14 February 2018

Effective Online Monitoring and Saving Strategy for Large-Scale Climate Simulation

Online data monitoring strategy for earth system models.

Science

Large-scale climate models run for months to understand climate changes over the course of decades. This process creates volumes of data with both high temporal and spatial resolution information. This paper proposes an effective online data monitoring and saving strategy with the consideration of practical storage and memory capacity constraints. The method selects and records the most informative extreme values in the raw data generated from real-time simulations in the context of better monitoring climate changes.

Impact

Saving time and data storage allows more complex and lengthy simulations to be performed and analyzed by climate scientists. 

Summary

Large-scale climate simulation models have been developed and widely used to generate historical data and study future climate scenarios. These simulation models often have to run for a couple of months to understand the changes in the global climate over the course of decades. This long-duration simulation process creates a huge amount of data with both high temporal and spatial resolution information; however, how to effectively monitor and record the climate changes based on these large-scale simulation results that are continuously produced in real time still remains to be resolved. Due to the slow process of writing data to disk, the current practice is to save a snapshot of the simulation results at a constant, slow rate although the data generation process runs at a very high speed. This paper proposes an effective online data monitoring and saving strategy over the temporal and spatial domains with the consideration of practical storage and memory capacity constraints. Our proposed method is able to intelligently select and record the most informative extreme values in the raw data generated from real-time simulations in the context of better monitoring climate changes.

Contact
Benjamin Mayer
Oak Ridge National Laboratory (ORNL)
Publications
2018.  "Quality Technology & Quantitative Management."  Quality Technology & Quantitative Management 1-17.  https://doi.org/10.1080/16843703.2017.1414112.