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Parallel Climate Data Assimilation PSAS Package Achieves 18 GFLOPs on 512-Node Intel Paragon

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dc.contributor.author Ding, H.Q. en_US
dc.contributor.author Chan, C. en_US
dc.contributor.author Gennery, D.B. en_US
dc.contributor.author Ferraro, R.D. en_US
dc.date.accessioned 2004-10-04T20:43:34Z
dc.date.available 2004-10-04T20:43:34Z
dc.date.issued 1995 en_US
dc.identifier.citation California Inst. Concurrent Supercomputing Consortium Annual Report en_US
dc.identifier.clearanceno 95-1458 en_US
dc.identifier.uri http://hdl.handle.net/2014/32085
dc.description.abstract Several algorithms were added to the Physical-space Statistical Analysis System (PSAS) from Goddard, which assimilates observational weather data by correcting for different levels of uncertainty about the data and different locations for mobile observation platforms. The new algorithms and use of the 512-node Intel Paragon allowed a hundred-fold decrease in processing time. en_US
dc.format.extent 169422 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject.other weather prediction climate modeling data assimilation Paragon supercomputing Kalman filter solver en_US
dc.title Parallel Climate Data Assimilation PSAS Package Achieves 18 GFLOPs on 512-Node Intel Paragon en_US


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