205 citations · 294 across the 5 of their papers we have counts for
6 papers
Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts
Romain Pic, Clément Dombry, Philippe Naveau +1
Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming. We propose a global…
Proper Scoring Rules for Multivariate Probabilistic Forecasts based on Aggregation and Transformation
Romain Pic, Clément Dombry, Philippe Naveau +1
Proper scoring rules are an essential tool to assess the predictive performance of probabilistic forecasts. However, propriety alone does not ensure an informative characterization…
Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk
Romain Pic, Clément Dombry, Philippe Naveau +1
The theoretical advances on the properties of scoring rules over the past decades have broadened the use of scoring rules in probabilistic forecasting. In meteorological forecastin…
Statistical Postprocessing for Weather Forecasts -- Review, Challenges and Avenues in a Big Data World
Stéphane Vannitsem, John Bjørnar Bremnes, Jonathan Demaeyer +21
Statistical postprocessing techniques are nowadays key components of the forecasting suites in many National Meteorological Services (NMS), with for most of them, the objective of…
Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions
Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau +1
Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased…
Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting
Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau +1
Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (…