Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra

Abstract

Abstract. Column-averaged volume mixing ratios of carbon dioxide and methane retrieved from the Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed observation (GOSAT SWIR X CO 2 and X CH 4 ) were compared with the reference calibrated data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) participating in the Total Carbon Column Observing Network (TCCON).Preliminary results are as follows: the GOSAT SWIR X CO 2 and X CH 4 (Version 01.xx) are biased low by 8.85 ± 4.75 ppm (2.3 ± 1.2 %) and 20.4 ± 18.9 ppb (1.2 ± 1.1 %), respectively. The standard deviation of the GOSAT SWIR X CO 2 and X CH 4 is about 1 % (1σ ) after correcting the negative biases of X CO 2 and X CH 4 by 8.85 ppm and 20.4 ppb, respectively. The latitudinal distributions of zonal means of the GOSAT SWIR X CO 2 and X CH 4 show similar features to those of the g-b FTS data except for the negative biases in the GOSAT data.

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.…”

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

“…These constituents, which include CO 2 and CH 4 , are retrieved from near-infrared solar absorption spectra using a nonlinear least-squares fitting algorithm referred to as GFIT (Wunch et al, , 2011a. The TC-CON data have been used to compare with satellite data and model simulations (Dils et al, 2006;Morino et al, 2011;Schneising et al, 2012;Saito et al, 2012;Heymann et al, 2012;Oshchepkov et al, 2013;Yoshida et al, 2013;Belikov et al, 2013;Dils et al, 2014;Nguyen et al, 2014;Scheepmaker et al, 2015) and elucidate the temporal behavior of greenhouse gases (Wunch et al, 2009;Messerschmidt et al, 2010;Ishizawa et al, 2016a). In this study, we used TCCON data analyzed with the GGG2014 version of the standard TCCON retrieval algorithm (Wunch et al, 2015) for correction of GOSAT data.…”

Performance simulations for a spaceborne methane lidar mission

“…Of particular concern, using these sensors however is the inability to make measurements in the high latitudes during the winter months, leaving large areas of the globe unobserved for a considerable amount of time. The data may also be biased in regions with aerosol layers or thin cirrus clouds [Morino et al, 2011] or over oceans [Basu et al, 2013]. On the other hand, infrared sensors like Atmospheric Infrared Sounder and Infrared Atmospheric Sounding Interferometer are not sensitive close to the methane sources on ground due to their unfavorable weighting functions [Xiong et al, 2013].…”