Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets

Abstract

Predicting extreme weather events in a short time period and their developing in localized areas is a challenge. The nowcasting of severe and extreme weather events is an issue for air traffic management and control because it affects aviation safety, and determines delays and diversions. This work is part of a larger study devoted to nowcasting rain and wind speed in the area of Malpensa airport by merging different datasets. We use as reference the weather station of Novara to develop a nowcasting machine learning model which could be reusable in other locations. In this location we have the availability of ground-based weather sensors, a Global Navigation Satellite System (GNSS) receiver, a C-band radar and lightning detectors. Our analysis shows that the Long Short-Term Memory Encoder Decoder (LSTM E/D) approach is well suited for the nowcasting of meteorological variables. The predictions are based on 4 different datasets configurations providing rain and wind speed nowcast for 1 h with a time step of 10 min. The results are very promising with the extreme wind speed probability of detection higher than 90%, the false alarms lower than 2%, and a good performance in extreme rain detection for the first 30 min. The configuration using just weather stations and GNSS data in input provides excellent performances and should be preferred to the other ones, since it refers to the pre-convective environment, and thus can be adaptable to any weather conditions.