deep-weather:深度学习用于后处理集成天气预报
深度学习用于后处理集成天气预报
我们使可用的数据，以及为需要运行在我们的模型中的代码通过这个资料库。 我们希望我们的发现和数据可以用于进一步推进天气预报研究。
研究内容
我们的研究重点是应用从深度学习到集成天气预报的最新架构。 为了实现这一目标，我们使用来自ECMWF的全局重新预测数据（我们称为ENS10 ）以及重新分析数据ERA5 。 更具体地说，ENS10旨在为研究人员提供基本的预报值数据集，这些数据将用于现代数值天气预报管道中。 使用ERA5数据作为基本事实，然后使用整体预测的子集进行后处理和改进。 我们的目标是帮助天气预报中心更便宜，更准确地预测极端天气事件。
在热带气旋的情况下，我们使用五个轨迹的子集，在全部10个成员合奏中的连续排名概率得分（CRPS）中进行了测量，相对于预测技能，其相对提高了26％以上。 此外，这些模型可以更准确地预测旋风的未来路径。
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