Multi-layer Retrievals of Greenhouse Gases from a Combined Use of GOSAT TANSO-FTS SWIR and TIR
Abstract
The TANSO-FTS sensor onboard GOSAT has three frequency bands in the shortwave infrared (SWIR) and the fourth band in the thermal infrared (TIR). Observations of high-resolution spectra of reflected sunlight in the SWIR are extensively utilized to retrieve column-averaged concentrations of the major greenhouse gases such as carbon dioxide (XCO2) and methane (XCH4). Although global XCO2 and XCH4 distribution retrieved from SWIR data can reduce the uncertainty in the current knowledge about sources and sinks of these gases, information on the vertical profiles would be more useful to constrain the surface flux and also to identify the local emission sources. Based on the degrees of freedom for signal, Kulawik et al. (2016, IWGGMS-12 presentation) shows that 2-layer information on the concentration of CO2 can be extracted from TANSO-FTS SWIR measurements, and the retrieval error is predicted to be about 5 ppm in the lower troposphere. In this study, we present multi-layer retrievals of CO2 and CH4 from a combined use of measurements of TANSO-FTS SWIR and TIR. We selected GOSAT observations at Railroad Valley Playa in Nevada, USA, which is a vicarious calibration site for TANSO-FTS, as we have various ancillary data including atmospheric temperature and humidity taken by a radiosonde, surface temperature, and surface emissivity with a ground based FTS. All of these data are useful especially for retrievals using TIR spectra. Currently, we use the 700-800 cm-1 and 1200-1300 cm-1 TIR windows for CO2 and CH4 retrievals, respectively, in addition to the SWIR bands. We found that by adding TIR windows, 3-layer information can be extracted, and the predicted retrieval error in the CO2 concentration was reduced about 1 ppm in the lower troposphere. We expect that the retrieval error could be further reduced by optimizing TIR windows and by reducing systematic forward model errors.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFM.A41F0106K
- Keywords:
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- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSESDE: 3337 Global climate models;
- ATMOSPHERIC PROCESSESDE: 3339 Ocean/atmosphere interactions;
- ATMOSPHERIC PROCESSESDE: 3360 Remote sensing;
- ATMOSPHERIC PROCESSES