Increasing NWP Thunderstorm Predictability Using Ensemble Data and Machine Learning

TL;DR

This paper demonstrates that combining ensemble NWP data with machine learning significantly enhances thunderstorm forecast skill, with ensemble averaging and neural networks extending predictability beyond traditional limits.

Contribution

The study introduces a neural network model, SALAMA 1D, that leverages ensemble data to improve thunderstorm prediction and provides an analytic framework explaining the skill gains.

Findings

Ensemble averaging improves forecast skill, matching 11-hour ensemble forecasts to 5-hour deterministic ones.

The analytic expression links skill differences to correlations among ensemble members.

ML models can identify predictable thunderstorm patterns at longer lead times.

Abstract

While numerical weather prediction (NWP) models are essential for forecasting thunderstorms hours in advance, NWP uncertainty, which increases with lead time, limits the predictability of thunderstorm occurrence. This study investigates how ensemble NWP data and machine learning (ML) can enhance the skill of thunderstorm forecasts. Using our recently introduced neural network model, SALAMA 1D, which identifies thunderstorm occurrence in operational forecasts of the convection-permitting ICON-D2-EPS model for Central Europe, we demonstrate that ensemble-averaging significantly improves forecast skill. Notably, an 11-hour ensemble forecast matches the skill level of a 5-hour deterministic forecast. To explain this improvement, we derive an analytic expression linking skill differences to correlations between ensemble members, which aligns with observed performance gains. This expression generalizes to any binary classification model that processes ensemble members individually. Additionally, we show that ML models like SALAMA 1D can identify patterns of thunderstorm occurrence which remain predictable for longer lead times compared to raw NWP output. Our findings quantitatively explain the benefits of ensemble-averaging and encourage the development of ML methods for thunderstorm forecasting and beyond.