Potential of a geostationary geoCARB mission to estimate surface emissions of CO 2 , CH 4 and CO in a polluted urban environment: case study Shanghai

This paper describes a numerical experiment to test the ability of the proposed geoCARB satellite to estimate emissions of trace gases (CO2, CH4 and CO) in the polluted urban environment of Shanghai. The meteorology over Shanghai is simulated with the Weather Research and Forecasting (WRF) model for...

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Veröffentlicht in:Atmospheric measurement techniques 2016-09, Vol.9 (9), p.4633-4654
Hauptverfasser: O'Brien, Denis M., Polonsky, Igor N., Utembe, Steven R., Rayner, Peter J.
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Sprache:eng
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Zusammenfassung:This paper describes a numerical experiment to test the ability of the proposed geoCARB satellite to estimate emissions of trace gases (CO2, CH4 and CO) in the polluted urban environment of Shanghai. The meteorology over Shanghai is simulated with the Weather Research and Forecasting (WRF) model for a 9-day period in August 2010. The meteorology includes water and ice clouds. The chemistry version of WRF (WRF-Chem V3.6.1) is used to predict the chemical composition, mass density and number density of aerosol species. Spectra in the bands measured by geoCARB are calculated, including the effects of polarisation and multiple scattering of radiation by clouds, aerosols and molecules. Instrument noise is added, and column-averaged trace-gas mole fractions are estimated from the noisy spectra using an algorithm based on that for the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) but adapted to geoCARB. As expected, the high aerosol loadings are challenging. However, when the retrieval algorithm is provided with regionally adjusted aerosol optical properties, as might be determined from observations of dark targets within the field of regard, the accuracies of retrieved concentrations are comparable to those reported earlier for geoCARB. Statistics of the errors in the retrieved column-averaged concentrations are used to predict the reduction in uncertainty of surface emissions possible with remotely sensed data.
ISSN:1867-8548
1867-8548
DOI:10.5194/amt-9-4633-2016