Bridging Research to Operations Transitions: Status and Plans of Community GSI (original) (raw)
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THE WEATHER RESEARCH AND FORECAST MODEL: SOFTWARE ARCHITECTURE AND PERFORMANCE
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The challenges of building an effective grid-based problem solving environment that truly extends and embraces a computational scientist’s traditional tools are multifold. It is far too easy to build simple stovepipes that allow fixed use patterns, that don’t extend a scientist’s desktop, and fail to encompass the full range of patterns that a scientist needs to find such a problem-solving environment as a liberating and enabling tool. In the LEAD project, we have focused on the most challenging users of numerical weather prediction, namely, the atmospheric science researchers, who are prone to use their own tools, their own modified versions of community codes such as the Weather Research and Forecasting (WRF) model, and are typically comfortable with elaborate shell scripts to perform the work they find to be necessary to succeed, to drive our development efforts. Our response to these challenges includes a multi-level workflow engine, to handle both the challenges of ensemble description and execution, as well as the detailed patterns of workflow on each computational resource; services to support the peculiarities of each platform being used to do the modeling (such as on TeraGrid), and the use of an RDF triple store and message bus together as the backbone of our notification, logging, and metadata infrastructure. The design of our problem-solving environment elements attempts to come to grips with lack of control of elements surrounding and supporting the environment; we achieve this through multiple mechanisms including using the OSGI plug-in architecture, as well as the use of RDF triples as our finest-grain descriptive element. This combination, we believe, is an important stepping stone to building a cyber environment, which aims to provide flexibility and ease of use far beyond the current range of typical problem solving environments.
Quarterly Journal of the Royal Meteorological Society, 2018
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Atmospheric science is advancing towards very complex phenomena at ever smaller temporal and spatial scales. One of the principal tools utilized in atmospheric science are weather prediction models. These models usually demand large execution times and resource allocation, such as CPU time and storage space. The main goal of our research is porting of the Weather Research and Forecasting model to the Grid infrastructure. Porting has been done through bash scripts that are using existing Grid tools for job and data management, authentification mechanisms, and other application level services produced within the SEE-GRID project. In this paper, through a few model runs on the Grid we describe certain benefits not only in the overall execution time but also in the ability of performing concurrent runs of the same model especially for scientific purposes. During the execution, we have also faced some drawbacks in data bandwidth, unreliability of some Grid services and relatively hard control of the model execution flow. The final conclusion is that there is a big need and justification for porting the WRF model to the Grid, although it takes a lot of effort to be properly implemented.
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The Emergence of Weather-Related Testbeds Linking Research and Forecasting Operations
Bulletin of the American Meteorological Society, 2013
Test beds have become an integral part of the weather enterprise, bridging research and forecast services by transitioning innovative tools and tested methods that impact forecasts and forecast users. sMith, anD steven weiss august 2013 aMERICaN MEtEOROLOgICaL sOCIEtY | AFFILIATIONS: ralph anD intrieri-noaa/earth system research laboratory, Boulder, colorado; anDra-noaa/national Weather service, norman, oklahoma; atlas anD Murillo-noaa/atlantic oceanographic and meteorological laboratory, miami, florida; boukabara anD riishoJgaarD-Joint center for satellite data assimilation, camp springs, maryland; bright, entwistle, harless, anD levit-noaa/national Weather service, national centers for environmental prediction, kansas city, missouri; DaviDson-office of science and Technology, noaa/national Weather service, silver spring, maryland; gaynor-noaa/office of policy, planning, and evaluation, silver spring, maryland; gooDMan-noaa/national environmental satellite, data, and information service, and nasa goddard space flight center, greenbelt, maryland; Jiing-noaa/ national hurricane center, miami, florida; huang-climate prediction center, noaa/national Weather service, camp springs, maryland; JeDlovec-nasa marshall space flight center, huntsville, alabama; kain, koch, anD sMith-noaa/national severe storms laboratory, norman, oklahoma, and cooperative institute for mesoscale meteorological studies, University of oklahoma, norman, oklahoma; kuo-national center for atmospheric research, Boulder, colorado; t. schneiDer-office of hydrologic development, noaa/national Weather service, Boulder, colorado; r. schneiDer anD weiss-storm prediction center, noaa/national Weather service, norman, oklahoma CORRESPONDING AUTHOR: marty ralph, noaa/earth system research laboratory, r/e/psd,