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Context-aware implicit neural representations to compress Earth systems model data.

Farinaz Mostajeran1, Nikhil M Pawar2, Jonathan M Villarreal2

  • 1Energy & Intelligence Lab, Department of Chemical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.

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|July 17, 2025
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Summary

Context-Aware Implicit Neural Representation (CA-INR) improves climate data compression by using auxiliary physical variables. This method reduces reconstruction errors, enhancing data quality for climate analysis.

Keywords:
Climate contextual informationContext-aware implicit neural representationData compressionEnergy exascale earth systems modelNeural-driven lossy compressionStep decay learning rate

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Area of Science:

  • Earth System Science
  • Data Science
  • Climate Modeling

Background:

  • Multiphysics, multiscale climate models like E3SM produce vast datasets crucial for climate analysis.
  • Data compression is essential for managing large climate datasets, with Implicit Neural Representations (INRs) showing promise.
  • Standard INRs can introduce reconstruction errors, potentially hindering downstream climate research.

Purpose of the Study:

  • To develop a novel Context-Aware Implicit Neural Representation (CA-INR) for improved lossy compression of climate data.
  • To evaluate the effectiveness of CA-INR in reducing reconstruction errors while maintaining high compression rates.
  • To assess the impact of incorporating contextual physical variables on the performance of climate data compression.

Main Methods:

  • Proposed a Context-Aware Implicit Neural Representation (CA-INR) model utilizing a multi-layer perceptron (MLP) architecture.
  • Trained the CA-INR model to overfit the data, using spatiotemporal coordinates and auxiliary physical variables (context) as inputs.
  • Evaluated CA-INR performance on surface temperature data from the Energy Exascale Earth System Model (E3SM), testing various contextual inputs like topography and climatological temperature.

Main Results:

  • Incorporating contextual information significantly reduced reconstruction errors in climate data compression.
  • CA-INR models achieved high compression rates comparable to standard INRs.
  • The inclusion of context, particularly topography and mean climatological temperature, enhanced data reconstruction quality.

Conclusions:

  • CA-INR offers a superior approach to lossy compression for climate data compared to standard INRs.
  • The method effectively minimizes reconstruction errors, making compressed data suitable for detailed climate analysis.
  • Contextual information integration is key to improving the accuracy and utility of compressed Earth system model data.