Retrieving XCO2from GOSAT FTS over east asia using simultaneous aerosol information from CAI

Woogyung Kim, Jhoon Kim, Yeonjin Jung, Hartmut Boesch, Hanlim Lee, Sanghee Lee, Tae Young Goo, Ukkyo Jeong, Mijin Kim, Chun Ho Cho, Mi Lim Ou

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number994
JournalRemote Sensing
Volume8
Issue number12
DOIs
Publication statusPublished - 2016

Bibliographical note

Funding Information:
This work was supported by NIMS Research Grant ?Development and Application of Methodology for Climate Change Prediction?. This research was also supported by the GEMS program of the Ministry of Environment, Korea, and the Eco Innovation Program of KEITI (2012000160002). Tsukuba and Saga TCCON data were obtained from the TCCON Data Archive, hosted by the Carbon Dioxide Information Analysis Center (CDIAC) (tccon.onrl.gov) and operated by Shuji Kawakami and Isamu Morino. We thank the NIES and TCCON for providing measurement data. We also appreciate ACOS, NIES and University of Leicester group for providing retrieval data.

Funding Information:
This work was supported by NIMS Research Grant �Development and Application of Methodology for Climate Change Prediction�. This research was also supported by the GEMS program of the Ministry of Environment, Korea, and the Eco Innovation Program of KEITI (2012000160002). Tsukuba and Saga TCCON data were obtained from the TCCON Data Archive, hosted by the Carbon Dioxide Information Analysis Center (CDIAC) (tccon.onrl.gov) and operated by Shuji Kawakami and Isamu Morino. We thank the NIES and TCCON for providing measurement data. We also appreciate ACOS, NIES and University of Leicester group for providing retrieval data.

Publisher Copyright:
© 2016 by the authors; licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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