期刊名称：Journal of Advances in Modeling Earth Systems
电子版ISSN：1942-2466
出版年度：2021
卷号：13
期号：2
页码：e2020MS002405
DOI：10.1029/2020MS002405
出版社：John Wiley & Sons, Ltd.
摘要：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. Plain Language Abstract Weather forecasts are created by running hugely complex computer simulations that encapsulate our knowledge of how the atmosphere works. This approach has served us well but is there a different way? The paradigm of machine learning proposes learning an algorithm from data rather than building it from physical principles. For several areas like computer vision and natural language processing this has worked exceedingly well, so it just makes sense to try it as well for weather forecasting. This paper presents the latest attempt at training a machine learning weather forecasting model. It is shown that the learned model produces reasonable forecasts, approximately on par with traditional models run on much lower resolution. However, there is still a large gap to current state–of–the–art high–resolution weather models that is unlikely to be closed with a purely data–driven approach because not enough training data exists.
