【Hackathon 5th No.61】Skillful nowcasting of extreme precipitation with NowcastNet #705
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Solver具有训练、推理和可视化的功能，可以参考bracket案例的evaluate函数进行实现。 |
rfcs/Science/20231018_nowcastnet.md
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| [No.61：Skillful nowcasting of extreme precipitation with NowcastNet](https://github.com/PaddlePaddle/community/blob/master/hackathon/hackathon_5th/%E3%80%90PaddlePaddle%20Hackathon%205th%E3%80%91%E5%BC%80%E6%BA%90%E8%B4%A1%E7%8C%AE%E4%B8%AA%E4%BA%BA%E6%8C%91%E6%88%98%E8%B5%9B%E7%A7%91%E5%AD%A6%E8%AE%A1%E7%AE%97%E4%BB%BB%E5%8A%A1%E5%90%88%E9%9B%86.md#no61skillful-nowcasting-of-extreme-precipitation-with-nowcastnet) | ||
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| 气象雷达回波以低于 2 公里的空间分辨率和高达 5 分钟的时间分辨率提供云观测，非常适合降水临近预报。利用这些数据的自然选择是数值天气预报，它根据求解大气耦合原始方程来预测降水。然而，这些方法即使在超级计算平台上实施，也将数值天气预报的更新周期限制在几小时，空间分辨率限制在中尺度，而极端天气过程通常表现出数十分钟的寿命和对流尺度的个体特征。DARTS10 和 pySTEPS9 等替代方法基于仅受连续性方程启发的平流方案。这些方法分别求解运动场的未来状态和来自复合雷达观测的强度残差，并迭代地平流过去的雷达场以预测未来的场。平流方案部分遵守降水演化的物理守恒定律，能够在 1h，但超过该范围后，它很快就会退化，导致较高的位置误差，并失去小的对流特征。由于现有的平流实现未能纳入非线性演化模拟和端到端的预测误差优化，这些误差以不受控制的方式在自回归平流过程中累积。 |
zhiminzhang0830
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Oct 20, 2023
luotao1
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Oct 22, 2023
…h NowcastNet (PaddlePaddle#705) * Add nowcastnet * Fix
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【Hackathon 5th No.61】Skillful nowcasting of extreme precipitation with NowcastNet
https://github.com/PaddlePaddle/community/blob/master/hackathon/hackathon_5th/%E3%80%90PaddlePaddle%20Hackathon%205th%E3%80%91%E5%BC%80%E6%BA%90%E8%B4%A1%E7%8C%AE%E4%B8%AA%E4%BA%BA%E6%8C%91%E6%88%98%E8%B5%9B%E7%A7%91%E5%AD%A6%E8%AE%A1%E7%AE%97%E4%BB%BB%E5%8A%A1%E5%90%88%E9%9B%86.md#no61skillful-nowcasting-of-extreme-precipitation-with-nowcastnet