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.

以自适应误差控制的时空自适应压缩为基础，该研究提出了一种解决存储成本问题的新技术，在提高后续分析精度的同时，通过减少数据精度和增加时间步长频率，实现了更准确的分析结果。通过与现有方法进行比较，该方法在中等和大的压缩比下提高了 26.4-51.3% 和 77.3-571.1% 的热带气旋追踪的匹配情况，并且仅有 5-11% 的计算开销。

科学数据的时空自适应压缩与特征保留 -- 以极端气候事件分析为例的案例研究

Spatiotemporally adaptive compression for scientific dataset with  feature preservation -- a case study on simulation data with extreme climate  events analysis