Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring

Kuze, Akihiko; Shimada, Hiroshi; Nakajima, M.; Hamazaki, Takashi

doi:10.1364/ao.48.006716

Cited by 841 publications

(705 citation statements)

References 25 publications

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“…4. To monitor the spatial distribution of atmospheric greenhouse gases from space, GOSAT was launched on 23 January 2009 into a sun-synchronous orbit with an overpass time of roughly 13:00 local time (Kuze et al, 2009). Over a 3-day period, TANSO-FTS onboard GOSAT makes observations above several tens of thousands of ground points spread over the earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Bias corrections of GOSAT SWIR XCO2 and XCH4 with TCCON data and their evaluation using aircraft measurement data

et al. 2016

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Abstract. We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO 2 (XCO 2 ) and CH 4 (XCH 4 ) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TC-CON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO 2 and XCH 4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO 2 /XCH 4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

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Section: Introductionmentioning

confidence: 99%

Bias corrections of GOSAT SWIR XCO2 and XCH4 with TCCON data and their evaluation using aircraft measurement data

et al. 2016

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“…Here, we retrieve the column-averaged deuterium depletion of atmospheric water vapor using the Japanese Greenhouse gases Observing Satellite (GOSAT, Hamazaki et al, 2005;Kuze et al, 2009), which was launched on 23 January 2009 into a sun-synchronous orbit with a local overpass time of 13:00 LT. ≈ 10 000 soundings with 82 km 2 circular spatial footprints are recorded daily, repeating a regularly spaced global footprint grid every 3 days. Spectra are recorded by the TANSO Fourier Transform Spectrometer (FTS) onboard GOSAT.…”

Section: Introductionmentioning

confidence: 99%

Water vapor isotopologue retrievals from high-resolution GOSAT shortwave infrared spectra

et al. 2013

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Remote sensing of the isotopic composition of water vapor can provide valuable information on the hydrological cycle. Here, we demonstrate the feasibility of retrievals of the relative abundance of HDO (the HDO/H2O ratio) from the Japanese GOSAT satellite. For this purpose, we use high spectral resolution nadir radiances around 6400 cm−1 (1.56 μm) to retrieve vertical column amounts of H2O and HDO. Retrievals of H2O correlate well with ECMWF (European Centre for Medium-Range Weather Forecasts) integrated profiles (r2 = 0.96). Typical precision errors in the retrieved column-averaged deuterium depletion (δD) are 20–40&permil;. We compare δD against a TCCON (Total Carbon Column Observing Network) ground-based station in Lamont, Oklahoma. Using retrievals in very dry areas over Antarctica, we detect a small systematic offset in retrieved H2O and HDO column amounts and take this into account for a bias correction of δD. Monthly averages of δD in the June 2009 to September 2011 time frame are well correlated with TCCON (r2 = 0.79) and exhibit a slope of 0.98 (1.23 if not bias corrected). We also compare seasonal averages on the global scale with results from the SCIAMACHY instrument in the 2003–2005 time frame. Despite the lack of temporal overlap, seasonal averages in general agree well, with spatial correlations (r2) ranging from 0.62 in September through November to 0.83 in June through August. However, we observe higher variability in GOSAT δD, indicated by fitted slopes between 1.2 and 1.46. The discrepancies are likely related to differences in vertical sensitivities but warrant further validation of both GOSAT and SCIAMACHY and an extension of the validation dataset

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“…These regions include For the construction of the "measured signal" R , we used the scalar spectral radiance S that was generated by NIES operational algorithm for CO 2 retrievals [17]. This scalar radiance was computed from P-and S-signal polarizations provided by the Japan Aerospace Exploration Agency (JAXA) within the L1B product [18].…”

Section: Reference Bases For the Eof Decompositionmentioning

confidence: 99%

EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations

Bril

Maksyutov

Belikov

et al. 2017

Journal of Quantitative Spectroscopy and Radiative Transfer

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, I., Velazco, V. et al (2017). EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations. Journal of Quantitative Spectroscopy and Radiative Transfer, 189 258-266.EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations Abstract This paper presents a novel retrieval algorithm for the rapid retrieval of the carbon dioxide total column amounts from high resolution spectra in the short wave infrared (SWIR) range observations by the Greenhouse gases Observing Satellite (GOSAT). The algorithm performs EOF (Empirical Orthogonal Function)-based decomposition of the measured spectral radiance and derives the relationship of limited number of the decomposition coefficients in terms of the principal components with target gas amount and a priori data such as airmass, surface pressure, etc. The regression formulae for retrieving target gas amounts are derived using training sets of collocated GOSAT and ground-based observations. The precision/accuracy characteristics of the algorithm are analyzed by the comparison of the retrievals with those from the Total Carbon Column Observing Network (TCCON) measurements and with the modeled data, and appear similar to those achieved by full-physics retrieval algorithms.Keywords amount, eof-based, regression, algorithm, fast, gosat, retrieval, observations, atmospheric, co2, total, column Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication DetailsBril, A., Maksyutov, S., Belikov, D., Oshchepkov, S., Yoshida, Y., Deutscher, N. M., Griffith, D., Hase, F., Kivi, R., Morino, I., Velazco, V. et al (2017). EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations. IMK-IFU, Garmisch-Partenkirchen 82467, Germany *Corresponding author: andrey.bril@gmail.com This paper presents a novel retrieval algorithm for the rapid retrieval of the carbon dioxide total column amounts from high resolution spectra in the short wave infrared (SWIR) range observations by the Greenhouse gases Observing Satellite (GOSAT). The algorithm performs EOF (Empirical Orthogonal Function)-based decomposition of the measured spectral radiance and derives the relationship of limited number of the decomposition coefficients in terms of the principal components with target gas amount and a priori data such as airmass, surface pressure, etc. The regression formulae for retrieving target gas amounts are derived using training sets of collocated GOSAT and 2 ground-based observations. The precision/accuracy characteristics of the algorithm are analyzed by the comparison of the retrievals with those from the Total Carbon Column Observing Network (TCCON) measurements and with the modeled data, and appear similar to those achieved by full-physics retrieval algorithms.

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Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring

Cited by 841 publications

References 25 publications

Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data

Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data

Water vapor isotopologue retrievals from high-resolution GOSAT shortwave infrared spectra

EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations

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Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring

Cited by 841 publications

References 25 publications

Bias corrections of GOSAT SWIR XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt; with TCCON data and their evaluation using aircraft measurement data

Bias corrections of GOSAT SWIR XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt; with TCCON data and their evaluation using aircraft measurement data

Water vapor isotopologue retrievals from high-resolution GOSAT shortwave infrared spectra

EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations

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Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data

Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data