Generalization of ML-based post-processing (DRN) to global high-resolution forecasts

Ascertain whether neural network–based distributional regression post-processing methods, specifically the Distributional Regression Network (DRN) trained on deterministic outputs of data-driven weather models, generalize effectively to global high-resolution gridded forecasts given the large data volumes and high dimensionality.

Background

The paper employs DRN as a post-hoc distributional regression method to convert deterministic outputs into probabilistic forecasts on a European domain and demonstrates strong performance, especially at shorter lead times.

The authors caution that scaling such ML-based post-processing methods to global high-resolution grids introduces substantial computational and data challenges, explicitly noting that it is unknown whether the methods will generalize well under those conditions.