Paper
28 March 2003 Dynamic-stochastic spatial forecast of mesoscale temperature and wind fields as applied to estimation of technogenic pollution distribution
Yu. B. Popov, V. S. Komarov, S. N. Ilyin, A. V. Kreminskii, V. A. Kurakov, V. V. Kuryshev, N. Ya. Lomakina, A. I. Popova, S. S. Suvorov
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Abstract
Original methodology and algorithms of spatial extrapolation of mesometeorological fields to the territory uncovered with observational data using the extended Kalman filter filter algorithm and the generalized dynamic-stochastic model of the spatiotemporal behavior of the parameters described by the first-order stochastic differential equations are considered. The results of statistical estimation of the quality of the suggested algorithms used for spatial prediction of the temperature and wind velocity fields on the mesoscale level are discussed.
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Yu. B. Popov, V. S. Komarov, S. N. Ilyin, A. V. Kreminskii, V. A. Kurakov, V. V. Kuryshev, N. Ya. Lomakina, A. I. Popova, and S. S. Suvorov "Dynamic-stochastic spatial forecast of mesoscale temperature and wind fields as applied to estimation of technogenic pollution distribution", Proc. SPIE 5027, Ninth Joint International Symposium on Atmospheric and Ocean Optics/Atmospheric Physics. Part II: Laser Sensing and Atmospheric Physics, (28 March 2003); https://doi.org/10.1117/12.497290
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KEYWORDS
Filtering (signal processing)

Data modeling

Atmospheric modeling

Mathematical modeling

Differential equations

Stochastic processes

Error analysis

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