Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data

Remote sensing of the atmosphere has provided a wealth of data for analyses and inferences in earth science. Satellite observations can provide information on the atmospheric state at fine spatial and temporal resolution while providing substantial coverage across the globe. For example, this capability can greatly enhance the understanding of the space-time variation of the greenhouse gas, carbon dioxide ($CO_2$), since ground-based measurements are limited. NASA's Orbiting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily, and the mission's retrieval algorithm processes these indirect measurements into estimates of atmospheric $CO_2$. The retrieval is an inverse problem and consists of a physical forward model for the transfer of radiation through the atmosphere that includes absorption and scattering by gases, aerosols, and the surface. The model and other algorithm inputs introduce key sources of uncertainty into the retrieval problem. This article dev...

[1]  Anthony O'Hagan,et al.  Probabilistic uncertainty specification: Overview, elaboration techniques and their application to a mechanistic model of carbon flux , 2012, Environ. Model. Softw..

[2]  Marie Davidian,et al.  Consequences of misspecifying assumptions in nonlinear mixed effects models , 2000 .

[3]  Jeffrey R. Harring,et al.  A Comparison of Estimation Methods for Nonlinear Mixed-Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study , 2016 .

[4]  L. Larrabee Strow,et al.  Improved agreement of AIRS tropospheric carbon monoxide products with other EOS sensors using optimal estimation retrievals , 2010 .

[5]  Noel A Cressie,et al.  Statistical properties of the state obtained by solving a nonlinear multivariate inverse problem , 2012 .

[6]  Hartmut Boesch,et al.  Orbiting Carbon Observatory: Inverse method and prospective error analysis , 2008 .

[7]  Noel A Cressie,et al.  Figures of merit for simultaneous inference and comparisons in simulation experiments , 2015 .

[8]  Corinne Le Quéré,et al.  Carbon and Other Biogeochemical Cycles , 2014 .

[9]  B. Connor,et al.  Quantification of uncertainties in OCO-2 measurements of XCO 2 : simulations and linear error analysis , 2016 .

[10]  Bayesian solution for nonlinear and non‐Gaussian inverse problems by Markov chain Monte Carlo method , 2001 .

[11]  Watson W. Gregg,et al.  Assessing the magnitude of CO2 flux uncertainty in atmospheric CO2 records using products from NASA's Carbon Monitoring Flux Pilot Project , 2015 .

[12]  James R. Campbell,et al.  Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis , 2014 .

[13]  Christopher W. O'Dell,et al.  Acceleration of multiple‐scattering, hyperspectral radiative transfer calculations via low‐streams interpolation , 2008 .

[14]  H. Haario,et al.  Markov chain Monte Carlo methods for high dimensional inversion in remote sensing , 2004 .

[15]  Robert J. D. Spurr,et al.  A fast linearized pseudo-spherical two orders of scattering model to account for polarization in vertically inhomogeneous scattering–absorbing media , 2007 .

[16]  Rebecca Castano,et al.  The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations , 2011 .

[17]  Marie Davidian,et al.  Nonlinear models for repeated measurement data: An overview and update , 2003 .

[18]  P. Kitanidis.  Quasi‐Linear Geostatistical Theory for Inversing , 1995 .

[19]  Wei Chen,et al.  Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification , 2012 .

[20]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[21]  Michael J. Barnsley,et al.  Global retrieval of bidirectional reflectance and albedo over land , 1997 .

[22]  B. Connor,et al.  Intercomparison of remote sounding instruments , 1999 .

[23]  J. Tamminen.  Validation of nonlinear inverse algorithms with Markov chain Monte Carlo method , 2004 .

[24]  Noel A Cressie,et al.  Statistical bias and variance for the regularized inverse problem: Application to space‐based atmospheric CO2 retrievals , 2016 .