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Remote Sens. 2016, 8(12), 994; doi:10.3390/rs8120994

Retrieving XCO2 from GOSAT FTS over East Asia Using Simultaneous Aerosol Information from CAI

1
Department of Atmospheric Sciences, Yonsei University, Seoul 03722, Korea
2
Earth Observation Science Group and National Centre for Eaerth Observation NCEO, University of Leicester, Leicester LE1 7RH, UK
3
Department of Geoinformatic Engineering, Pukyong National University, Busan 48513, Korea
4
National Institute of Meteorological Sciences, NIMS, Jeju 63568, Korea
5
Korea Meteorological Administration, KMA, Seoul 07062, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 16 September 2016 / Revised: 25 November 2016 / Accepted: 28 November 2016 / Published: 2 December 2016
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Abstract

In East Asia, where aerosol concentrations are persistently high throughout the year, most satellite CO2 retrieval algorithms screen out many measurements during quality control in order to reduce retrieval errors. To reduce the retrieval errors associated with aerosols, we have modified YCAR (Yonsei Carbon Retrieval) algorithm to YCAR-CAI to retrieve XCO2 from GOSAT FTS measurements using aerosol retrievals from simultaneous Cloud and Aerosol Imager (CAI) measurements. The CAI aerosol algorithm provides aerosol type and optical depth information simultaneously for the same geometry and optical path as FTS. The YCAR-CAI XCO2 retrieval algorithm has been developed based on the optimal estimation method. The algorithm uses the VLIDORT V2.6 radiative transfer model to calculate radiances and Jacobian functions. The XCO2 results retrieved using the YCAR-CAI algorithm were evaluated by comparing them with ground-based TCCON measurements and current operational GOSAT XCO2 retrievals. The retrievals show a clear annual cycle, with an increasing trend of 2.02 to 2.39 ppm per year, which is higher than that measured at Mauna Loa, Hawaii. The YCAR-CAI results were validated against the Tsukuba and Saga TCCON sites and show an root mean square error of 2.25, a bias of −0.81 ppm, and a regression line closer to the linear identity function compared with other current algorithms. Even after post-screening, the YCAR-CAI algorithm provides a larger dataset of XCO2 compared with other retrieval algorithms by 21% to 67%, which could be substantially advantageous in validation and data analysis for the area of East Asia. Retrieval uncertainty indicates a 1.39 to 1.48 ppm at the TCCON sites. Using Carbon Tracker-Asia (CT-A) data, the sampling error was analyzed and was found to be between 0.32 and 0.36 ppm for each individual sounding. View Full-Text
Keywords: CO2; aerosols; GOSAT; East Asia; optimal estimation CO2; aerosols; GOSAT; East Asia; optimal estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kim, W.; Kim, J.; Jung, Y.; Boesch, H.; Lee, H.; Lee, S.; Goo, T.-Y.; Jeong, U.; Kim, M.; Cho, C.-H.; Ou, M.-L. Retrieving XCO2 from GOSAT FTS over East Asia Using Simultaneous Aerosol Information from CAI. Remote Sens. 2016, 8, 994.

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