Statistical postprocessing of medium-range weather forecasts is an important component of
modern forecasting systems. Since the beginning of modern data science, numerous new …

The main approach to probabilistic weather forecasting has been the use of ensemble
forecasting. In ensemble forecasting, the probability information is generally derived by …

This study aims to decipher the interactions of a precipitation post-processor and several
other tools for uncertainty quantification implemented in a hydrometeorological forecasting …

Strong winds associated with extratropical cyclones are one of the most dangerous natural
hazards in Europe. These high winds are mostly associated with five mesoscale dynamical …

Abstract The Madden–Julian Oscillation (MJO) is a major source of predictability on the sub-
seasonal (10 to 90 d) timescale. An improved forecast of the MJO may have important …

Although, by now, ensemble-based probabilistic forecasting is the most advanced approach
to weather prediction, ensemble forecasts still suffer from a lack of calibration and/or display …

Statistical post-processing techniques are widely used to reduce systematic biases and
quantify forecast uncertainty in numerical weather prediction (NWP). In this study, we …

Near-surface winds over complex terrain generally feature a large variability at the local
scale. Forecasting these winds requires high-resolution numerical weather prediction (NWP) …

With a rapidly increasing capacity of electricity generation from wind power, the demand for
accurate power production forecasts is growing. To date, most wind power installations have …

Physical numerical weather prediction models have biases and miscalibrations that can
depend on the weather situation, which makes it difficult to post-process them effectively …