Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.
Abstract
The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. The limited nature of the measured data leads to a severely-underdetermined estimation problem. If the estimation is performed at fine spatial resolutions, it can also be computationally expensive. In order to enable such estimations, advances are needed in the spatial representation of ffCO2 emissions, scalable inversion algorithms and the identification of observables to measure. To that end, we investigate parsimonious spatial parameterizations of ffCO2 emissions which can be used in atmospheric inversions. We devise and test three random field models, based on wavelets, Gaussian kernels and covariance structures derived from easily-observed proxies of human activity. In doing so, we constructed a novel inversion algorithm, based on compressive sensing and sparse reconstruction, to perform the estimation. We also address scalable ensemble Kalman filters as an inversion mechanism and quantify the impact of Gaussian assumptions inherent in them. We find that the assumption does not impact the estimates of mean ffCO2 source strengths appreciably, but a comparison with Markov chain Monte Carlo estimates show significant differences in themore »
- Authors:
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1096253
- Report Number(s):
- SAND2013-7817
474359
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Ray, Jaideep, Lee, Jina, Lefantzi, Sophia, Yadav, Vineet, Michalak, Anna M., van Bloemen Waanders, Bart Gustaaf, and McKenna, Sean Andrew. Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.. United States: N. p., 2013.
Web. doi:10.2172/1096253.
Ray, Jaideep, Lee, Jina, Lefantzi, Sophia, Yadav, Vineet, Michalak, Anna M., van Bloemen Waanders, Bart Gustaaf, & McKenna, Sean Andrew. Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.. United States. https://doi.org/10.2172/1096253
Ray, Jaideep, Lee, Jina, Lefantzi, Sophia, Yadav, Vineet, Michalak, Anna M., van Bloemen Waanders, Bart Gustaaf, and McKenna, Sean Andrew. 2013.
"Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.". United States. https://doi.org/10.2172/1096253. https://www.osti.gov/servlets/purl/1096253.
@article{osti_1096253,
title = {Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.},
author = {Ray, Jaideep and Lee, Jina and Lefantzi, Sophia and Yadav, Vineet and Michalak, Anna M. and van Bloemen Waanders, Bart Gustaaf and McKenna, Sean Andrew},
abstractNote = {The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. The limited nature of the measured data leads to a severely-underdetermined estimation problem. If the estimation is performed at fine spatial resolutions, it can also be computationally expensive. In order to enable such estimations, advances are needed in the spatial representation of ffCO2 emissions, scalable inversion algorithms and the identification of observables to measure. To that end, we investigate parsimonious spatial parameterizations of ffCO2 emissions which can be used in atmospheric inversions. We devise and test three random field models, based on wavelets, Gaussian kernels and covariance structures derived from easily-observed proxies of human activity. In doing so, we constructed a novel inversion algorithm, based on compressive sensing and sparse reconstruction, to perform the estimation. We also address scalable ensemble Kalman filters as an inversion mechanism and quantify the impact of Gaussian assumptions inherent in them. We find that the assumption does not impact the estimates of mean ffCO2 source strengths appreciably, but a comparison with Markov chain Monte Carlo estimates show significant differences in the variance of the source strengths. Finally, we study if the very different spatial natures of biogenic and ffCO2 emissions can be used to estimate them, in a disaggregated fashion, solely from CO2 concentration measurements, without extra information from products of incomplete combustion e.g., CO. We find that this is possible during the winter months, though the errors can be as large as 50%.},
doi = {10.2172/1096253},
url = {https://www.osti.gov/biblio/1096253},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Sep 01 00:00:00 EDT 2013},
month = {Sun Sep 01 00:00:00 EDT 2013}
}