A Framework for Automatic Validation and Application of Lossy Data Compression in Ensemble Data Assimilation

TL;DR

This paper introduces a framework for evaluating and applying lossy data compression in ensemble data assimilation, significantly reducing storage needs while maintaining data integrity in weather prediction models.

Contribution

It provides a practical, statistical approach for selecting safe compression parameters and demonstrates its effectiveness on a climate model with notable storage and speed improvements.

Findings

Achieved 1/3 reduction in storage space.

Realized 6% speedup per assimilation cycle.

Maintained data integrity during compression.

Abstract

Ensemble data assimilation techniques form an indispensable part of numerical weather prediction. As the ensemble size grows and model resolution increases, the amount of required storage becomes a major issue. Data compression schemes may come to the rescue not only for operational weather prediction, but also for weather history archives. In this paper, we present the design and implementation of an easy-to-use framework for evaluating the impact of lossy data compression in large scale ensemble data assimilation. The framework leverages robust statistical qualifiers to determine which compression parameters can be safely applied to the climate variables. Furthermore, our proposal can be used to apply the best parameters during operation, while monitoring data integrity. We perform an exemplary study on the Lorenz96 model to identify viable compression parameters and achieve a 1/3 saving in storage space and an effective speedup of 6% per assimilation cycle, while monitoring the state integrity.