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Uncertainty-aware methods for enhancing rainfall prediction with deep-learning based post-processing segmentation
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DOI：10.1016/j.cageo.2025.105992.png)
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Abstract
En 中文
• Probabilistic deep learning models to enhance NWP post-processing aggregation. • Design to enhance forecasting accuracy preserving uncertainty quantification. • Introduces SDE U-Net, an uncertainty-aware model for segmentation tasks. • Evaluation on cumulative precipitation forecasting for non-intense and intense events.
Keywords:
Rainfall prediction
Probabilistic deep learning
Deep learning post-processing
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