be the most skilful of all. Although our LAM post-processing does not implicitly include bias correction (a topic for further work) our study nonetheless provides a unique blueprint for successfully combining ensemble rainfall forecasts from global and LAM systems around the world. It also has important implications for forecast products as global ensembles move ever closer to having convection-permitting resolution.