AIRS on the EOS/Aqua satellite was launched in polar orbit (13:30 LST, ascending node) in May 2002. It has 2378 channels covering 649–1136, 1217–1613 and 2169–2674 cm-1 at high spectral resolution (λ/Δλ=1200, ∼ 0.5 cm-1) (Aumann et al., 2003), and the noise equivalent differential temperature (NeΔT) at a reference temperature of 250 K ranges from 0.14 K in the 4.2 µm region (the lower tropospheric sounding) to 0.35 K in the 15 µm region (the upper tropospheric sounding). The spatial resolution of AIRS is 13.5 km at nadir, and in a 24 h period, AIRS nominally observes the complete globe twice per day. In order to retrieve CH4 in both clear and partially cloudy scenes, nine AIRS fields of view (FOVs) within the footprint of the Advanced Microwave Sounding Unit (AMSU) are used to derive a single cloud-cleared radiance spectrum in a field of regard (FOR). The cloud-cleared FOR radiance spectrum is then used to retrieve profiles with a spatial resolution of approximately 45 km (Aumann et al., 2003). The atmospheric temperature profiles, water vapor profiles, surface temperatures and surface emissivity are required as inputs to compute the radiances in the CH4 absorption band. The differences between the computed radiances and the AIRS measured radiances for clear pixels or the derived cloud-cleared FOR radiances for partially cloudy pixels are used to derive CH4 profiles based on optimal estimation method. A total of 50–60 CH4 absorption channels near the 7.66 µm band are selected for the retrievals. The AIRS retrieval algorithm is a sequential retrieval method with multiple steps, in which the temperature and water vapor profiles are retrieved using appropriate channels in previous steps. Thus the quality of the CH4 retrievals depends on the whole AIRS science team's efforts in improving the temperature and moisture profiles as well as surface temperature and emissivity products. More details of AIRS CH4 retrievals in its most recent version, i.e., version 6 (V6), can be found in Xiong et al. (2015).

GOSAT was launched into a sun-synchronous orbit on 23 January 2009 by an H-IIA launch vehicle. GOSAT is on a 666 km orbit and has a 3-day revisit orbit cycle and a 12-day operation cycle. The local solar time is 13:00 ± 15 min. GOSAT carries two sensors: the TANSO-FTS and the TANSO-CAI. The IFOV of the TANSO-FTS is 10.5 km in diameter, and that of the TANSO-CAI is 0.5–1.5 km in diameter. TANSO-FTS on board GOSAT makes global observations, including both nadir and off-nadir measurements, of approximately 56 000 ground points every 3 days. TANSO-FTS consists of four spectral bands: Band 1 (0.75–0.78 µm), Band 2 (1.56–1.72 µm), Band 3 (1.92–2.08 µm), and Band 4 (5.5–14.3 µm). The spectra resolution of Band 4 is 0.2 cm-1 and its signal-to-noise ratio (SNR) averages approximately 300 at Band 4 for a blackbody temperature of 280 K. (Kuze et al., 2009, 2012; Saitoh et al., 2009). More information on TANSO-FTS TIR and its calibration can be found in Kuze et al. (2012). In the TIR retrieval algorithm version 1.0, all the channels in 7.3–8.8 µm, which include both the CH4 and N2O absorption bands, are used for CH4 retrieval. In the TANSO-FTS TIR V1.0 CH4 retrieval processing, we simultaneously retrieve H2O, N2O, O3, and temperature other than CH4. We also simultaneously derive surface temperature and surface emissivity as a correction parameter of spectral bias inherent in TANSO-FTS TIR V161.160 L1B spectra in the same manner as CO2 retrieval (Saitoh et al., 2016).The retrieval algorithm is a non-linear maximum a posteriori method with linear mapping (Rodgers, 2000). The a priori CH4 profiles used in the retrieval are taken from the National Institute for Environmental Studies (NIES) transport model (Maksyutov et al., 2008; Saeki et al., 2013). Profiles of temperature and water vapor required for the retrieval are taken from the Japan Meteorological Agency Grid Point Values (JMA-GPV) data set. Values of surface emissivities are estimated by a linear regression analysis using the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library (Baldridge et al., 2009), the information of land cover, sea ice, wind speed, and the vegetation index derived from the TANSO-Cloud and Aerosol Imager (CAI). Surface temperatures are estimated from the TANSO-FTS TIR spectra in the window region. The signal-to-noise ratios (SNR) of TANSO-FTS at around the 7–8 µm band are estimated to be 70–100, and the measurement covariance matrix used in the retrieval is based on the SNR values. The footprint of GOSAT-TANSO is 10.5 km in diameter, and the number of scan points of GOSAT in cross-track direction is five before July 2010 and three thereafter.

This study is made using the standard products of both sensors. GOSAT TANSO-FTS TIR Level 2 CH4 profile products in version 01.01 for a 2-year period from August 2010 to June 2012 are used, and the data during this period have been released to all registered users selected under the GOSAT research announcement. AIRS V6 CH4 data are used in this study, and they are downloaded from Goddard Earth Sciences Data and Information Services Center (DISC) (http://mirador.gsfc.nasa.gov/cgi-bin/mirador/presentNavigation.pl?tree=project&project=AIRS). Only the data from the ascending mode of AIRS, with quality flag equal to 0 or 1, are used for comparison with GOSAT-TIR. Note that the number of retrieval profiles from AIRS is much denser than GOSAT-TIR, as shown in Fig. 1. In this case, the number of profiles from GOSAT-TIR is 1479, while the number of the AIRS profiles from ascending node with QC = 0 and 1 is 164 355. The AIRS retrievals within 1∘ from each GOSAT-TIR measurement and in the same day were averaged to match up with each GOSAT-TIR measurement. The errors resulting from the time difference of about 4 h between AIRS and GOSAT-TIR observations were not accounted for in this study given that CH4 is a long-lived and well-mixed gas. For simplification, in the comparison of the total column CH4, the AIRS gridded products from NASA DISC in 1∘ × 1∘ were used directly, and the GOSAT-TIR data were interpolated to the same geographical grid as AIRS.

Comparisons between GOSAT-TIR and AIRS CH4 products include (1) CH4 profile comparison, (2) comparison of CH4 mixing ratios with and without using the averaging kernels, and (3) comparison of the column-averaged CH4 and the total column abundance in different latitude zones and different times. The CH4 total column abundance from AIRS ascending mode whose unit is molecules cm-2 was used. Since the unit of GOSAT-TIR CH4 profile was ppb, pressure profile P and surface pressure P0 are used to convert the unit of GOSAT-TIR CH4 profile. First, pressure gradient is calculated as ΔPi=Pi+1-Pi, where i denotes layer number; the formula to calculate Tc is Tc=∑ΔPi⋅Xi/P0-PT, where PT is the top-layer pressure. Pressure profile is included in the GOSAT-TIR product. As a GOSAT TANSO-FTS TIR CH4 profile consists of 22 layers, and AIRS-V6 CH4 profile contains 100 layers in the supporting product and 10 layers in the standard product, interpolation of AIRS CH4 profile from 100 layers to 22 layers was made using the pressure data included in both CH4 products.

Later in this paper the “differences” between GOSAT-TIR and AIRS CH4 are calculated as ΔX=XG-XAOr(XG-XA)/XA×100%, where XG denotes the CH4 mixing ratios or column amounts from GOSAT-TIR, and XA from AIRS.

Figure 2 shows a simple comparison of the GOSAT-TIR profiles and the coincident AIRS CH4 profiles from 1 day of global data on 4 September 2010. They are in a good agreement above 100 hPa and below 400 hPa, with mean difference no more than 50 ppb, but at 200–300 hPa AIRS CH4 spans a large range and tends to be smaller than GOSAT TIR on average.

The averaging kernels are defined to provide a characterization of the relationship between the retrieval and the true state. The retrieval sensitivity can be obtained from the sum of the rows of the averaging kernel matrix, which is also referred to as “the area of the averaging kernel” (Rodgers, 2000). To better demonstrate the differences in sensitivities between AIRS and GOSAT-TIR retrievals, Fig. 3 shows an example of the averaging kernels using data at a randomly selected location (38∘ S, 180∘ W) on 4 September 2010. There are 10 retrieval layers for AIRS and 22 for GOSAT-TIR. The area of the averaging kernels, which is computed as the sum of all individual kernels, from AIRS is larger than that from GOSAT-TIR, as shown in Fig. 3. To demonstrate the sensitivity variations in latitudes, Fig. 4 shows a curtain plot of the area of averaging kernels using 1 day of global data on 4 September 2010. Overall, the patterns from AIRS are similar to GOSAT TIR, with both peak sensitivities located in the 300–600 hPa range in the high latitudes and 200–600 hPa in the tropics. The sensitivities below 800 hPa are small for both, which reflects the major limitation of TIR in measuring the change of CH4 in the lower troposphere.

The information content, which is usually represented as the DOF, is computed as the trace of the averaging kernel matrix (Rodgers and Connor, 2003). Figure 5 shows the variation of DOFs at different latitudes, and on average the DOF of AIRS CH4 is approximately 1.1, whereas the mean DOF for the GOSAT-TIR retrieval of CH4 is approximately 0.61.

Since intercomparison is made between two space-based sensors, it is necessary to take account of the different characteristics of the observing systems, particularly their averaging kernels, which is usually applied to the “truth” based on the following equation (Rodgers and Connor, 2003): X^-Xc=A(X-Xc)+ϵx, where A is the averaging kernel. Here we use this equation to calculate the difference between GOSAT-TIR and AIRS CH4. So X represents the true state of CH4 profile; Xc is the mean of comparison ensemble of CH4 profile, and it can be calculated using a regression-based function of latitude and longitude; X^ is the retrieved quantity related to the true profile X and ϵx is the error. The computed value of X^ is referred to as the convolved data later in this paper, which is usually compared with the retrieved CH4 mixing ratio in validation studies. Considering that the AIRS retrieval layers are coarser than those of GOSAT-TIR, we used the AIRS averaging kernels, A, to convert the GOSAT-TIR CH4 profiles, X, and the convolved (or smoothed) GOSAT-TIR profiles (X^) are then used to derive CH4 total column for comparison. This calculation is based on the Eq. (25) from Rodgers and Connor (2003). c12^=cc+a1T(X^-Xc), where a1 is the total column averaging kernel of AIRS CH4; cc is the total column derived from Xc; and c12^ is the related total column using convolved GOSAT-TIR profiles. As the AIRS averaging kernel is a 10 by 10 matrix, the GOSAT-TIR CH4 profile and AIRS first-guess profile are interpolated onto the 10 pressure layers of the AIRS retrieval grid.

Figure 6 shows the distribution of the absolute differences between GOSAT-TIR CH4 and AIRS CH4 column mixing ratios. The upper panel gives the statistical histogram of AIRS CH4 difference to smoothed GOSAT-TIR CH4, while the lower panel shows that to unsmoothed GOSAT-TIR CH4. According to Fig. 6, the number of matched pairs with small differences increases after smoothing. A comparison of the column-averaged mixing ratio, XCH4, in Fig. 7 also shows that the correlation coefficient between AIRS and GOSAT TANSO-FTS TIR retrievals increases from 0.88 to 0.91 after using the smoothed data, and the mean difference decreases from -21.32 to -2.78 ppb. The mean difference and standard deviation between AIRS and the smoothed GOSAT-TIR data are smaller than those without smoothing using the averaging kernels, demonstrating that applying the averaging kernels helps achieve better agreements in the intercomparison between two different measurements, as suggested by Rogers and Connor (2003).

To show the impact of using averaging kernels in the intercomparison, Fig. 8 shows the scatter plot of AIRS versus GOSAT-TIR CH4 mixing ratios in four retrieval layers of 272–343, 343–441, 441–575 and 575–777 hPa. The correlation coefficients between AIRS and the smoothed GOSAT-TIR values are 0.70, 0.70, 0.79 and 0.87 in these four layers respectively, while the correlation coefficients between AIRS and GOSAT-TIR without smoothing are 0.36, 0.45, 0.57 and 0.75 respectively.

In next sections, we will focus on the comparison of the total abundance between AIRS and GOSAT-TIR retrievals without applying averaging kernel for smoothing.

As the sensitivity of TIR measurements is impacted by the surface thermal contrast and the water vapor content in the atmosphere (Deeter et al., 2007; Xiong et al., 2010b), the sensitivity varies with latitudes and seasons. Below we compare the differences between AIRS and GOSAT TANSO-FTS TIR retrieved total column abundance in six latitude zones from south to north with an interval of 30∘. As shown in Figs. 9 and 10, the correlations between AIRS and GOSAT-TIR are reasonably good, and the correlation coefficient for the least correlated case is 0.83 in zone 30–60∘ S. The split of CH4 daily comparison is due to these data being located in the high mountains between Chile and Bolivia in South America. This reflects a larger uncertainty in the mountain or coastline regions for AIRS and/or GOSAT. To show the change of their differences with time, Figs. 9 and 10 also show the monthly means of the differences from August 2010 to June 2012. In the tropics their differences are less than 1 % in all seasons, but in the mid to high latitudes in the Northern Hemisphere, GOSAT-TIR is ∼ 1–2 % lower than AIRS, with the largest bias occurring in September. At high latitudes in the Southern Hemisphere (60–90∘ S) the differences of GOSAT from AIRS show a large variation with time, i.e., from -3 % in October to +2 % in July. This large difference in the high attitudes in the Southern Hemisphere is related to the very low DOFs, particularly in GOSAT-TIR retrievals (see Fig. 5), and the large uncertainties in the retrieval of atmospheric states when there is snow/ice coverage over the ocean during October to July.

To better show the difference between GOSAT-TIR and AIRS in different latitudes, we computed the mean difference over a 2-year period from 1 August 2010 to 30 June 2012 and in each 15∘ zone. As shown in Fig. 11, the standard deviations in the Southern Hemisphere high latitudes are much larger than in the other latitudes, and the mean differences are smaller from 60∘ S to 10∘ N but increase in the Northern Hemisphere to -1.5 % at 60∘ N.

Using the monthly averaged total column density of CH4 from AIRS and GOSAT products, we compared the seasonal cycles of CH4 from 1 August 2010 to 30 June 2012. The left panels in Fig. 12 are the comparisons in the Northern Hemisphere, and the right panels are the comparisons in the Southern Hemisphere. Again, GOSAT-TIR agrees with AIRS to within 1 % in the mid-latitude regions of the Southern Hemisphere and in the tropics. However, the seasonal variation in the tropics from AIRS observations is larger than that from GOSAT. In the mid to high latitudes in the Northern Hemisphere, GOSAT-TIR is ∼ 1–2 % lower than AIRS, but the seasonal variations agree well. In the high-latitude regions in the Southern Hemisphere, the seasonal variation of the total column of CH4 is large, which is due to a lot of data points with very low total column of CH4 observed during October–January from both AIRS and GOSAT-TIR. However, AIRS and GOSAT agree well in capturing the variation even though their difference is relatively larger than in other regions (see Fig. 10).