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Workflows for eScience: Scientific Workflows for Grids
глава Dynamic, Adaptive Workflows for Mesoscale Meteorology, стр. 126--142. Springer London, (2007)
DOI: 10.1007/978-1-84628-757-2_9
Аннотация
The Linked Environments for Atmospheric Discovery (LEAD) 122 is a National Science Foundation funded1 project to change the paradigm for mesoscale weather prediction from one of static, fixed-schedule computational forecasts to one that is adaptive and driven by weather events. It is a collaboration of eight institutions,2 led by Kelvin Droegemeier of the University of Oklahoma, with the goal of enabling far more accurate and timely predictions of tornadoes and hurricanes than previously considered possible. The traditional approach to weather prediction is a four-phase activity. In the first phase, data from sensors are collected. The sensors include ground instruments such as humidity and temperature detectors, and lightning strike detectors and atmospheric measurements taken from balloons, commercial aircraft, radars, and satellites. The second phase is data assimilation, in which the gathered data are merged together into a set of consistent initial and boundary conditions for a large simulation. The third phase is the weather prediction, which applies numerical equations to measured conditions in order to project future weather conditions. The final phase is the generation of visual images of the processed data products that are analyzed to make predictions. Each phase of activity is performed by one or more application components.
