Simulation-based uncertainty quantification for estimating atmospheric CO2 from satellite data

SIAM-ASA Journal on Uncertainty Quantification

Volumn 5, Issue 1, 2017, Pages 956-985

  Hobbs, Jonathan ^a   Braverman, Amy ^a   Cressie, Noel ^b   Granat, Robert ^a   Gunson, Michael ^a  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

Bayesian inference; Inverse problem; Nonlinear model; Optimal estimation; Radiative transfer; Simulation experiment; Surrogate model

Indexed keywords

BAYESIAN NETWORKS; CARBON DIOXIDE; CARBON DIOXIDE PROCESS; EARTH ATMOSPHERE; FORWARD SCATTERING; GREENHOUSE GASES; INFERENCE ENGINES; NASA; ORBITS; RADIATIVE TRANSFER; REMOTE SENSING; UNCERTAINTY ANALYSIS;

ABSORPTION AND SCATTERINGS; BAYESIAN INFERENCE; COMPUTATIONALLY EFFICIENT; NON-LINEAR MODEL; OPTIMAL ESTIMATIONS; SPATIAL AND TEMPORAL RESOLUTIONS; SURROGATE MODEL; UNCERTAINTY QUANTIFICATIONS;

INVERSE PROBLEMS;

EID: 85040817669     PISSN: None     EISSN: 21662525     Source Type: Journal    
DOI: 10.1137/16M1060765     Document Type: Article

References (27)

1. A. Baccini, S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton, Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nature Clim. Change, 2 (2012), pp. 182-185.
2. V. Buchard, A. M. da Silva, P. R. Colarco, A. Darmenov, C. A. Randles, R. Govindaraju, O. Torres, J. Campbell, and R. Spurr, Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA aerosol reanalysis, Atmos. Chem. Phys., 15 (2015), pp. 5743-5760.
3. P. Clais, C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. L. Quere, R. B. Myneni, S. Piao, and P. Thornton , Carbon and other biogeochemical cycles, in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, eds., Cambridge University Press, Cambridge, 2013, pp. 465-570.
4. B. Connor, H. Bffosch, J. McDuffie, T. Taylor, D. Fu, C. Frankenberg, C. O'Dell, V. H. Payne, M. Gunson, R. Pollock, J. Hobbs, F. Oyafuso, and Y. Jiang, Quantification of uncertainties in OCO-2 measurements of XCO2: Simulations and linear error analysis, Atmos. Measure. Tech., 9 (2016), pp. 5227-5238.
5. B. J. Connor, H. Boesch, G. Toon, B. Sen, C. Miller, and D. Crisp, Orbiting carbon observatory: Inverse method and prospective error analysis, J. Geophys. Res. Atmos., 113 (2008), D05305, https://doi.org/10.1029/2006JD008336.
6. N. Cressie and S. Burden, Figures of merit for simultaneous inference and comparisons in simulation experiments, Stat, 4 (2015), pp. 196-211.
7. N. Cressie and R. Wang, Statistical properties of the state obtained by solving a nonlinear multivariate inverse problem, Appl. Stoch. Models Bus. Ind., 29 (2013), pp. 424-438.
8. N. Cressie, R. Wang, M. Smyth, and C. E. Miller, Statistical bias and variance for the regularized inverse problem: Application to space-based atmospheric CO2 retrievals, J. Geophys. Res. Atmos., 121 (2016), pp. 5526-5537.
9. D. Crisp, H. Boesch, L. Brown, R. Castano, M. Christi, B. Connor, A. Eldering, C. Frankenberg, M. Gunson, J. McDuffie, C. Miller, V. Natraj, C. O'Dell, D. O'Brien, I. Polonsky, F. Oyafuso, M. Smyth, D. Thompson, G. Toon, and R. Spurr, Orbiting Carbon Observatory (OCO)-2 Level 2 Full Physics Algorithm Theoretical Basis Document, Technical report, OCO D- 65488, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 2014.
10. M. Davidian and D. M. Giltinan, Nonlinear models for repeated measurement data: An overview and update, J. Agric. Biol. Environ. Stat., 8 (2003), pp. 387-419.
11. A. Eldering, R. Pollock, R. Lee, R. Rosenberg, F. Oyafuso, D. Crisp, L. Chapsky, and R. Granat, Orbiting Carbon Observatory (OCO)-2 Level 1b Theoretical Basis Document, Technical report, OCO D-55206, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 2015.
12. H. Haario, M. Laine, M. Lehtinen, E. Saksman, and J. Tamminen, Markov chain Monte Carlo methods for high dimensional inversion in remote sensing, J. Roy. Statist. Soc. Ser. B, 66 (2004), pp. 1-17.
13. J. R. Harring and J. Liu, A comparison of estimation methods for nonlinear mixed effects models under model misspecification and data sparseness: A simulation study, J. Mod. Appl. Stat. Meth., 15 (2016), pp. 539-569.
14. A. Hartford and M. Davidian, Consequences of misspecifying assumptions in nonlinear mixed effects models, Comput. Statist. Data Anal., 34 (2000), pp. 139-164.
15. P. K. Kitanidis, Quasi-linear geostatistical theory for inversing, Water Res. Res., 31 (1995), pp. 2411- 2419.
16. V. Natraj and R. J. D. Spurr, A fast linearized pseudo-spherical two orders of scattering model to account for polarization in vertically inhomogeneous scattering-absorbing media, J. Quant. Spectros., 107 (2007), pp. 263-293.
17. NRC, Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Veri- cation, Validation, and Uncertainty Quantification, The National Academies Press, Washington, DC, 2012.
18. C. W. O'Dell, Acceleration of multiple-scattering, hyperspectral radiative transfer calculations via low-streams interpolation, J. Geophys. Res. Atmos., 115 (2010), D10206, https://doi.org/10.1029/2009JD012803.
19. C. W. O'Dell, B. Connor, H. Boesch, D. O'Brien, C. Frankenberg, R. Castano, A. Eldering, B. Fisher, M. Gunson, J. McDuffie, C. E. Miller, V. Natraj, F. Oyafuso, I. Polonsky, M. Smyth, T. Taylor, G. C. Toon, P. O. Wennberg, and D. Wunch, The ACOS CO2 retrieval algorithm - Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5 (2012), pp. 99-121.
20. A. O'Hagan, Probabilistic uncertainty specification: Overview, elaboration techniques and their applica- tion to a mechanistic model of carbon ux, Environ. Model. Assess., 36 (2012), pp. 35-48.
21. L. E. Ott, S. Pawson, G. J. Collatz, W. W. Gregg, D. Menemenlis, H. Brix, C. S. Rousseaux, K. W. Bowman, J. Liu, A. Eldering, M. R. Gunson, and S. R. Kawa, Assessing the magnitude of CO2 ux uncertainty in atmospheric CO2 records using products from NASA's Carbon Monitoring Flux Pilot Project, J. Geophys. Res. Atmos., 120 (2015), pp. 734-765.
22. C. D. Rodgers, Inverse Methods for Atmospheric Sounding, World Scientific, Hackensack, NJ, 2000.
23. C. D. Rodgers and B. J. Connor, Intercomparison of remote sounding instruments, J. Geophys. Res. Atmos., 108 (2003), 4116, https://doi.org/10.1029/2002JD002299.
24. J. Tamminen, Validation of nonlinear inverse algorithms with Markov chain Monte Carlo method, J. Geophys. Res. Atmos., 109 (2004), 019303, https://doi.org/10.1029/2004JD004927.
25. J. Tamminen and E. Kyrffolffa, Bayesian solution for nonlinear and non-Gaussian inverse problems by Markov chain Monte Carlo method, J. Geophys. Res. Atmos., 106 (2001), pp. 14, 377-14, 390.
26. W. Wanner, A. H. Strahler, B. Hu, P. Lewis, J.-P. Muller, X. Li, C. L. B. Schaaf, and M. J. Barnsley, Global retrieval of bidirectional reectance and albedo over land from EOS MODIS and MISR data: Theory and algorithm, J. Geophys. Res. Atmos., 102 (1997), pp. 17, 143-17, 161.
27. J. X. Warner, Z. Wei, L. L. Strow, C. D. Barnet, L. C. Sparling, G. Diskin, and G. Sachse, Improved agreement of AIRS tropospheric carbon monoxide products with other EOS sensors using optimal estimation retrievals, Atmos. Chem. Phys., 10 (2010), pp. 9521-9533.