  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.
