Spatiotemporally adaptive compression for scientific dataset with
  feature preservation -- a case study on simulation data with extreme climate
  events analysis
Qian Gong Chengzhu Zhang Xin Liang Viktor Reshniak Jieyang Chen Anand Rangarajan Sanjay Ranka Nicolas Vidal Lipeng Wan Paul Ullrich Norbert Podhorszki Robert Jacob Scott Klasky
Scientific discoveries are increasingly constrained by limited storage space
and I/O capacities. For time-series simulations and experiments, their data
often need to be decimated over timesteps to accommodate storage and I/O
limitations. In this paper, we propose a technique that addresses storage costs
while improving post-analysis accuracy through spatiotemporal adaptive,
error-controlled lossy compression. We investigate the trade-off between data
precision and temporal output rates, revealing that reducing data precision and
increasing timestep frequency lead to more accurate analysis outcomes.
Additionally, we integrate spatiotemporal feature detection with data
compression and demonstrate that performing adaptive error-bounded compression
in higher dimensional space enables greater compression ratios, leveraging the
error propagation theory of a transformation-based compressor.
  To evaluate our approach, we conduct experiments using the well-known E3SM
climate simulation code and apply our method to compress variables used for
cyclone tracking. Our results show a significant reduction in storage size
while enhancing the quality of cyclone tracking analysis, both quantitatively
and qualitatively, in comparison to the prevalent timestep decimation approach.
Compared to three state-of-the-art lossy compressors lacking feature
preservation capabilities, our adaptive compression framework improves
perfectly matched cases in TC tracking by 26.4-51.3% at medium compression
ratios and by 77.3-571.1% at large compression ratios, with a merely 5-11%
computational overhead.