2022/10/22 by Langwen Huang, Torsten Hoefler, Huang, Langwen +1 · 4 citations
Earth and Planetary Sciences · Computer Science · #Meteorological Phenomena and Simulations #Advanced Data Compression Techniques #Image and Signal Denoising Methods
paper · pdf · doi:10.48550/arxiv.2210.12538
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.