Improving the ensemble forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models

Ensemble forecasts of precipitation with sub-seasonal lead times offer  useful information for decision makers when they sufficiently sample the possible outcomes of trajectories. In this study, we aim to improve  precipitation ensemble forecast systems using a stochastic weather generator (SWG) based on analogs of the atmospheric circulation. This approach is tested for sub-seasonal lead times (from 2 to 4 weeks). The SWG ensemble forecasts  yield promising probabilistic skill scores for lead times of 5-10 days for precipitation (Krouma et al, 2022) and for lead times of 40 days for temperature   (Yiou and Déandréis, 2019) . In this work, we adapt the parameters of the SWG to optimize the simulation of European precipitations from ensemble dynamical reforecasts of ECMWF and CNRM. We present the HC-SWG forecasting tool (HC refers to Hindcast and SWG to the stochastic weather generator) based on a combination of dynamical and stochastic models.

We start by computing analogs of Z500 from the ensemble member reforecast of ECMWF (11 members) and CNRM (10 members). Then, we generate an ensemble of 100 members for precipitation over Europe. We evaluate the ensemble forecast of the HC-SWG using skill scores such as the continuous probabilistic score CRPS and ROC curve.

We obtain reasonable forecast skill scores for lead times up to 35 days for different locations in Europe (Madrid, Toulouse, Orly, De Bilt and Berlin). We compare the HC-SWG forecast with other precipitation forecasts to further confirm the benefit of our method. We found that the HC-SWG shows improvement against the ECMWF precipitation forecast until 25 days.