论文标题
数据驱动的中等范围天气预测，并在气候模拟上预估计的重新网络：Weatherbench的新型号
Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench
论文作者
论文摘要
传统上，数值天气预测是基于大气的物理模型。然而，最近，深度学习的兴起引起了人们对纯粹数据驱动的中等范围天气预报的兴趣，并通过探索这种方法的可行性的第一项研究。为了加速该领域的进展，定义了WeatherBench Benchmark挑战。在这里，我们训练一个深度残留的卷积神经网络（RESNET），以预测可提前5天的5.625度分辨率的地球电位，温度和降水。为了避免过度拟合和提高预测技能，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预处理模型。由此产生的预测表现优于先前对Weatherbench的提交，并且在类似分辨率的情况下，技能与物理基线相当。我们还分析了神经网络如何创建其预测，并发现除了某些例外，它与物理推理兼容。最后，我们执行缩放实验，以估计更高分辨率下数据驱动方法的潜在技能。
Numerical weather prediction has traditionally been based on physical models 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 degree 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, with some exceptions, it is compatible with physical reasoning. Finally, we perform scaling experiments to estimate the potential skill of data-driven approaches at higher resolutions.
