Brief communication: Training of AI-based nowcasting models for rainfall early warning should take into account user requirements

Abstract

In the field of precipitation nowcasting, deep learning (DL) has emerged as an alternative to conventional tracking and extrapolation techniques. However, DL struggles to adequately predict heavy precipitation, which is essential in early warning. By taking into account specific user requirements, though, we can simplify the training task and boost predictive skill. As an example, we predict the cumulative precipitation of the next hour (instead of 5 min increments) and the exceedance of thresholds (instead of numerical values). A dialogue between developers and users should identify the requirements to a nowcast and how to consider these in model training.