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Cloud computing and virtualization within the regional climate model and evaluation system

Research paper by Chris A. Mattmann, Duane Waliser, Jinwon Kim, Cameron Goodale, Andrew Hart, Paul Ramirez, Dan Crichton, Paul Zimdars, Maziyar Boustani, Kyo Lee, Paul Loikith, Kim Whitehall, Chris Jack, Bruce Hewitson

Indexed on: 13 Jul '13Published on: 13 Jul '13Published in: Earth Science Informatics



Abstract

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an “offline”, lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution’s computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users.