Rainfall Nowcasting Exploiting Machine-Learning Techniques: A Case Study in Southern Italy

Abstract

During emergency situations, short-term rainfall forecasting is crucial for human life-saving and economic damage mitigation. However, due to the high interconnection among the meteorological variables, the rainfall evolution mechanism is challenging to predict. Since machine-learning techniques do not require any previous physical assumption, this study suggests a rainfall nowcasting model based on Artificial Neural Networks. The proposed model provides punctual rainfall predictions at three different lead times: 30 min, 1 h, and 2 h. The analysis is based on 10 years of records from meteorological stations over the Campania region, southern Italy. Several feed-forward neural network models were trained with 350 spatial rainfall events, with 10 min time step. The approach produced consistent predictions and learned the relationship describing space-time rainfall evolution. Characterized by high update frequency and short computational time, the procedure is suitable for real-time early warning systems.

Keywords

• short-term prediction
• precipitation forecasting
• artificial neural networks
• cumulative precipitation fields
• spatial rainfall events