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CO2 and CH4 Retrievals from GOSAT

Hartmut Boesch(1), Robert Parker(1), Diane Knappett(1), Austin Cogan(1), Paul Palmer(2), Liang Feng(2), Tatsuya Yokota(3), Shamil Maksyutov(3), David Crisp(4) and Charles E. Miller(4)

(1) University of Leicester, University Road, Leicester LE1 7RH, United Kingdom
(2) University of Edinburgh, , , United Kingdom
(3) National Institute for Environmental Studies, , , Japan
(4) Jet Propulsion Laboratory, , , United States

Abstract

With the launch of the Japanese Greenhouse gas Observing SATellite (GOSAT) on 23 January 2009, the first observations from a dedicated greenhouse satellite sensor has become available. GOSAT provides global measurements of total column CO2 and CH4 from its shortwave infrared (SWIR) bands and of mid-tropospheric sub-columns from its thermal-IR bands.

Specifically, observations of total column CO2 and CH4 are well suited to improve our knowledge of sources and sinks. However, to infer the total columns with the required precision and accuracy is a major challenge for the applied retrieval algorithms mainly due to the presence of atmospheric aerosols and cirrus clouds.

Work is presented where the FSI WFM-DOAS retrieval algorithm has been applied to retrieve CO2 and CH4 total columns from SCIAMACHY. These retrievals have then been validated against TCCON FTS data and compared to model simulations. Such comparative studies between ground-based data and retrievals from SCIAMACHY's moderate resolution data and those from the new, higher resolution observations of GOSAT can facilitate the extension of the existing SCIAMACHY retrieval database to produce a longer-term data record.

We will present first results of CO2 and CH4 retrievals from GOSAT spectra using the retrieval algorithms developed for OCO and for SCIAMACHY. We will compare our results to results obtained with the GOSAT retrieval algorithm developed at NIES and against model calculations and ground-based column data. Furthermore, we will show preliminary results for the CO2 and CH4 retrievals from TIR spectra which can add additional information to the SWIR columns.