Parametric Kalman filter for chemical transport models

被引：15

作者：

Pannekoucke, Olivier ^{[1
,2
]}

Ricci, Sophie ^{[2
]}

Barthelemy, Sebastien ^{[2
]}

Menard, Richard ^{[3
]}

Thual, Olivier ^{[2
,4
]}

机构：

[1] CNRS, Meteo France, CNRM UMR 3589, INPT ENM, Toulouse, France

[2] CERFACS, UMR5318, CECI, Toulouse, France

[3] Environm & Climate Change Canada, ARQI Air Qual Res Div, Dorval, PQ, Canada

[4] Univ Toulouse, IMFT, CNRS, INPT, Toulouse, France

来源：

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY | 2016年 / 68卷

关键词：

data assimilation; Kalman filter; covariance dynamics; parameterisation of analysis; SATELLITE-OBSERVATIONS; DATA ASSIMILATION; ENSEMBLE; LOCALIZATION; DYNAMICS;

D O I：

10.3402/tellusa.v68.31547

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

A computational simplification of the Kalman filter (KF) is introduced - the parametric Kalman filter (PKF). The full covariance matrix dynamics of the KF, which describes the evolution along the analysis and forecast cycle, is replaced by the dynamics of the error variance and the diffusion tensor, which is related to the correlation length-scales. The PKF developed here has been applied to the simplified framework of advection-diffusion of a passive tracer, for its use in chemical transport model assimilation. The PKF is easy to compute and computationally cost-effective than an ensemble Kalman filter (EnKF) in this context. The validation of the method is presented for a simplified 1-D advection-diffusion dynamics.

引用

页数：14

共 50 条

[41] Using the Kalman filter for parameter estimation in biogeochemical models
Trudinger, C. M.
Raupach, M. R.
Rayner, P. J.
Enting, I. G.
ENVIRONMETRICS, 2008, 19 (08) : 849 - 870
[42] Robust Kalman filter for rank deficient observation models
Koch, KR
Yang, Y
JOURNAL OF GEODESY, 1998, 72 (7-8) : 436 - 441
[43] Conditional forecasts on SVAR models using the Kalman filter
Camba-Mendez, Gonzalo
ECONOMICS LETTERS, 2012, 115 (03) : 376 - 378
[44] Low-Rank Kalman Filtering for Efficient State Estimation of Subsurface Advective Contaminant Transport Models
El Gharamti, M.
Hoteit, I.
Sun, S.
JOURNAL OF ENVIRONMENTAL ENGINEERING, 2012, 138 (04) : 446 - 457
[45] Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model
Tsyrulnikov, Michael
Sotskiy, Arseniy
SPATIAL STATISTICS, 2024, 64
[46] 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
[47] Optimizing emission inventory for chemical transport models by using genetic algorithm
Li, M. J.
Chen, D. S.
Cheng, S. Y.
Wang, F.
Li, Y.
Zhou, Y.
Lang, J. L.
ATMOSPHERIC ENVIRONMENT, 2010, 44 (32) : 3926 - 3934
[48] The High-Rank Ensemble Transform Kalman Filter
Huang, Bo
Wang, Xuguang
Bishop, Craig H.
MONTHLY WEATHER REVIEW, 2019, 147 (08) : 3025 - 3043
[49] The Potential of the Ensemble Kalman Filter to Improve Glacier Modeling
Knudsen L.
Park-Kaufmann H.
Corcoran E.
Robel A.
Mayo T.
La Matematica, 2024, 3 (3): : 1085 - 1102
[50] A consistent interpretation of the stochastic version of the Ensemble Kalman Filter
van Leeuwen, Peter Jan
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2020, 146 (731) : 2815 - 2825

← 1 2 3 4 5 →