A prototype upper-atmospheric data assimilation scheme based on optimal interpolation: 2. Numerical experiments |
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| Institution: | 1. Department of Aerospace Engineering Sciences, Campus Box 429, University of Colorado, Boulder, CO 80309–0429, USA;1. ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Via Martiri di Monte Sole, 4, Bologna, 40129, Italy;2. Università Milano Bicocca, Piazza Della Scienza 1, 20126, Milano, Italy;1. ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Via Martiri di Monte Sole 4, 40129, Bologna, Italy;2. INERIS, National Institute for Industrial Environment and Risks, Parc Technologique ALATA, 60550, Verneuil-en-Halatte, France;3. RSE S.p.A., via Rubattino 54, 20134, Milan, Italy;4. PSI, LAC, Paul Scherrer Institute, 5232, Villigen, PSI, Switzerland;5. Climate Modelling and Air Pollution Division, Research and Development Department, Norwegian Meteorological Institute (MET Norway), P.O. Box 43, Blindern, N-0313, Oslo, Norway;6. TNO, Dept. Climate, Air and Sustainability, P.O. Box 80015, 3508, TA, Utrecht, the Netherlands;7. HZG, Helmholtz-Zentrum Geesthacht, Institute for Coastal Research, Max-Planck-Straße 1, 21502, Geesthacht, Germany;8. Freie Universität Berlin, Institut für Meteorologie Troposphärische Umweltforschung, Carl-Heinrich-Becker Weg 6–10, 12165, Berlin, Germany;9. CIEMAT, Atmospheric Pollution Unit, Avda. Complutense, 22, 28040, Madrid, Spain;10. ex-European Commission, Joint Research Centre (JRC), 21020, Ispra, Va, Italy;11. Norwegian Institute for Air Research (NILU), Box 100, 2027, Kjeller, Norway;12. ARIANET Srl, Via Gilino n.9, 20128, Milan, Italy;13. Leibniz-Institut für Troposphärenforschung (TROPOS), Permoserstraße 15, 04318, Leipzig, Germany;14. Dept. of Chemistry and CIRES, University of Colorado, Boulder, CO, USA;15. Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 1627, FIN-70211, Kuopio, Finland;p. Air Quality Research, Finnish Meteorological Institute, Erik Palménin aukio 1, 00560, Helsinki, Finland |
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| Abstract: | 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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