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

Ding, H.Q.; Chan, C.; Gennery, D.B.; Ferraro, R.D.

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