干旱区生态环境知识资源中心(Arid): Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.

Arid

DOI	10.2172/1096253
报告编号	SAND2013-7817
来源ID	OSTI_ID: 1096253
	Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.
	Ray, Jaideep; Lee, Jina; Lefantzi, Sophia; Yadav, Vineet; Michalak, Anna M.; van Bloemen Waanders, Bart Gustaaf; McKenna, Sean Andrew
英文摘要	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%.
出版年	2013
报告类型	Technical Report
语种	英语
国家	美国
URL	http://www.osti.gov/scitech/servlets/purl/1096253
资源类型	科技报告
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/270787
推荐引用方式 GB/T 7714	Ray, Jaideep,Lee, Jina,Lefantzi, Sophia,et al. Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.,2013.