Deep Learning for rain and lightning nowcasting

Nowcasting (very short-term forecasting) in meteorology is a very important topic for agriculture, human safety, and renewable energy production. Recently, a precipitation nowcasting method has been proposed that achieves state of the art results using deep learning techniques. In this work, we present the application of the method on a novel dataset acquired from echo patterns of weather radar at the regional scale in Trentino-Suedtirol, in the Italian Alps. The model can forecast rainfall up to 75’ ahead at a spatial resolution of 1.65km based on 5 frames of recent (25’) radar data. Further, we show that the model can manage blocking effects due to orography. We also introduce an evolution of the method that is applied to lighting prediction of the same area using a dataset of 95.7K lightning strikes gathered from a collaborative lightning location network. This evolved model can forecast lightning strikes up to 30’ ahead at a spatial resolution of 6.6Km. The performances of the models are assessed by CSI (Critical success index), FAR (False alarm rate), POD (Probability of detection) and Correlation measures commonly used in meteorology, achieving state of the art results. We describe a deep learning framework for precipitation and lightning nowcasting, applied to weather echo radar and lightning data at a regional scale in Trentino-Suedtirol, in the Italian Alps. Nowcasting, i.e. forecasts obtained by extrapolation for a period of 0 to 6 hours ahead (World Meteorological Organization) relies here on a Conv-LSTM model [1] and it is embedded in an operational context. The model is able to forecast reflectivity up to 75’ ahead at a spatial resolution of 1.65km based on 5 frames of recent (25’) radar data. Further, the model can manage blocking effects due to orography. The framework is then applied to 95.7K events from a collaborative lightning location network in the same region.