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

Reliable forecasts of quasi-stationary, recurrent, and persistent large-scale atmo-
spheric 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 Euro-
pean Centre for Medium-Range Weather Forecasts (ECMWF). The manifesta-
tion of each of the seven regimes can be expressed by a continuous weather
regime index, representing the projection of the instantaneous 500-hPa geopo-
tential height anomalies (Z500A) 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. ... mehrCompared with current forecast calibration
practices, which rely on correcting the Z500 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 fore-
cast quality systematically for weather regime forecasts. Our method outperforms
current practices in the multivariate aspect, as measured by the energy and var-
iogram 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 fore-
casts, offering a neat alternative for cost- and time-efficient post-processing of
real-time weather regime forecasts.