NOWCASTING OF EXTREME PRECIPITATION USING DEEP GENERATIVE MODELS
Haoran Bin (TU Delft); Max Kyryliuk (TU Delft); Zhiyi Wang (TU Delft); Cristian Meo (TUDelft); Yanbo Wang (TU Delft); Ruben Imhoff (Deltares); Remko Uijlenhoet (TU Delft); Justin Dauwels (TU Delft)
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Nowcasting is an observation-based method that uses the current
state of the atmosphere to forecast future weather conditions over
several hours. Recent studies have shown the promising potential
of using deep learning models for precipitation nowcasting. In this
paper, novel deep generative models are proposed for precipitation
nowcasting. These models are equipped with extreme-value
losses to more reliably predict extreme precipitation events. The
proposed deep generative model contains a Vector Quantization
Generative Adversarial Network and a Transformer (“VQGAN +
Transformer”). For enhanced modeling and forecasting of extreme
events, Extreme Value Loss (EVL) is incorporated in the autoregressive
Transformer. The numerical results show that the proposed
model achieves comparable performance with the state-of-the-art
conventional nowcasting method PySTEPS for predicting nominal
values. By incorporating an EVL, the proposed model yields more
accurate nowcasting of extreme precipitation.