Validation of XCO<sub>2</sub> derived from SWIR spectra of GOSAT TANSO-FTS with aircraft measurement data

Abstract

Abstract. Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by 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 HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

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

“…XCO 2 has a seasonal amplitude of approximately 7-12 ppm at mid-latitudes over the Northern Hemisphere. In this study, the growth rate of uncorrected GOSAT XCO 2 was roughly 2.5 ppm yr −1 from 2009 to 2013, while Inoue et al (2013) showed that the growth rate of aircraft-based XCO 2 at most sites was about 2.0 ppm yr −1 from 2007 to 2010. This is consistent with the rapid increase of CO 2 emissions over the last few years.…”

“…To calculate aircraft-based XCO 2 and XCH 4 (as described in the next paragraph), we also used tower data from the Meteorological Research Institute (MRI) in Tsukuba (Inoue andMatsueda, 1996, 2001) and the NOAA ESRL/GMD tall tower network in Park Falls, WI and West Branch, IA . Details of the aircraft and tower measurements are described in Inoue et al (2013) and Inoue et al (2014), except for the JMA aircraft and groundbased measurements. The JMA aircraft measurements are conducted by utilizing the cargo aircraft C-130H of the Japan Ministry of Defense (MOD) to collect flask air samples once a month during a regular flight between the mainland of Japan and Minamitorishima, an island located nearly 2000 km southeast of Tokyo (Tsuboi et al, 2013).…”

“…Column-averaged dry air mole fractions of CO 2 (XCO 2 ) and CH 4 (XCH 4 ) are retrieved from the Short-Wavelength InfraRed (SWIR) spectra of the Thermal And Near-infrared Sensor for carbon Observation -Fourier Transform Spectrometer (TANSO-FTS) onboard GOSAT. Validation of XCO 2 and XCH 4 derived from the GOSAT TANSO-FTS has been conducted by using ground-based high-resolution Fourier Transform Spectrometer (ground-based FTS) data and aircraft measurements Saitoh et al, 2012;Yoshida et al, 2013;Inoue et al, 2013Inoue et al, , 2014Gavrilov et al, 2014). The results showed that the GOSAT SWIR XCO 2 measurements (Ver.…”

A method for colocating satellite <i>X</i><sub>CO<sub>2</sub></sub> data to ground-based data and its application to ACOS-GOSAT and TCCON

“…Geographical colocation methodology is perhaps the most popular colocation methodology due to its simplicity and straightforwardness. Examples of geographical coincident criteria include selecting all same-day satellite observations falling within ±5 • of a location of interest (Inoue et al, 2013), selecting data falling within ±30 min from about 0.5 to 1.5 • rectangles centered at each validation site (Morino et al, 2011), selecting data within 5 • and ±2 h (Butz et al, 2011;Cogan et al, 2012), selecting observations within a 10 • × 10 • lat-long box (Reuter et al, 2013), and selecting weekly data that fall within a 5 • radius of a validation site (Oshchepkov et al, 2012). For the performance comparison in this section, we define a geographical colocation methodology by averaging all same-day satellite observations falling within 500 km of a location of interest.…”

“…Geographical colocation typically defines a spatiotemporal neighborhood region, also known as a coincidence criterion, around the location of interest and then take summary statistics (e.g., mean or median). Examples of geographical colocation includes averaging all same-day satellite observations falling within ±5 • of a location of interest (Inoue et al, 2013), averaging all observations falling within 5 • and ±2 h , and taking the monthly median of all observations within a 10 • × 10 • lat-long box (Reuter et al, 2013). More sophisticated colocation methodologies add other correlated geophysical covariates in constructing such "neighborhoods" under the principle that conditioning on these additional correlated covariates would improve the quality of the comparison.…”