Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the data, and the resulting parameters are taken as a compact representation of the original grid-based data. While compression ratios range from 300x to more than 3,000x, our method outperforms the state-of-the-art compressor SZ3 in terms of weighted RMSE, MAE. It can faithfully preserve important large scale atmosphere structures and does not introduce artifacts. When using the resulting neural network as a 790x compressed dataloader to train the WeatherBench forecasting model, its RMSE increases by less than 2%. The three orders of magnitude compression democratizes access to high-resolution climate data and enables numerous new research directions.

翻译：天气和气候模拟产生高分辨率数据,随后由研究人员分析这些数据,以便了解气候变化或恶劣天气。我们提出了压缩这种多层面天气和气候数据的新方法:一个基于协调的神经网络经过培训,以过度配置数据,由此得出的参数被作为原始基于网格的数据的缩压表示。压缩比率从300x到3 000x不等,但我们的方法在加权的RMSE、MAE方面优于先进的压缩压缩器SZ3。它可以忠实地保存重要的大型大气结构,不引进文物。当将由此形成的神经网络用作790x压缩数据载荷来培训天气港预报模型时,其ERME增加了不到2%。三个数量级的压缩使高分辨率的气候数据民主化,并使得许多新的研究方向得以实现。