Forecasting Convective Storms Trajectory and Intensity by Neural Networks

Niccolò Borghi, Giorgio Guariso and Matteo Sangiorgio ()
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Niccolò Borghi: Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, I-20133 Milan, Italy
Giorgio Guariso: Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, I-20133 Milan, Italy
Matteo Sangiorgio: Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, I-20133 Milan, Italy

Forecasting, 2024, vol. 6, issue 2, 1-17

Abstract: Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network approach to forecast the convective cell trajectory and intensity, using, as an example, a region in northern Italy that is frequently hit by convective storms in spring and summer. The predictor input is constituted by radar-derived information about the center of gravity of the cell, its reflectivity (a proxy for the intensity of the precipitation), and the area affected by the storm. The essential characteristic of the proposed approach is that the neural network directly forecasts the evolution of the convective cell position and of the other features for the following hour at a 5-min temporal resolution without a relevant loss of accuracy in comparison to predictors trained for each specific variable at a particular time step. Besides its accuracy ( R 2 of the position is about 0.80 one hour in advance), this machine learning approach has clear advantages over the classical numerical weather predictors since it runs at orders of magnitude more rapidly, thus allowing for the implementation of a real-time early-warning system.

Keywords: weather forecasting; convective cells; multi-output neural network; storm positioning; radar reflectivity (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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