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Host: Institute for Mathematics and its Applications
Homepage: http://www.ima.umn.edu/geoscience/spring/g10.html
Email: staff@ima.umn.edu
Organizers: R. N. Miller, O. Talagrand, P. Malnotte-Rizzoli, J. Derber
Description:
Data assimilation designates the use of a dynamical model, in combination with sparse and
inaccurate, data, irregularly distributed in space and time, to infer the evolving state of the
system being modeled. Systematic estimates of errors in the model and the observed data are
essential features of any data assimilation system. Posterior error estimates are also necessary
for evaluation of the system. Construction of these error estimates is the most challenging
and scientifically important task. Two complementary approaches to data assimilation are
currently in use in the atmospheric and oceanic sciences, based respectively on sequential
estimation and optimal control theory. The theoretical, numerical, and physical aspects of
applying estimation and control methods to atmospheric and oceanic models will be covered,
emphasizing issues of observing system design and parameter estimation.
Keywords: stochastic ODEs, PDEs, control, atmosphere and oceans
Date received: January 31, 2001
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