Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …

A probabilistic forecast takes the form of a predictive probability distribution over future
quantities or events of interest. Probabilistic forecasting aims to maximize the sharpness of …

Ensembles used for probabilistic weather forecasting tend to be biased and
underdispersive. This paper proposes a statistical method for postprocessing ensembles …

Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
Page 1 Statistical Science 2013, Vol. 28, No. 4, 616–640 DOI: 10.1214/13-STS443 © Institute of …

Statistical post‐processing of dynamical forecast ensembles is an essential component of
weather forecasting. In this article, we present a post‐processing method which generates …

Statistical methods to bias correct global or regional climate model output are now common
to get data closer to observations in distribution. However, most bias correction (BC) …

Around the world wind energy is starting to become a major energy provider in electricity
markets, as well as participating in ancillary services markets to help maintain grid stability …

Generative machine learning methods for multivariate ensemble postprocessing Page 1
The Annals of Applied Statistics 2024, Vol. 18, No. 1, 159–183 https://doi.org/10.1214/23-AOAS1784 …

We propose a method for post‐processing an ensemble of multivariate forecasts in order to
obtain a joint predictive distribution of weather. Our method utilizes existing univariate post …

In weather forecasting, non-homogeneous regression (NR) is used to statistically post-
process forecast ensembles in order to obtain calibrated predictive distributions. For wind …