Bochow Nils, Poltronieri Anna, Rypdal Martin, Boers Niklas
Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway, Tromsø, Norway.
Physics of Ice, Climate and Earth, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.
Sci Adv. 2025 Apr 4;11(14):eadp0558. doi: 10.1126/sciadv.adp0558. Epub 2025 Apr 2.
Historical records of climate fields are often sparse because of missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we use a recently introduced deep learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach, we are able to realistically reconstruct large and irregular areas of missing data and to reproduce known historical events, such as strong El Niño or La Niña events, with very little given information. Our method outperforms the widely used statistical kriging method, as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.
由于测量数据缺失，气候场的历史记录往往很稀疏，尤其是在大规模卫星任务引入之前。已经引入了几种基于统计和模型的方法来填补空白并重建历史记录。在这里，我们使用一种最近引入的基于傅里叶卷积的深度学习方法，该方法在数值气候模型输出上进行训练，以重建历史气候场。使用这种方法，我们能够逼真地重建大面积不规则的缺失数据区域，并以极少的已知信息重现已知的历史事件，如强烈的厄尔尼诺或拉尼娜事件。我们的方法优于广泛使用的统计克里金法以及其他最近的机器学习方法。该模型能够推广到比其训练分辨率更高的分辨率，并且可用于各种气候场。此外，它允许对模型训练期间从未见过的掩码进行图像修复。
