Compressing multidimensional weather and climate data into neural networks

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 300 to more than 3,000, 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 significant artifacts. When using the resulting neural network as a 790 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. Numerical weather and climate simulations can produce hundreds of terabytes to several petabytes of data (Kay et al., 2015; Hersbach et al., 2020) and such data are growing even bigger as higher resolution simulations are needed to tackle climate change and associated extreme weather (Schulthess et al., 2019; Schär et al., 2019). In fact, kilometer-scale climate data are expected to be one of, if not the largest, scientific datasets worldwide in the near future.