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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. [ABSTRACT FROM AUTHOR]

Surface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona-New Mexico region. RCMs generally overestimate surface insolation, especially in the eastern United States. Negative correlation between the biases in insolation and precipitation suggest that these two fields are related, likely via clouds. Systematic variations in biases for regions, seasons, variables, and metrics suggest that the bias correction in applying climate model data to assess the climate impact on various sectors must be performed accordingly. Precipitation evaluation with multiple observations reveals that observational data can be an important source of uncertainties in model evaluation; thus, cross examination of observational data is important for model evaluation. [ABSTRACT FROM AUTHOR]