A cloud-enabled regional climate model evaluation system

  • Authors:

  • Andrew F. Hart;Cameron E. Goodale;Chris A. Mattmann;Paul Zimdars;Dan Crichton;Peter Lean;Jinwon Kim;Duane Waliser

  • Affiliations:

  • California Institute of Technology, Pasadena, CA, USA;California Institute of Technology, Pasadena, CA, USA;California Institute of Technology & University of Southern California, Pasadena & Los Angeles, CA, USA;California Institute of Technology, Pasadena, CA, USA;California Institute of Technology, Pasadena, CA, USA;California Institute of Technology & University of California at Los Angeles, Pasadena & Los Angeles, CA, USA;University of California at Los Angeles, Los Angeles, CA, USA;California Institute of Technology & University of California at Los Angeles, Pasadena & Los Angeles, CA, USA

  • Venue:
  • Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing
  • Year:
  • 2011

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Abstract

The climate research community is increasingly interested in utilizing direct, observational measurements to validate model output in an effort to tune those models to better approximate our planet's dynamic climate. The current emphasis on performing these comparisons at regional, as opposed to global, scales presents challenges both scientific and technical, since regional ecosystems are highly heterogeneous and the available data is not readily consumed on a regional basis. If provided with a common approach for efficiently accessing and utilizing the existing observational datasets, climate researchers have the potential to effect lasting societal, economic and political benefits. A key challenge, however, is that model-to-observational comparison requires massive quantities of data and significant computational capabilities. Further complicating matters is the fact that, currently, observational data and model outputs exist in a variety of data formats, utilize varying degrees of specificity and resolution, and reside in disparate, highly heterogeneous data systems. In this paper we present a software architectural approach that leverages the advantages of cloud computing and modern open-source software technologies to address the regional climate modeling problem. Our system, dubbed RCMES, is highly scalable and elastic, allows for both local and distributed management of the satellite observations and generated model outputs, and delivers this information to climate researchers in a way that is easily integrated into existing climate simulations and statistical tools.