Preprints
https://doi.org/10.5194/egusphere-2024-1945
https://doi.org/10.5194/egusphere-2024-1945
29 Jul 2024
 | 29 Jul 2024

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

Georgy Ayzel and Maik Heistermann

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 five minute 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Share

Journal article(s) based on this preprint

03 Jan 2025
Brief communication: Training of AI-based nowcasting models for rainfall early warning should take into account user requirements
Georgy Ayzel and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 25, 41–47, https://doi.org/10.5194/nhess-25-41-2025,https://doi.org/10.5194/nhess-25-41-2025, 2025
Short summary
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Forecasting rainfall over the next hour is an essential feature of early warning systems. Deep...
Share