Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
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IMPA-Net improves extreme convective radar nowcasting by incorporating meteorology-aware multi-scale attention and a three-level asymmetric dynamic loss, raising Heidke Skill Score at ≥45 dBZ from 0.049 to 0.143 versus SimVP while preserving spectral energy better than baselines.
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Multi-Quantile Regression for Extreme Precipitation Downscaling
Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
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IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting
IMPA-Net improves extreme convective radar nowcasting by incorporating meteorology-aware multi-scale attention and a three-level asymmetric dynamic loss, raising Heidke Skill Score at ≥45 dBZ from 0.049 to 0.143 versus SimVP while preserving spectral energy better than baselines.