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The Kalman Filter and High Performance Computing at NASA's Data Assimilation Office (DAO)Atmospheric data assimilation is a method of combining actual observations with model simulations to produce a more accurate description of the earth system than the observations alone provide. The output of data assimilation, sometimes called "the analysis", are accurate regular, gridded datasets of observed and unobserved variables. This is used not only for weather forecasting but is becoming increasingly important for climate research. For example, these datasets may be used to assess retrospectively energy budgets or the effects of trace gases such as ozone. This allows researchers to understand processes driving weather and climate, which have important scientific and policy implications. The primary goal of the NASA's Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. This presentation will: (1) describe ongoing work on the advanced Kalman/Lagrangian filter parallel algorithm for the assimilation of trace gases in the stratosphere; and (2) discuss the Kalman filter in relation to other presentations from the DAO on Four Dimensional Data Assimilation at this meeting. Although the designation "Kalman filter" is often used to describe the overarching work, the series of talks will show that the scientific software and the kind of parallelization techniques that are being developed at the DAO are very different depending on the type of problem being considered, the extent to which the problem is mission critical, and the degree of Software Engineering that has to be applied.
Document ID
19990019836
Document Type
Conference Paper
Authors
Lyster, Peter M. (NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
August 19, 2013
Publication Date
January 1, 1999
Publication Information
Publication: HPCCP/CAS Workshop Proceedings 1998
Subject Category
Environment Pollution
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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IDRelationTitle19990019831Analytic PrimaryHPCCP/CAS Workshop Proceedings 1998