ML4WX

Nowcasting

Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands

Charlotte Cambier van Nooten, Koert Schreurs, Jasper S. Wijnands, Hidde Leijnse, Maurice J. Schmeits, Kirien Whan, Yuliya Shapovalova

Artificial Intelligence for the Earth Systems · · KNMI (Royal Netherlands Meteorological Institute)

PaperPDF

In one sentence. A deep generative nowcasting model with a balanced loss and temperature input extends skillful heavy-rain nowcasts over the Netherlands to 60-minute lead times.

Labels

Topic
Nowcasting, Extremes & hazards
Type
New model or method
Method
GAN or VAE, CNN / U-Net
Time scale
0–6 h
Domain
Regional
Data
Radar, Station / in-situ
Status
Research
Identifiers
DOI:10.1175/aies-d-23-0017.1 OpenAlex:W4386740901
Added
2026-10-22

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