The instrumentation and the BBM test results of thermal and near-infrared sensor for carbon observation (TANSO) on GOSAT

Urabe, Tomoyuki; Shimada, Hiroshi; Kaneko, Yutaka; Hamazaki, Takashi

doi:10.1117/12.677113

Cited by 28 publications

(17 citation statements)

References 1 publication

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“…GOSAT is equipped with two sensors; the Thermal and Near-infrared Sensor for Carbon ObservationFourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI) (Kuze et al 2006). TANSO-FTS detects short-wavelength infrared (SWIR) light reflected from the earth's surface, along with the thermal infrared (TIR) radiation emitted from the ground and atmosphere.…”

Section: Gosat Instrumentsmentioning

confidence: 99%

Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results

Yokota

Yoshida

Eguchi

et al. 2009

SOLA

525

372

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The Greenhouse Gases Observing Satellite (GOSAT) was launched on January 23, 2009, to monitor global atmospheric levels of CO2 and CH4 from space. GOSAT started initial operation of its instruments after an initial satellite system check. Although the radiant data obtained by the GOSAT instruments are currently in the preliminary stages of calibration and validation, the spectral absorption features of CO2 and CH4 are clearly identifiable. An initial retrieval of these gaseous concentrations was performed for measurement scenes of cloud-free conditions over land. These results showed that column-averaged dry air mole fractions of both CO2 and CH4 in the northern hemisphere were higher than those in the southern hemisphere. These latitudinal differences agree with data obtained from groundbased sources and other satellite observations; however, the absolute values of the gaseous concentrations from GOSAT data seem to have been underestimated. Calibrations as well as validation should be conducted to improve the quality of GOSAT retrievals.

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Section: Gosat Instrumentsmentioning

confidence: 99%

Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results

Yokota

Yoshida

Eguchi

et al. 2009

SOLA

525

372

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“…It has a wide swath that includes the IFOV of TANSO-FTS and has a spatial resolution of 0.5 km in the nadir direction. To monitor aerosol and cloud distributions, TANSO-CAI has a continuous spatial coverage with a spatial resolution of 0.5 km (1.5 km for band 4), which is a much higher spatial resolution than that of TANSO-FTS [30].…”

Section: Gosat Instrumentsmentioning

confidence: 99%

“…X CO2 is retrieved mainly from the weak CO 2 band at 1.6 µm, which is less affected by temperature compared with other gases. The O 2 A band at 0.76 µm is used to estimate the effective optical path length, which is affected by cloud, aerosols, and air mass [30]. The strong CO 2 band at 2.0 µm provide not only CO 2 information but also additional constraints on water vapor and optical path length uncertainties caused by clouds and aerosols [31].…”

Section: Gosat Instrumentsmentioning

confidence: 99%

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

Kim

Jung

et al. 2016

Remote Sensing

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Abstract:In East Asia, where aerosol concentrations are persistently high throughout the year, most satellite CO 2 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 X CO2 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 X CO2 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 X CO2 results retrieved using the YCAR-CAI algorithm were evaluated by comparing them with ground-based TCCON measurements and current operational GOSAT X CO2 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 X CO2 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.

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“…GOSAT has a 666 km sun-synchronous orbit and completes one orbit in ~100 minutes providing global coverage in approximately 3 days (Kadygrov et al, 2009). The GOSAT Thermal And NearInfrared Sensor for carbon Observation (TANSO) consists of two units: the Fourier Transform Spectrometer (FTS) and the Cloud Aerosol Imager (CAI) (Kuze et al, 2006). The TANSO-FTS has three bands in the Short Wave InfraRed (SWIR) region (0.76, 1.6, and 2.0 μm) and a wide Thermal Infrared (TIR) band (5.5-14.3 μm) with a circular ~10.5 km instantaneous field of view at nadir .…”

Section: Gosat Co 2 Productsmentioning

confidence: 99%

Comparison of Model-simulated Atmospheric Carbon Dioxide with GOSAT Retrievals

Shim¹,

Nassar²,

Kim³

2011

Asian J. Atmos. Environ

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Global atmospheric CO 2 distributions were simulated with a chemical transport model (GEOS-Chem) and compared with space-borne observations of CO 2 column density by GOSAT from April 2009 to January 2010. The GEOS-Chem model simulated 3-D global atmospheric CO 2 at 2� ×2.5�horizontal resolution using global CO 2 surface sources/sinks as well as 3-D emissions from aviation and the atmospheric oxidation of other carbon species. The seasonal cycle and spatial distribution of GEOS-Chem CO 2 columns were generally comparable with GOSAT columns over each continent with a systematic positive bias of 1.0%. Data from the World Data Center for Greenhouse Gases (WDCGG) from twelve ground stations spanning 90� S-82� N were also compared with the modeled data for the period of 2004-2009 inclusive. The ground-based data show high correlations with the GEOS-Chem simulation (0.66‹R 2 ‹0.99) but the model data have a negative bias of ~1.0%, which is primarily due to the model initial conditions. Together these two comparisons can be used to infer that GOSAT CO 2 retrievals underestimate CO 2 column concentration by ~2.0%, as demonstrated in recent validation work using other methods. We further estimated individual source/sink contributions to the global atmospheric CO 2 budget and trends through 7 tagged CO 2 tracers (fossil fuels, ocean exchanges, biomass burning, biofuel burning, net terrestrial exchange, shipping, aviation, and CO oxidation) over [2004][2005][2006][2007][2008][2009]. The global CO 2 trend over this period (2.1 ppmv/year) has been mainly driven by fossil fuel combustion and cement production (3.2 ppmv/year), reinforcing the fact that rigorous CO 2 reductions from human activities are necessary in order to stabilize atmospheric CO 2 levels.

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The instrumentation and the BBM test results of thermal and near-infrared sensor for carbon observation (TANSO) on GOSAT

Cited by 28 publications

References 1 publication

Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results

Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results

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

Comparison of Model-simulated Atmospheric Carbon Dioxide with GOSAT Retrievals

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