Development of the empirical orthogonal functions-based algorithm for the retrievals of atmospheric CO2 total column amount from space-borne observations of reflected sunlight
| Autor: | Shamil Maksyutov, Anton Fedarenka, Ryoichi Imasu, Anatoli Chaikovsky, Chisa Iwasaki, Andrey Bril, Yukio Yoshida, Sergey Oshchepkov |
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| Rok vydání: | 2018 |
| Předmět: |
Data processing
010504 meteorology & atmospheric sciences Empirical orthogonal functions Surface pressure Collocation (remote sensing) 01 natural sciences 010309 optics 0103 physical sciences Principal component analysis General Earth and Planetary Sciences Environmental science Satellite Total Carbon Column Observing Network Algorithm Atmospheric optics 0105 earth and related environmental sciences |
| Zdroj: | Journal of Applied Remote Sensing. 12:1 |
| ISSN: | 1931-3195 |
| DOI: | 10.1117/1.jrs.12.046012 |
| Popis: | We present further development of the empirical orthogonal functions (EOF)-based retrieval algorithm. The algorithm output is a regression formula that relates principal components of the reflected sunlight spectra with CO2 total column amount. The algorithm was implemented and tested for the observations from the Japanese satellite Greenhouse gases Observing Satellite (GOSAT). Training of the EOF-based algorithm with the collocated ground-based and space-borne data (e.g., Total Carbon Column Observing Network and GOSAT observations, respectively) was shown to impose some errors that were interpreted as a result of implicit averaging over the collocation area. Alternative training with the small subset (∼5 % to 10%) of the full-physics algorithm is free of such errors; however, this option requires additional filtering of the space-borne observations that are strongly affected by atmospheric light scattering. This filtering was implemented by the comparison of the EOF-regression estimates of surface pressure with corresponding meteorological data. |
| Databáze: | OpenAIRE |
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