Forecasting, Volume 6, Issue 2 (June 2024) – 13 articles
Cover Story (view full-size image):
Convective storms are dangerous weather phenomena that can cause heavy rainfalls. Predicting them is difficult due to their high speed and variability, but essential to provide accurate early warnings. In recent years, machine learning tools have been tested as an alternative to numerical weather prediction models that rely on an explicit physical description of the atmospheric processes. This paper presents an innovative approach using artificial neural networks to forecast the storm’s trajectory, its radar reflectivity (which is related to the rainfall intensity), and the area hit by the storm. The results obtained on a northern Italian region often affected by convective storms in spring and summer show that the neural model is accurate and much faster than classical weather prediction models, making real-time early warnings possible. View this paper
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