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• W2100055911 abstract "Abstract. Spectroscopic measurements of sunlight backscattered by the Earth's surface is a technique widely used for remote sensing of atmospheric constituent concentrations from space. Thereby, remote sensing of greenhouse gases poses particularly challenging accuracy requirements for instrumentation and retrieval algorithms which, in general, suffer from various error sources. Here, we investigate a method that helps disentangle sources of error for observations of sunlight backscattered from the glint spot on the ocean surface. The method exploits the backscattering characteristics of the ocean surface, which is bright for glint geometry but dark for off-glint angles. This property allows for identifying a set of clean scenes where light scattering due to particles in the atmosphere is negligible such that uncertain knowledge of the lightpath can be excluded as a source of error. We apply the method to more than 3 yr of ocean-glint measurements by the Thermal And Near infrared Sensor for carbon Observation (TANSO) Fourier Transform Spectrometer (FTS) onboard the Greenhouse Gases Observing Satellite (GOSAT), which aims at measuring carbon dioxide (CO2) and methane (CH4) concentrations. The proposed method is able to clearly monitor recent improvements in the instrument calibration of the oxygen (O2) A-band channel and suggests some residual uncertainty in our knowledge about the instrument. We further assess the consistency of CO2 retrievals from several absorption bands between 6400 cm−1 (1565 nm) and 4800 cm−1 (2100 nm) and find that the absorption bands commonly used for monitoring of CO2 dry air mole fractions from GOSAT allow for consistency better than 1.5 ppm. Usage of other bands reveals significant inconsistency among retrieved CO2 concentrations pointing at inconsistency of spectroscopic parameters." @default.
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• W2100055911 date "2013-09-27" @default.
• W2100055911 modified "2023-10-17" @default.
• W2100055911 title "Using ocean-glint scattered sunlight as a diagnostic tool for satellite remote sensing of greenhouse gases" @default.
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• W2100055911 doi "https://doi.org/10.5194/amt-6-2509-2013" @default.
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