Valid Error Bars for Neural Weather Models using Conformal Prediction

arXiv:2406.14483v1 Announce Type: new
Abstract: Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do not quantify the uncertainty associated with their forecasts. This limits the trust in the model and the usefulness of the forecasts. In this work we construct and formalise a conformal prediction framework as a post-processing method for estimating this uncertainty. The method is model-agnostic and gives calibrated error bounds for all …

abstract arxiv cs.lg error forecasting however physics potential prediction trust type uncertainty weather weather forecasting