Spatial interpolation of meteorological fields using a multilevel parametric dynamic stochastic low-order model; Journal of Atmospheric and Solar-Terrestrial Physics; Vol. 181, Pt. A

Spatial interpolation of meteorological fields using a multilevel parametric dynamic stochastic low-order model; Journal of Atmospheric and Solar-Terrestrial Physics; Vol. 181, Pt. A

Bibliographische Detailangaben
Parent link:Journal of Atmospheric and Solar-Terrestrial Physics
Vol. 181, Pt. A.— 2018.— [P. 38-43]
Körperschaften: Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение математики и информатики, Национальный исследовательский Томский политехнический университет
Weitere Verfasser: Lavrinenko A. V. Andrey Viktorovich, Moldovanova E. A. Evgeniya Aleksandrovna, Mymrina D. F. Dina Fedorovna, Popova A. I. Avgustina Ivanovna, Popova K. Yu. Kseniya Yurjevna, Popov Yu. B. Yury Borisovich
Zusammenfassung:Title screen
The paper focuses on a new method of spatial interpolation of air temperature and wind velocity fields in the troposphere. The method is based on Kalman filtering and a multilevel parametric dynamic stochastic low-order model. The key feature of the proposed model is that it has parameters, which are responsible for the altitude levels. Generally, models use so-called “shallow water” (shallow water approximation), and altitude correlation is not taken into account, or they may rely only on mandatory isobaric levels data, thus ignoring the data obtained for significant levels. Standard levels are located at considerable distances in altitude from each other and the altitude correlation there is not usually significant. By using parameters that are responsible for the altitude levels, this model allows us to estimate the effect that information coming from neighbouring altitude levels may have on the final estimate. The paper presents the results of a statistical estimation of the proposed spatial interpolation algorithm. A comparison of the results statistical estimation spatial interpolation of the proposed algorithm with a four-dimensional dynamic-stochastic model is given.
Режим доступа: по договору с организацией-держателем ресурса
Sprache:Englisch
Veröffentlicht: 2018
Schlagworte:
электронный ресурс
труды учёных ТПУ
Kalman filter
spatial interpolation
data assimilation
numerical modelling
low-order parametric dynamic stochastic model
фильтр Калмана
интерполяция
ассимиляция
численное моделирование
параметрические модели
стохастические модели
Online-Zugang:https://doi.org/10.1016/j.jastp.2018.10.009
Format: Elektronisch Buchkapitel
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=659529

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