Computationally efficient methods for large-scale atmospheric inverse modeling

Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for m...

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Veröffentlicht in:Geoscientific Model Development Jg. 15; H. 14; S. 5547 - 5565
Hauptverfasser: Cho, Taewon, Chung, Julianne, Miller, Scot M., Saibaba, Arvind K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Katlenburg-Lindau Copernicus GmbH 20.07.2022
Copernicus Publications
Schlagworte:
Air pollution
Algorithms
Atmospheric models
Carbon
Computational efficiency
Computer applications
Covariance matrix
Earth surface
Emissions
Exploitation
Fluxes
Gases
Greenhouse gases
Inverse problems
Iterative algorithms
Iterative methods
Mathematical models
Methods
Modelling
Observatories
Random variables
Regularization
Robustness (mathematics)
Satellite observation
Satellites
Surface fluxes
ISSN:1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X
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Abstract Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges for standard inverse modeling frameworks that were often originally designed with much smaller data sets in mind. In this article, we discuss computationally efficient methods for large-scale atmospheric inverse modeling and focus on addressing some of the main computational and practical challenges. We develop generalized hybrid projection methods, which are iterative methods for solving large-scale inverse problems, and specifically we focus on the case of estimating surface fluxes. These algorithms confer several advantages. They are efficient, in part because they converge quickly, they exploit efficient matrix–vector multiplications, and they do not require inversion of any matrices. These methods are also robust because they can accurately reconstruct surface fluxes, they are automatic since regularization or covariance matrix parameters and stopping criteria can be determined as part of the iterative algorithm, and they are flexible because they can be paired with many different types of atmospheric models. We demonstrate the benefits of generalized hybrid methods with a case study from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. We then address the more challenging problem of solving the inverse model when the mean of the surface fluxes is not known a priori; we do so by reformulating the problem, thereby extending the applicability of hybrid projection methods to include hierarchical priors. We further show that by exploiting mathematical relations provided by the generalized hybrid method, we can efficiently calculate an approximate posterior variance, thereby providing uncertainty information.
AbstractList Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges for standard inverse modeling frameworks that were often originally designed with much smaller data sets in mind. In this article, we discuss computationally efficient methods for large-scale atmospheric inverse modeling and focus on addressing some of the main computational and practical challenges. We develop generalized hybrid projection methods, which are iterative methods for solving large-scale inverse problems, and specifically we focus on the case of estimating surface fluxes. These algorithms confer several advantages. They are efficient, in part because they converge quickly, they exploit efficient matrix–vector multiplications, and they do not require inversion of any matrices. These methods are also robust because they can accurately reconstruct surface fluxes, they are automatic since regularization or covariance matrix parameters and stopping criteria can be determined as part of the iterative algorithm, and they are flexible because they can be paired with many different types of atmospheric models. We demonstrate the benefits of generalized hybrid methods with a case study from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. We then address the more challenging problem of solving the inverse model when the mean of the surface fluxes is not known a priori; we do so by reformulating the problem, thereby extending the applicability of hybrid projection methods to include hierarchical priors. We further show that by exploiting mathematical relations provided by the generalized hybrid method, we can efficiently calculate an approximate posterior variance, thereby providing uncertainty information.
Audience Academic
Author Cho, Taewon
Miller, Scot M.
Saibaba, Arvind K.
Chung, Julianne
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  surname: Miller
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  givenname: Arvind K.
  surname: Saibaba
  fullname: Saibaba, Arvind K.
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Cites_doi 10.1088/1361-6420/aaa0e1
10.5194/acp-19-9797-2019
10.5194/gmd-2021-181
10.1007/978-0-387-69277-7_8
10.1029/2002JD003161
10.5194/gmd-14-4683-2021
10.3402/tellusb.v67.28452
10.1117/12.2187291
10.5194/acp-14-3855-2014
10.1016/j.advwatres.2015.04.012
10.5194/acp-13-11643-2013
10.5194/acp-20-323-2020
10.1137/1.9780898718836
10.1029/2012JD018176
10.1002/gamm.202000017
10.1126/sciadv.aaw0076
10.1029/2005JD005970
10.5194/gmd-7-303-2014
10.1007/978-3-0348-5460-3_7
10.1137/1.9780898717921
10.1007/s00211-013-0518-8
10.1002/qj.3209
10.1126/science.aam5745
10.5194/amt-11-6539-2018
10.1142/9789812813718
10.5194/acp-7-2413-2007
10.5194/acp-8-6341-2008
10.5194/gmd-10-3695-2017
10.1016/j.atmosenv.2018.05.044
10.5194/acp-21-6663-2021
10.1175/2008JTECHA1082.1
10.5194/acp-18-6785-2018
10.1029/2003JD004422
10.1002/nla.2325
10.1088/1748-9326/abfac1
10.5194/gmd-2021-393
10.1117/12.974954
10.1111/j.1600-0889.2006.00218.x
10.1525/elementa.188
10.5194/bg-9-457-2012
10.1137/1.9781611971484
10.5194/gmd-13-1771-2020
10.1017/S0962492918000016
10.1137/15M1037925
10.1017/CBO9780511535741
10.7551/mitpress/3206.001.0001
10.5194/acp-22-1097-2022
10.1007/s00703-010-0068-x
10.5194/gmd-6-583-2013
10.1137/16M1081968
10.5194/acp-16-13449-2016
10.1017/9781316544754
10.1029/2011WR011778
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References ref13
ref57
ref12
ref56
ref15
ref59
ref14
ref58
ref53
ref52
ref11
ref55
ref10
ref54
ref17
ref16
ref19
ref18
ref51
ref50
ref46
ref45
ref48
ref47
ref42
ref41
ref44
ref43
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
ref35
ref34
ref37
ref36
ref31
ref30
ref33
ref32
ref2
ref1
ref39
ref38
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
References_xml – ident: ref17
  doi: 10.1088/1361-6420/aaa0e1
– ident: ref19
  doi: 10.5194/acp-19-9797-2019
– ident: ref59
  doi: 10.5194/gmd-2021-181
– ident: ref55
  doi: 10.1007/978-0-387-69277-7_8
– ident: ref31
  doi: 10.1029/2002JD003161
– ident: ref32
  doi: 10.5194/gmd-14-4683-2021
– ident: ref48
  doi: 10.3402/tellusb.v67.28452
– ident: ref18
  doi: 10.1117/12.2187291
– ident: ref23
  doi: 10.5194/acp-14-3855-2014
– ident: ref53
  doi: 10.1016/j.advwatres.2015.04.012
– ident: ref9
  doi: 10.5194/acp-13-11643-2013
– ident: ref37
  doi: 10.5194/acp-20-323-2020
– ident: ref27
  doi: 10.1137/1.9780898718836
– ident: ref10
  doi: 10.1029/2012JD018176
– ident: ref2
– ident: ref24
  doi: 10.1002/gamm.202000017
– ident: ref30
  doi: 10.1126/sciadv.aaw0076
– ident: ref36
  doi: 10.1029/2005JD005970
– ident: ref38
  doi: 10.5194/gmd-7-303-2014
– ident: ref26
  doi: 10.1007/978-3-0348-5460-3_7
– ident: ref57
  doi: 10.1137/1.9780898717921
– ident: ref3
  doi: 10.1007/s00211-013-0518-8
– ident: ref40
– ident: ref6
  doi: 10.1002/qj.3209
– ident: ref21
  doi: 10.1126/science.aam5745
– ident: ref46
  doi: 10.5194/amt-11-6539-2018
– ident: ref51
  doi: 10.1142/9789812813718
– ident: ref29
  doi: 10.5194/acp-7-2413-2007
– ident: ref16
– ident: ref34
  doi: 10.5194/acp-8-6341-2008
– ident: ref28
  doi: 10.5194/gmd-10-3695-2017
– ident: ref42
  doi: 10.1016/j.atmosenv.2018.05.044
– ident: ref11
  doi: 10.5194/acp-21-6663-2021
– ident: ref33
  doi: 10.1175/2008JTECHA1082.1
– ident: ref39
  doi: 10.5194/acp-18-6785-2018
– ident: ref45
– ident: ref35
  doi: 10.1029/2003JD004422
– ident: ref54
  doi: 10.1002/nla.2325
– ident: ref12
  doi: 10.1088/1748-9326/abfac1
– ident: ref13
  doi: 10.5194/gmd-2021-393
– ident: ref43
  doi: 10.1117/12.974954
– ident: ref1
  doi: 10.1111/j.1600-0889.2006.00218.x
– ident: ref20
  doi: 10.1525/elementa.188
– ident: ref25
  doi: 10.5194/bg-9-457-2012
– ident: ref5
  doi: 10.1137/1.9781611971484
– ident: ref41
  doi: 10.5194/gmd-13-1771-2020
– ident: ref4
  doi: 10.1017/S0962492918000016
– ident: ref50
  doi: 10.1137/15M1037925
– ident: ref22
  doi: 10.1017/CBO9780511535741
– ident: ref49
  doi: 10.7551/mitpress/3206.001.0001
– ident: ref47
  doi: 10.5194/acp-22-1097-2022
– ident: ref44
  doi: 10.1007/s00703-010-0068-x
– ident: ref58
  doi: 10.5194/gmd-6-583-2013
– ident: ref15
  doi: 10.1137/16M1081968
– ident: ref8
– ident: ref56
  doi: 10.5194/acp-16-13449-2016
– ident: ref7
  doi: 10.1017/9781316544754
– ident: ref52
  doi: 10.1029/2011WR011778
– ident: ref14
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Snippet Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of...
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StartPage 5547
SubjectTerms Air pollution
Algorithms
Atmospheric models
Carbon
Computational efficiency
Computer applications
Covariance matrix
Earth surface
Emissions
Exploitation
Fluxes
Gases
Greenhouse gases
Inverse problems
Iterative algorithms
Iterative methods
Mathematical models
Methods
Modelling
Observatories
Random variables
Regularization
Robustness (mathematics)
Satellite observation
Satellites
Surface fluxes
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Title Computationally efficient methods for large-scale atmospheric inverse modeling
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Volume 15
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