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  • Author or Editor: A. V. Lavrinenko x
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V. S. Komarov
,
A. V. Lavrinenko
,
N. Ya. Lomakina
, and
S. N. Il’in

Abstract

A two-dimensional, dynamic-stochastic model presented in this study is used for short-term forecasting of vertical profiles of air temperature and wind velocity orthogonal components in the atmospheric boundary layer (ABL). The technique of using a two-dimensional dynamic-stochastic model involves preliminary estimation of its coefficients using the Kalman filter (KF) algorithm and observations at only one measuring station. The results obtained can be useful for aviation meteorology, mobile meteorological systems deployed in regions uncovered or rarely covered by meteorological observations, and devices with limited computational resources. In addition, they can be useful for wind-power and pollutant dispersion applications. Two cases of experiments with real observations using a radiometer and sodar (Doppler radar) deployed in the region of Tomsk, Russia, and data of more frequent (4 times a day) radiosonde observations in the region of Omsk (station 28698) are examined. The forecast period of numerical weather prediction (NWP) for all cases considered in this study ranged from 0.5 to 6 h. The results obtained demonstrate higher forecast quality in comparison with the persistence forecast.

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V. S. Komarov
,
A. V. Lavrinenko
,
A. V. Kreminskii
,
N. Ya Lomakina
,
Yu B. Popov
, and
A. I. Popova

Abstract

A new method and an algorithm of spatial extrapolation of mesometeorological fields to a territory uncovered with observations are suggested. The algorithm uses a linear Kalman filter for a four-dimensional dynamic–stochastic model of space–time variations of the atmospheric parameters. The results of statistical estimation of the quality of the algorithm used for spatial extrapolation of mesoscale temperature and wind velocity fields are discussed.

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