Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations.
We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting.
Weather Forecasting Instrumentation and Methods for Astrophysics
We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor.
Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography.
Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output.