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.
Nature, 597(7878)
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
A MATLAB/ONNX testbed integrates the Pangu AI model with PID closed-loop control to perform single-input single-output perturbation-response experiments on typhoon track and intensity.
citing papers explorer
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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.
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A Simulation Methodology Testbed for Typhoon Sensitivity Analysis: Framework Development and Perturbation-Response Experiments with the Pangu Weather Model
A MATLAB/ONNX testbed integrates the Pangu AI model with PID closed-loop control to perform single-input single-output perturbation-response experiments on typhoon track and intensity.