Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models

被引：0

作者：

Penenko, A., V ^{[1
,2
]}

Konopleva, V. S. ^{[1
,2
]}

Golenko, P. M. ^{[1
,2
]}

Penenko, V. V. ^{[1
,2
]}

机构：

[1] ICM&MG SB RAS, Inst Computat Math & Math Geophys, Prospekt Akad Lavrentjeva 6, Novosibirsk 630090, Russia

[2] Novosibirsk State Univ, Pirogova Str 2, Novosibirsk 630090, Russia

来源：

27TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS, ATMOSPHERIC PHYSICS | 2021年 / 11916卷

基金：

俄罗斯基础研究基金会;

关键词：

data assimilation; atmospheric chemistry; uncertainty function; continuation problem; reaction rates; variational approach; differential evolution;

D O I：

10.1117/12.2603422

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

The development of efficient data assimilation algorithms for atmospheric chemistry models is an important part of modern air quality studies. In the data assimilation framework considered, the identification of the chosen model parameters is used to continue the model state function to the unobservable part of the domain. This continuation problem is solved sequentially on the set of time intervals called the data assimilation windows. The framework is illustrated on a low-dimensional atmospheric chemistry model.

引用

页数：7

共 50 条

[1] Data assimilation for atmospheric chemistry models
vanLoon, M
Heemink, AW
Builtjes, PJH
MEASUREMENTS AND MODELLING IN ENVIRONMENTAL POLLUTION, 1997, : 293 - 300
[2] Numerical study of variational data assimilation algorithms based on decomposition methods in atmospheric chemistry models
Penenko, Alexey
Antokhin, Pavel
INTERNATIONAL CONFERENCE AND EARLY CAREER SCIENTISTS SCHOOL ON ENVIRONMENTAL OBSERVATIONS, MODELLING AND INFORMATION SYSTEMS (ENVIROMIS-2016), 2016, 48
[3] Mathematical aspects of data assimilation for atmospheric chemistry models
Dimitriu, G
Cuciureanu, R
ADVANCES IN AIR POLLUTION MODELING FOR ENVIRONMENTAL SECURITY, 2005, 54 : 93 - 104
[4] Large scale data assimilation based on RRSQRT-filters; application on atmospheric chemistry models
Segers, AJ
Heemink, AW
Verlaan, M
van Loon, M
AIR POLLUTION VI, 1998, 6 : 25 - 34
[5] Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models
Bocquet, M.
Elbern, H.
Eskes, H.
Hirtl, M.
Zabkar, R.
Carmichael, G. R.
Flemming, J.
Inness, A.
Pagowski, M.
Perez Camano, J. L.
Saide, P. E.
San Jose, R.
Sofiev, M.
Vira, J.
Baklanov, A.
Carnevale, C.
Grell, G.
Seigneur, C.
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2015, 15 (10) : 5325 - 5358
[6] Sequential Variational Data Assimilation Algorithms at the Splitting Stages of a Numerical Atmospheric Chemistry Model
Penenko, Alexey
Penenko, Vladimir
Tsvetova, Elena
Grishina, Anastasia
Antokhin, Pavel
LARGE-SCALE SCIENTIFIC COMPUTING, LSSC 2017, 2018, 10665 : 536 - 543
[7] Data assimilation for models with parametric uncertainty
Yang, Lun
Qin, Yi
Narayan, Akil
Wang, Peng
JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 396 (785-798) : 785 - 798
[8] Data assimilation: a powerful tool for atmospheric chemistry
Lary, DJ
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1999, 357 (1763): : 3445 - 3457
[9] Data assimilation for chemistry transport models
Elbern, H
Tilmes, S
Schmidt, H
PROCEEDINGS OF EUROTRAC SYMPOSIUM '96 - TRANSPORT AND TRANSFORMATION OF POLLUTANTS IN THE TROPOSPHERE, VOL 1: CLOUDS, AEROSOLS, MODELLING AND PHOTO-OXIDANTS, 1997, : 681 - 685
[10] Ensemble-based data assimilation for atmospheric chemical transport models
Sandu, A
Constantinescu, EM
Liao, WY
Carmichael, GR
Chai, TF
Seinfeld, JH
Daescu, D
COMPUTATIONAL SCIENCE - ICCS 2005, PT 2, 2005, 3515 : 648 - 655

← 1 2 3 4 5 →