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Status: Unchecked ×Keyword: optical flow comparison ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Precipitation Measurement and Analysis

   RSG-GAN: A GAN-Based Precipitation Nowcasting Model Integrating Radar QPE, GOES-16 SWD, and GNSS ZTDs

   Lu, Luo, Zheng, Wang, Li and Wang · IEEE Transactions on Geoscience and Remote Sensing · 2025

   The paper proposes RSG-GAN, a neural network that fuses radar, GOES-16 satellite and GNSS delay data to forecast rain a short time ahead, and reports it outperforming optical flow and other deep learning models.

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   1. UncheckedAcross 189 rain periods, RSG-GAN had the lowest average errors over a 120-minute lead time: MAE 0.34 mm/h and RMSE 0.61 mm/h, versus optical flow.“Statistical analysis across 189 precipitation periods reveals that the RSG-GAN model achieves the lowest average Mean Absolute Error (MAE) of 0.34 mm/h and Root Mean Square Error (RMSE) of 0.61 mm/h over a 120-minute lead time, with reductions of 36.3% and 41.6%, respectively, compared to the optic…”
   2. UncheckedThe RSG-GAN nowcasting model is reported to beat three other deep learning methods (CM-STJointNet, MM-RNN, MM-STMixGAN) on accuracy and event detection.“Compared with three deep learning-based methods (CM-STJointNet, MM-RNN and MM-STMixGAN), the RSG-GAN model consistently shows superior performance in both prediction accuracy and event detection.”
   3. UncheckedTransfer learning tests on the public SEVIR dataset are reported to show that RSG-GAN generalises well beyond its original study area.“Furthermore, transfer learning experiments on the publicly dataset Storm EVent ImageRy (SEVIR) also demonstrate the remarkable generalization capability of RSG-GAN model.”