A prototype upper-atmospheric data assimilation scheme based on optimal interpolation: 2. Numerical experiments

Details A prototype upper-atmospheric data assimilation scheme based on optimal interpolation: 2. Numerical experiments A prototype upper-atmospheric data assimilation scheme based on optimal interpolation: 2. Numerical experiments

Apr 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999jastp..61..505a&link_type=abstract

Journal of Atmospheric and Solar-Terrestrial Physics, Volume 61, Issue 6, p. 505-517.

Physics

1

Scientific paper

In Part 1 of this work ([Akmaev, 1999]), an overview of the theory of optimal interpolation (OI) ([Gandin, 1963]) and related techniques of data assimilation based on linear optimal estimation ([Liebelt, 1967]; [Catlin, 1989]; [Mendel, 1995]) is presented. The approach implies the use in data analysis of additional statistical information in the form of statistical moments, e.g., the mean and covariance (correlation). The a priori statistical characteristics, if available, make it possible to constrain expected errors and obtain optimal in some sense estimates of the true state from a set of observations in a given domain in space and/or time. The primary objective of OI is to provide estimates away from the observations, i.e., to fill in data voids in the domain under consideration. Additionally, OI performs smoothing suppressing the noise, i.e., the spectral components that are presumably not present in the true signal. Usually, the criterion of optimality is minimum variance of the expected errors and the whole approach may be considered constrained least squares or least squares with a priori information. Obviously, data assimilation techniques capable of incorporating any additional information are potentially superior to techniques that have no access to such information as, for example, the conventional least squares (e.g., [Liebelt, 1967]; [Weisberg, 1985]; [Press et al., 1992]; [Mendel, 1995]).

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