Numerical weather prediction has traditionally been based on the models that discretize the dynamical and physical equations of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data‐driven medium‐range weather forecasting with first studies exploring the feasibility of such an approach. To accelerate progress in this area, the WeatherBench benchmark challenge was defined. Here, we train a deep residual convolutional neural network (Resnet) to predict geopotential, temperature and precipitation at 5.625° resolution up to 5 days ahead. To avoid overfitting and improve forecast skill, we pretrain the model using historical climate model output before fine‐tuning on reanalysis data. The resulting forecasts outperform previous submissions to WeatherBench and are comparable in skill to a physical baseline at similar resolution. We also analyze how the neural network creates its predictions and find that, for the case studies analyzed, the model has learned physically reasonable correlations. Finally, we perform scaling experiments to estimate the potential skill of data‐driven approaches at higher resolutions.

中文翻译：

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数据驱动的中程天气预报，气候模拟对Resnet进行了预训练：WeatherBench的新模型

传统上，数值天气预报是基于离散化大气动力学方程和物理方程的模型。但是，近来，深度学习的兴起引起了人们对纯粹由数据驱动的中程天气预报的兴趣，这是第一个研究探索这种方法可行性的研究。为了加快该领域的进步，定义了WeatherBench基准挑战。在这里，我们训练了一个深度残差卷积神经网络（Resnet），以预测未来5天以5.625°的分辨率预测地势，温度和降水。为了避免过度拟合并提高预测技能，我们在重新分析数据进行微调之前，使用历史气候模型输出对模型进行了预训练。所得的预报优于之前提交给WeatherBench的预报，并且在类似分辨率下的技巧与物理基准相当。我们还分析了神经网络如何创建其预测，并发现，对于所分析的案例研究，该模型已学会了物理上合理的相关性。最后，我们进行缩放实验，以估计更高分辨率下数据驱动方法的潜在技能。