A machine-learning approach to thunderstorm forecasting through post-processing of simulation data

TL;DR

This paper presents SALAMA, a neural network model that improves thunderstorm forecasting by post-processing NWP data and lightning observations, achieving higher skill at lead times up to eleven hours.

Contribution

The study introduces SALAMA, a novel machine learning approach that enhances thunderstorm prediction accuracy using pixel-wise NWP data and lightning observations.

Findings

SALAMA outperforms traditional NWP reflectivity-based forecasts.

Forecast skill increases linearly with spatial scale.

Reliable calibration of thunderstorm occurrence probabilities.

Abstract

Thunderstorms pose a major hazard to society and economy, which calls for reliable thunderstorm forecasts. In this work, we introduce a Signature-based Approach of identifying Lightning Activity using MAchine learning (SALAMA), a feedforward neural network model for identifying thunderstorm occurrence in numerical weather prediction (NWP) data. The model is trained on convection-resolving ensemble forecasts over Central Europe and lightning observations. Given only a set of pixel-wise input parameters that are extracted from NWP data and related to thunderstorm development, SALAMA infers the probability of thunderstorm occurrence in a reliably calibrated manner. For lead times up to eleven hours, we find a forecast skill superior to classification based only on NWP reflectivity. Varying the spatiotemporal criteria by which we associate lightning observations with NWP data, we show that the time scale for skillful thunderstorm predictions increases linearly with the spatial scale of the forecast.