Multivariate post‐processing of probabilistic sub‐seasonal weather regime forecasts

Abstract

Reliable forecasts of quasi‐stationary, recurrent, and persistent large‐scale atmospheric circulation patterns—so‐called weather regimes—are crucial for various socio‐economic sectors, including energy, health, and agriculture. Despite steady progress, probabilistic weather regime predictions still exhibit biases in the exact timing and amplitude of weather regimes. This study thus aims at advancing probabilistic weather regime predictions in the North Atlantic–European region through ensemble post‐processing. Here, we focus on the representation of seven year‐round weather regimes in sub‐seasonal to seasonal reforecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The manifestation of each of the seven regimes can be expressed by a continuous weather regime index, representing the projection of the instantaneous 500‐hPa geopotential height anomalies (A) onto the respective mean regime pattern. We apply a two‐step ensemble post‐processing involving first univariate ensemble model output statistics and second ensemble copula coupling, which restores the multivariate dependence structure. Compared with current forecast calibration practices, which rely on correcting the field by the lead‐time‐dependent mean bias, our approach extends the forecast skill horizon for daily/instantaneous regime forecasts moderately by 1 day (from 13.5 to 14.5 days). Additionally, to our knowledge our study is the first to evaluate the multivariate aspects of forecast quality systematically for weather regime forecasts. Our method outperforms current practices in the multivariate aspect, as measured by the energy and variogram score. Still, our study shows that, even with advanced post‐processing, weather regime prediction becomes difficult beyond 14 days, which likely points towards intrinsic limits of predictability for daily/instantaneous regime forecasts. The proposed method can easily be applied to operational weather regime forecasts, offering a neat alternative for cost‐ and time‐efficient post‐processing of real‐time weather regime forecasts.