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.

We describe a reusable architecture and implementation framework for managing science processing pipelines for mission ground data systems. Our system, dubbed "PCS", for Process Control System, improves upon an existing software component, the OODT Catalog and Archive (CAS), which has already supported the QuikSCAT, SeaWinds and AMT earth science missions. This paper focuses on PCS within the context of two current earth science missions: the Orbiting Carbon Observatory (OCO), and NPP Sounder PEATE projects.

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.

Knowledge discovery and data correlation require a unified approach to basic data management. However, achieving such an approach is nearly impossible with hundreds of disparate data sources. legacy systems and data formats. This problem is pervasive in the space science community where data models, taxonomies and data management systems are locally implemented and limited metadata has been collected and organized. Technology developed by the Object Oriented Data Technology (OODT) task at NASA's Jet Propulsion Laboratory (JF'L) has been exploring component frameworks for managing, locating and exchanging data residing within a geographically distributed nehvork. OODT has taken a novel approach towards solving this problem by exploiting web technologies usually dedicated to e-commerce, combined with a rich, metadata-based environment. The components developed by OODT Create a set of distributed peer-to-peer services that allow for data managed by a peer to he searched and returned as part of an integrated data management system. This paper discusses the approach taken to develop a software framework, and two prototype development efforts for the Planetary Data System (PDS) and the Mission and Ground Asset Database.'

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