Mathematical algorithms for processing and analysis of near-infrared data from a satellite-borne Fourier transform spectrometer

Kataev, Mikhail Yu.; Kataev, S. G.; Maksyutov, Shamil; Andreev, A. G.; Bazelyuk, S. A.; Lukianov, A. K.

doi:10.1007/s11182-012-9816-3

Cited by 5 publications

(1 citation statement)

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“…An EOF-based approach was used for retrievals of the atmospheric methane profiles from the Atmospheric Infrared Sounder (AIRS) thermal infrared spectra [14]. The possibility of applying an EOF application to CO 2 retrievals from the GOSAT measurements in 1.6-µm CO 2 absorption band was demonstrated in [15]. However, information from the reflected sunlight radiance spectra in only the 1.6 µm band is generally insufficient for accurate CO 2 retrievals due to optical path modification by aerosol and clouds [4].…”

Section: New Greenhouse Gas Observing Missions Such As Oco-2 (Orbitimentioning

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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Section: New Greenhouse Gas Observing Missions Such As Oco-2 (Orbitimentioning

confidence: 99%