5:15 PM - 6:30 PM
[AAS12-P04] Development of a new match-up method of the satellite and ground-based greenhouse gases data by trajectory analysis for the GOSAT-2 data validation
Keywords:GOSAT, TCCON, match-up
The SWIR (Short Wavelength InfraRed) surface scattered solar spectra observed by the TANSO-FTS (Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer) onboard GOSAT is used to retrieve column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4). TCCON (Total Carbon Column Observing Network) is a ground-based observation network using Bruker IFS 120HR/125HR high-resolution fourier transform spectrometers. The GOSAT data, XCO2 and XCH4, has been validated using TCCON data. However, GOSAT and TCCON data are not completely spatio-temporally matched due to characteristics of the satellite orbit.
For example, one of the co-location methods uses a geometric distance between GOSAT and TCCON data to obtain matched data (Geophysical co-location method, e.g., Morino et al., 2011). Validation of GOSAT requires statistically significant match-up number but there is not enough match-up number with geophysical co-location method. In the case of XCO2, various methods for increasing the number of match-up data have been developed: the same potential temperature field at 700-hPa as a proxy for equivalent latitude for CO2 gradients (Keppel-Aleks et al., 2011, Wunch et al., 2011), and the same concentration field predicted or assimilated with the atmospheric transport model (Guerlet et al., 2013). For the time gap, it has been used the same day or within the time range of GOSAT overpass time because GOSAT has three-day revisit and sun-synchronous orbit with a local time around 13h at descending. Increasing the number of match-up data in consideration of the gaps of time and space is important for advanced validation.
In this study, we develop a new match-up method utilizing the forward and backward trajectory analyses from GOSAT observation position by HYSPLIT model. The new method is applicable to other gases not only XCO2 using flow of air masses. We show the result of analyzed bias variation with the spatio-temporal gap and the effectiveness of the developed method together with results using other match-up methods.