Statistical postprocessing techniques are nowadays key components of the forecasting
suites in many national meteorological services (NMS), with, for most of them, the objective …

Forecasts are always wrong, otherwise, they are merely deterministic calculations. Besides
leveraging advanced forecasting methods, post-processing has become a standard practice …

Abstract Quantile Regression Averaging (QRA) has sparked interest in the electricity price
forecasting community after its unprecedented success in the Global Energy Forecasting …

Today's ensemble weather prediction systems provide reliable and sharp probabilistic
forecasts—yet they are still rarely communicated to outside users because of two main …

Statistical postprocessing of global ensemble weather forecasts is revisited by leveraging
recent developments in machine learning. Verification of past forecasts is exploited to learn …

Based on the principle “learn from past errors to correct current forecasts,” statistical
postprocessing consists of optimizing forecasts generated by numerical weather prediction …

Weather forecast ensembles, consisting of multiple integrations of dynamical forecast
models, are intended to represent effects of initial-condition sensitivity and model structural …

Current practice in predicting future weather is the use of numerical weather prediction
(NWP) models to produce ensemble forecasts. Despite of enormous improvements over the …

Statistical postprocessing of ensemble forecasts is widely applied to make reliable
probabilistic weather forecasts. Motivated by the fact that nature imposes few restrictions on …

Renewable energy generation capacities are being deployed at a rapid pace, now reaching
a total of more than 800 GW worldwide, if adding up generation capacities for wind and solar …