Hierarchical Autoencoder-based Lossy Compression for Large-scale
  High-resolution Scientific Data
Hieu Le Hernan Santos Jian Tao
Lossy compression has become an important technique to reduce data size in
many domains. This type of compression is especially valuable for large-scale
scientific data, whose size ranges up to several petabytes. Although
Autoencoder-based models have been successfully leveraged to compress images
and videos, such neural networks have not widely gained attention in the
scientific data domain. Our work presents a neural network that not only
significantly compresses large-scale scientific data but also maintains high
reconstruction quality. The proposed model is tested with scientific benchmark
data available publicly and applied to a large-scale high-resolution climate
modeling data set. Our model achieves a compression ratio of 140 on several
benchmark data sets without compromising the reconstruction quality. Simulation
data from the High-Resolution Community Earth System Model (CESM) Version 1.3
over 500 years are also being compressed with a compression ratio of 200 while
the reconstruction error is negligible for scientific analysis.