**Monday**,
February 6, 2006, 4 p.m. PSA 113

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Dept Math & Stats

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Data Assimilation: Finding the Initial Conditions in Large Dynamical Systems

**Abstract**
One of the most significant problems in modeling a spatio-temporal
dynamical system, such as a weather forecast model, is finding the
initial conditions with which to start the simulation. Present-day
global forecast models incorporate on the order of 100 million dynamical
variables, and operational forecast centers collect on the order of
1 million measurements every six hours. Data assimilation refers to the
problem of incorporating the measurements into the dynamical models and
updating the initial conditions. This is the most expensive part of
numerical weather prediction. By the end of the decade, new satellite
observing systems will produce an order of magnitude more data than current
assimilation systems can handle.
In my talk, I will describe a new, model-independent approach
to the problem that promises to be much more accurate than current
schemes, can incorporate huge amounts of data, and is amenable to
efficient implementation on parallel computers. Applications to
models other than weather forecast models will be discussed, and
implementation strategies on parallel computers ranging from modest
Beowulf clusters to IBM's Blue Gene will be described.

For further information please contact:
Anne Gelb