Towards a data-driven nowcasting of severe weather based on geostationary satellite data

Nowcasting severe weather is crucial not only to mitigate the effects of extreme weather events like storms and flash floods but also to support decision-makers on weather-dependent operations like aviation and outdoor events. Stunning pace of developments in Artificial Intelligence (AI) and increasing availability of high resolution data from different sensors motivate using AI in weather prediction, particularly in nowcasting due to its rapidly changing dynamics in short timescales.

Meteosat Third Generation (MTG) will greatly improve our capacity on nowcasting with its high resolution sensors on board. However, data-driven algorithms are needed to use the information stored within large volumes of MTG data for nowcasting in an efficient way. Therefore, we are developing AI based nowcasting algorithms fusing remote sensing data with ground based radar mosaics for nowcasting severe weather. 

As MTG data are not available yet (expected to be launched in late 2022), we use data from GOES satellites as the primary data source, which have comparable sensors with MTG. Specifically, we use the Storm Event ImageRy (SEVIR) dataset in the initial phase of the study which contains more than 10000 image sequences, 20 % of which contain storm events reported by NOAA. Each of these image sequences cover 384 x 384 km in space and 4-hour in time, containing three bands from the visible and infrared spectrums, and the lightning mapper data from GOES-16 with Vertically Integrated Liquid (VIL) mosaics derived from ground-based radar. We obtained promising results to reproduce spatial variations of VIL with Generative Adversarial Network (GAN) as a baseline. We are currently developing Recurrent Neural Network (RNN) based models to reproduce temporal variations of VIL to incorporate temporal information in GAN models. Furthermore, we are using European Weather Cloud, which not only provides a strong computation infrastructure but also fosters collaboration across projects. Development of efficient AI algorithms for nowcasting severe weather using GOES data will enable the opportunity to fully use MTG data on nowcasting severe weather.