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

Bibliografiska uppgifter
Parent link:Journal of Atmospheric and Solar-Terrestrial Physics
Vol. 181, Pt. A.— 2018.— [P. 38-43]
Institutionella upphovsmän: Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение математики и информатики, Томский политехнический университет
Övriga upphovsmän: 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
Sammanfattning: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
Språk:engelska
Publicerad: 2018
Ämnen:
Länkar:https://doi.org/10.1016/j.jastp.2018.10.009
Materialtyp: Elektronisk Bokavsnitt
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=659529

MARC

LEADER 00000naa0a2200000 4500
001 659529
005 20260715071722.0
035 |a (RuTPU)RU\TPU\network\28134 
035 |a RU\TPU\network\15846 
090 |a 659529 
100 |a 20190226d2018 k||y0rusy50 ba 
101 0 |a eng 
102 |a NL 
135 |a drcn ---uucaa 
181 0 |a i  
182 0 |a b 
200 1 |a Spatial interpolation of meteorological fields using a multilevel parametric dynamic stochastic low-order model  |f A. V. Lavrinenko [et al.] 
203 |a Text  |c electronic 
300 |a Title screen 
330 |a 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. 
333 |a Режим доступа: по договору с организацией-держателем ресурса 
461 |t Journal of Atmospheric and Solar-Terrestrial Physics 
463 |t Vol. 181, Pt. A  |v [P. 38-43]  |d 2018 
610 1 |a электронный ресурс 
610 1 |a труды учёных ТПУ 
610 1 |a Kalman filter 
610 1 |a spatial interpolation 
610 1 |a data assimilation 
610 1 |a numerical modelling 
610 1 |a low-order parametric dynamic stochastic model 
610 1 |a фильтр Калмана 
610 1 |a интерполяция 
610 1 |a ассимиляция 
610 1 |a численное моделирование 
610 1 |a параметрические модели 
610 1 |a стохастические модели 
701 1 |a Lavrinenko  |b A. V.  |g Andrey Viktorovich 
701 1 |a Moldovanova  |b E. A.  |c mathematician  |c Senior Lecturer of Tomsk Polytechnic University  |f 1968-  |g Evgeniya Aleksandrovna  |3 (RuTPU)RU\TPU\pers\33417  |9 17111 
701 1 |a Mymrina  |b D. F.  |c linguist  |c Associate Professor of Tomsk Polytechnic University, candidate of philological sciences  |f 1979-  |g Dina Fedorovna  |3 (RuTPU)RU\TPU\pers\34816 
701 1 |a Popova  |b A. I.  |g Avgustina Ivanovna 
701 1 |a Popova  |b K. Yu.  |g Kseniya Yurjevna 
701 1 |a Popov  |b Yu. B.  |g Yury Borisovich 
712 0 2 |a Национальный исследовательский Томский политехнический университет  |b Школа базовой инженерной подготовки  |b Отделение математики и информатики  |3 (RuTPU)RU\TPU\col\23555 
712 0 2 |a Томский политехнический университет  |c 1991-  |7 ca  |8 rus  |9 26305 
801 2 |a RU  |b 63413507  |c 20190226  |g RCR 
856 4 |u https://doi.org/10.1016/j.jastp.2018.10.009 
942 |c CF