Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm

. The National Institute for Environmental Studies has provided the column-averaged dry-air mole fraction of carbon dioxide and methane (XCO 2 and XCH 4 ) products (L2 products) obtained from the Greenhouse gases Ob-serving SATellite (GOSAT) for more than a decade. Recently, we updated the retrieval algorithm used to produce the new L2 product, V03.00. The main changes from the previous version (V02) of the retrieval algorithm are the treat-ment of cirrus clouds, the degradation model of the Thermal And Near-infrared Spectrometer for carbon Observation– Fourier Transform Spectrometer (TANSO–FTS), solar irradiance spectra, and gas absorption coefﬁcient tables. The retrieval results from the updated algorithm showed improvements in ﬁtting accuracies in the O 2 A, weak CO 2 , and CH 4 bands of TANSO–FTS, although the residuals increase in the strong CO 2 band over the ocean. The direct comparison of the new product obtained from the updated (V03) algorithm with the previous version V02.90/91 and the validations us-ing the Total Carbon Column Observing Network revealed that the V03 algorithm increases the amount of data without diminishing the data qualities of XCO 2 and XCH 4 over land. However, the negative bias of XCO 2 is larger than that of the previous version over the ocean, and bias correction is still necessary. Additionally, the V03 algorithm resolves the underestimation of the XCO 2 growth rate compared with the in situ measurements over the ocean recently found using V02.90/91 and V02.95/96.


Solar irradiance spectra
The solar irradiance spectra used in the V02 algorithm were created using the baseline estimated from the report by Dr. R.
Kurucz and the Fraunhofer lines personally provided by Dr. G. C. Toon (Yoshida et al., 2013). The baseline and Fraunhofer lines were updated in V03. The baseline was estimated using the Total and Spectral Solar Irradiance Sensor-1 Hybrid Solar 130 Reference Spectrum (TSIS-1 HSRS; Coddington et al., 2021). Fraunhofer lines were obtained from version 2016 of Toon (2015b).

Gas absorption coefficient database
In the radiative transfer calculation of retrieval processing, gas absorption coefficients are obtained by interpolating look-up tables (LUTs) as the functions of temperature, pressure, and wavenumber. The LUTs are created using several databases, and 135 the referenced databases were updated (Table 1.)  found that the CH4 retrieval using HITRAN2008 depends on the solar zenith angle. In the V02 retrievals, the residuals at several H2O absorption lines increase with increasing water vapor because of the large uncertainties in spectroscopic parameters of H2O. These problems can be resolved or mitigated by the updates. Associated with this update of LUTs, the scaling factor for O2 absorption (see Section 2.3 of Yoshida et al., 2013 for details) is updated to 0.99556. Owing to the updates, the gas absorption coefficient database used in 140 V03 retrievals is common to that used in the NIES SWIR L2 retrieval algorithm for TANSO-FTS-2 on GOSAT-2.

Other changes
In the NIES retrieval algorithm, the empirical noise model was estimated as the quadratic function of the signal-to-noise ratio to define the error covariance matrix (Yoshida et al., 2013). The coefficients of the functions in the V03 algorithm were updated due to the abovementioned changes. The empirical noise is not applied to the H2O sub-band. 145 Post-screening is applied to the result after the retrievals, and one of the screening items is the spectral residual. The retrieval results with the mean squared of the residuals normalized with spectral noise larger than the thresholds are screened and not included in the L2 product. The thresholds were re-evaluated as 1.2, 1.2, 1.2, and 1.3 for the O2 A, WCO2, CH4, and SCO2 sub-bands, respectively. The threshold is undefined for the H2O sub-band due to its large variability in water vapor concentrations. 150 Tables 2 and 3 summarize the retrieval setup for the V03 algorithm and the pre/post-screening procedures for the V02 and V03 algorithms.
https://doi.org/10.5194/amt-2022-281 Preprint. Discussion started: 8 November 2022 c Author(s) 2022. CC BY 4.0 License. Figure 1 shows the spectral residuals at each sub-band obtained in April 2009 and April 2020 over land from V02.90. These 155 are differences between the simulated radiance spectra using posterior states and the observed spectra. In each sub-band presented in the figure, the residuals exhibit some spectral dependencies. In the O2 A sub-band, the residuals at the edges of the sub-band are larger than those in the central region and the structures of the O2 absorption are seen. In the WCO2 and CH4 sub-bands, the residuals have relatively fine structures related to the gas absorptions, though those at the edges and in the center are flattened. Figure 2 shows the spectral residuals same as Fig. 1, except that V03.00 is used. Compared with Fig.  160 1, the wavenumber dependencies of the residuals are decreased and the retrievals seem to be well fitted in Fig. 2. Same figures over the ocean are shown in Fig. 3 and 4. Same as over land, the fitting accuracies of V03.00 are found to be better than those of V02.90 in the O2 A, WCO2, and CH4 sub-bands. However, in the SCO2 sub-band, the residual has a significant spectral dependency, and it corresponds to the CO2 absorption structure. The root mean squares of the averaged spectral residuals in April 2020 shown in the figures are summarized in Table 4. The values from V03.00 are lower than the values 165 from V02.90/91 and the spectral fitting accuracies are improved except for the SCO2 sub-band over the ocean.

Spectral fitting accuracy
The abovementioned differences in spectral residuals between V02.90 and V03.00 are mainly owing to the update of solar irradiance and gas absorption cross-section database. This is because the treatment of clouds has a smaller impact on the fine structure of the residuals, and there are slight spectral dependencies of differences between the new and old degradation models shortly after the launch. The update of solar irradiance decreased the relatively large wavenumber dependencies, 170 such as the large residuals around 6,375 cm −1 and the large wavenumber dependency around 6,000 cm −1 shown in Fig. 3.
Updating the gas absorption cross-section database significantly improves the fitting accuracy in the CH4 sub-band and substantially decreases the fine structure of the residuals. The O2 A sub-band is flattened, and the differences between the center and edges of the sub-band are decreased by both the updates of solar irradiance and gas absorption coefficients.
However, in the O2 A sub-band, some differences between 2009 and 2020 remain. One possible reason of this is the 175 degradation model. The number of components used to construct the degradation model in the O2 A sub-band is smaller than the other band because the contributions of the primary components are large. The temporal differences are possibly due to the contributions by the other components which are not considered in the construction of the degradation model. Figure 4 shows the significant spectral dependencies of the residuals obtained from V03.00 in the SCO2 sub-band over the ocean. In this figure, the baselines of the simulated radiance spectra seem to have some biases. Over the ocean, the surface 180 state is described only by the surface wind speed in the retrieval and the spectral baseline is not adjusted (unlike that in over land). The spectral structure corresponding to CO2 absorption is found in this figure. This can be result of changes in retrieved CO2 to reduce residuals due to baseline bias. This can lead to a bias in the retrieved XCO2.

Global distribution of the retrieval results
In this section, we show the difference in the retrieval results between V02.90/91 and V03.00. The data from the launch to 185 2021 are used for both the versions. Global distributions of the retrieved XCO2, XCH4, and the number of observations for V02.90/91, V03.00, and their differences are shown in Fig. 5. The XCO2 from V03.00 over land is approximately the same as that from V02.90/91. Conversely, over the ocean, the XCO2 from V03.00 is several ppm lower than that from V02.90/91. This difference arises due to the spectral residual in the SCO2 sub-band mentioned in Section 4.1.
The XCH4 from V03.00 is lower than that from V02.90/91 globally. It is largely decreased in the middle and low latitudinal 190 areas. Although it is difficult to isolate the impacts of each update on the retrieval results, our sensitivity test revealed that the XCH4 over land changed by approximately 7 ppb depending on whether solar irradiance spectra are updated or not. On the other hand, the other test with the replacement of the gas absorption table shows smaller changes in XCH4 over land.
These may indicate that the decrease in XCH4 is mainly because of the update of the solar irradiance spectra.
The number of observations over land is increased significantly because the 2 µm cloud screening is not applied in V03 195 retrievals. Because the XCO2 values over land from V02.90/91 and V03.00 have only slight differences, the addition of the cirrus cloud parameters is effective to increase the number of observations. However, the number of observations over the ocean is decreased, except in the intertropical convergence zone, where cirrus clouds frequently exist because the residuals in the SCO2 sub-band are increased, and more observations are filtered through the post-screening process in the V03.00 retrieval. The numbers of observations from the V02.90/91 and V03.00 XCO2 products are shown in Table 5. The V03.00 200 product increases the number of observations obtained over land and the mixed surface of land and ocean, by 12.7% and 22.3% compared with the V02.90/91 product, respectively. In opposite, it decreases by 20.3% over the ocean. Overall, the number of available observations from V03.00 is 2.3% larger than that from V02.90/91. Figure 6 shows the global distributions of the ancillary parameters, the difference between the retrieved and a priori surface pressures (∆Ps), retrieved temperature shift, large-particle AOT, and the COT from V02.90/91 and V03.00. These results are 205 obtained only from the observations that passed the post-screening process those with large AOT and COT (>0.1) are excluded. The general ∆Ps patterns are similar for V02.90/91 and V03.00. Over land, negative biases are slightly improved in V03.00. Over the ocean, positive biases are large in the high latitudes of the southern hemisphere for V02.90/91 and low latitudes for V03.00. The horizontal pattern of ∆Ps over land in the middle and low latitudes seems to correspond to that of the difference in XCH4 shown in Fig. 5. The relatively large decrease in XCH4 in low latitudes over the ocean could be 210 attributed to the changes in ∆Ps. Negative biases of temperature shift decreased globally for V03.00, and those over the ocean for V02.90/91 changed to slightly positive biases. Although the relatively large negative biases remain in inland China for V03.00, those in Europe and America for V02.90/91 become smaller for V03.00. The AOT of large particles at 1.6 µm decreased globally, especially over the ocean for V03.00. The COT is obtainable only for V03.00. Although the observations with large COT values are rejected by post-screening, the relatively large values are seen in the tropical regions, where cirrus

Comparison with TCCON measurements
The retrieved XCO2 and XCH4 are validated using the TCCON measurements in this section. The TCCON sites used in this study are listed in Table A1. The GOSAT measurements used for the comparisons are selected within ±2° from each TCCON site. The TCCON measurements within ±30 min from the GOSAT measurement time are averaged for comparison. 220 We used the data from the launch to 2021. Currently, the newest TCCON product, version GGG2020, is provided and we used this version in this analysis. However, not all sites have produced their full GGG2020 time series at the time of writing.
The main changes between GGG2020 and the previous version, GGG2014, are found on the TCCON wiki page (https://tccon-wiki.caltech.edu/Main/DataDescriptionGGG2020)). The data amount of GGG2020 is currently smaller than that of GGG2014 because of stricter quality control processes, but much of these data should be recovered in the near future. 225 In particular, measurements collected before 2011 are sparse.
The comparison results for V03.00 and V02.90/91 versus TCCON are shown in Table 6. Bias means the average of the differences between GOSAT and TCCON, and the standard deviations are calculated from these differences. The GOSAT measurements are categorized according to the surface state and the gain (high: H or middle: M) setting of the FTS measurement. The observations containing both the land and ocean surfaces in the instantaneous field of views of TANSO-230 FTS are not used here. The number of observations with gain H from V03.00 is larger than that from V02.90/91 over land.
On the other hand, those with gain M from V03.00 are slightly smaller than those from V02.90/91. The sites used for gain M are only two sites, Pasadena and JPL which are very close to each other and located near Los Angels. Over the ocean, the number of observations from V03.00 decreases. There are no substantial changes in the standard deviations of the differences for XCO2 and XCH4 in all the situations, although the biases are different between V03.00 and V02.90/91 in 235 some cases.
The biases and standard deviations of the XCO2 from V03.00 are close to those from V02.90/91 over land. Considering these results, the XCO2 from V03.00 has similar qualities as that from V02.90/91 over land. Meanwhile, the bias of the XCO2 from V3.00 is larger and more negative than that from V02.90/91 over the ocean. This issue is consistent with the results presented in Section 4.2 and is because of the fitting accuracy shown in Section 4.1. Therefore, the bias correction 240 seems necessary for the XCO2 from V03.00 over the ocean.
As shown in Section 4.2, the XCH4 from V03.00 decreased from those from V02.90/91. Over land, the absolute values of the XCH4 from V03.00 are slightly larger with gain H and significantly smaller with gain M than those from V02.90/91.
Over the ocean, the bias from V03.00 is larger, although a smaller data amount is available. Therefore, we need to investigate the biases over the ocean with a larger amount of data in the future. 245 The validation results over land with gain H in the stricter match-up condition of ±0.1° are shown in Table 7 to investigate these differences more precisely. Because of the spatial variability of GHGs, the validation with the stricter condition is more reliable, especially for XCH4. Unfortunately, there are no match-up data found over land with gain M and over the ocean in this match-up condition. In this table, the absolute values of bias and standard deviation of the XCH4 from V03.00 are https://doi.org/10.5194/amt-2022-281 Preprint. Discussion started: 8 November 2022 c Author(s) 2022. CC BY 4.0 License. smaller than those from V02.90/91. Therefore, the quality of the XCH4 from V03.00 can be regarded as almost the same as 250 or better than those from V02.90/91.
Inter-site and temporal variability of the differences between GOSAT and TCCON are investigated using the match-up condition of ±0.1°. The data with more than 10 match-up observations were used for both the investigations of inter-site and temporal variability. 10 TCCON sites (Burgos, Caltech, JPL02, Lauder02, Lauder03, Lamont, Paris, Saga, Sodankyla, and Tsukuba) were found as the match-up data sites for investigating inter-site variability Site biases, average site bias, and site-255 to-site variability were calculated as the mean differences from TCCON for individual sites, an average of site biases, and a standard deviation of site biases, respectively. The average site biases and the site-to-site variabilities from V03.00 are −0.01 and 1.74 ppm for XCO2 and −2.14 and 9.33 ppb for XCH4, respectively. Those from V02.90/91 are −0.02 and 1.72 ppm for XCO2 and 5.99 and 9.12 ppb for XCH4. The average site biases and the site-to-site variabilities of XCO2 are similar for V03.00 and V02.90/91. For XCH4, although the site-to-site variability from V03.00 is slightly higher than that from 260 V02.90/91, the average site bias is smaller in V03.00. Temporal variability was calculated from the annual mean of the differences between GOSAT and TCCON. The time series of the annual mean differences are shown in

Evaluating the long-term trend using in situ measurements
The TCCON sites used in the previous section were mainly obtained over land. However, as noted in Section 2.2, there is an issue with the decadal growth rate of XCO2 estimated using the V02.90/91 product over the ocean. In this section, we 270 evaluate the long-term trends of XCO2 using in situ measurement data.
NIES has observed CO2 via air sampling on ships (Tohjima et al., 2005), and at ground stations (Nomura et al., 2017;2021) in southwestern Asia and the western Pacific Ocean for more than a decade. CO2 in the upper troposphere has been observed by aircraft in the CONTRAIL project (Machida et al., 2008). In addition, NOAA Global Monitoring Laboratory has provided flask sampling and in situ measurement data on the western Pacific islands (Conway et al., 1994;Lan et al., 2022). 275 The data used in this study are listed in Table A2. The products from these in situ measurements are appropriate to evaluate the GOSAT product in terms of the stability of data accuracy. Because these observations obtain the concentrations of the trace gases at the surfaces or at certain atmospheric levels, that are not column-averaged, they are not directly comparable with the XCO2 obtained from GOSAT. Therefore, we only focus on the decadal increasing trend of CO2 from both products in this study. Further, we only evaluate the CO2 trends because the comparison of CH4 is more complicated due to its large 280 vertical gradient and variability. For aircraft measurement, only the data obtained at altitudes of 5 km or higher were used.
The 22 areas are defined using 12° × 12° grid boxes and the CO2 concentrations obtained from GOSAT and each in situ https://doi.org/10.5194/amt-2022-281 Preprint. Discussion started: 8 November 2022 c Author(s) 2022. CC BY 4.0 License. measurement platform were monthly averaged in each area for comparison. The locations of the in situ measurements and areas used in this analysis are depicted in Fig. 8. Figure 9 shows the time series of the differences between the XCO2 from the GOSAT V02.90/91 or V03.00 product and 285 CO2 concentration from each in situ measurement platform. Over land, the growth rates of CO2 estimated from the GOSAT V02.90/91 and V03.00 products are consistent with that from the in situ measurements within 1 ppm/decade. This value is close to the difference between TCCON and the in situ measurements. On the other hand, the growth rate for V02.90/01 over the ocean is 1.7 ppm/decade smaller than that from the in situ measurements. However, the difference in the growth rate for V03.00 is improved to 0.0 ppm/decade although the biases are negatively large as shown in the previous sections. 290 The main cause of this trend of the GOSAT V02.90/91 product over the ocean is estimated as the sensitivity degradation of TANSO-FTS. Although the degradation is considered in the V02 algorithm with the degradation model according to Yoshida et al. (2012), the degradations after 2012 are the expected ones. The error of this degradation model generates a gap in the spectral baseline between the observed and simulated spectra. The difference in trend is not significant over land because the gap can be adjusted by simultaneously retrieving surface albedo. In the NIES retrieval algorithm, only the wind 295 speed is retrieved as a surface property over the ocean and not surface albedo. Therefore, the difference in the trend of CO2 between GOSAT V02.90/91 and the in situ measurements could have resulted from the increasing error of the degradation model with time. This improvement of the trend of V03.00 over the ocean is mainly because of the update of the degradation model described in Section 3.2 as the other updates do not vary over time.

Bias correction 300
Because the V03.00 product has biases particularly for XCO2 over the ocean, as shown in the previous sections, those should be corrected. We used TCCON GGG2014 for this bias correction because insignificant changes were found in XCO2 between both versions and the available amount of data is larger than GGG2020. The site information of TCCON GGG2014 used in this study is listed in Table A3. The bias correction for XCH4 is not processed here since those are largely changed between GGG2014 and GGG2020. Since the GGG2020 is not fully available, we plan to correct XCH4 based on GGG2020 305 after more stations are published. The bias correction strategy is the same as that used in the V02.95/96 products (NIES GOSAT project, 2020). The bias correction of the XCO2 for V03 is a function of AOT, ∆Ps, and surface albedo at the O2 A sub-band. Multiple linear regression analysis was used to estimate the coefficients. The TCCON data from 2009 to 2019 is used as the reference data. The changes in the XCO2 from V03.00 after the correction are shown in Fig. 10. Only minor differences are found over land. The negative bias over the ocean revealed in the previous sections is corrected by this 310 procedure. The mean changes of the XCO2 by the bias correction are +0.55 ppm over land and +6.31 ppm over the ocean.
The bias-corrected version of the XCO2 product plans to be released as V03.05.

Summary and conclusions
The retrieval algorithm for the GOSAT TANSO-FTS SWIR L2 product from NIES was updated to generate the next version, the V03 product. The main changes in the V03 algorithm compared with the current retrieval algorithm (V02) are as follows: 315 1. COT and CTP are retrieved simultaneously with the GHGs instead of the cirrus cloud screening using the 2µm band in the pre-screening processing 2. The degradation model of TANSO-FTS is updated to that of Someya and Yoshida (2020) 3. Solar irradiance spectra are updated to those produced from TSIS-1 HSRS and the version 2016 of Toon (2015b) 4. Gas absorption coefficient tables are updated using several new references 320 The retrieval results show that the spectral fitting accuracies are successfully improved, and the systematic spectral residuals in the V02.90/91 product are reduced in the O2 A, WCO2, and CH4 sub-bands. Conversely, the residual in the SCO2 sub-band increases over the ocean with a systematic spectral structure corresponding to the CO2 absorptions. This increase in the residual is mainly attributed to a gap in the spectral baseline between observed and simulated spectra.
The amount of data from V03.00 is larger than that from V02.90/91 over land and the mixed surfaces mainly owing to the 325 change in the treatment of clouds, although it is smaller over the ocean because of the residual in the SCO2 sub-band. Overall, the amount of data from V03.00 increased by 2.3% compared with that from V02.90/91. The direct comparison of V03.00 with V02.90/91 and the validation using TCCON measurements shows that the quality of XCO2 from V03.00 is almost the same level as that from V02.90/91 over land-the update achieves an increase in the available data without reducing the quality of the retrieved XCO2. On the other hand, the XCO2 from V03.00 over the ocean 330 is negatively biased and the bias correction is necessary. Although the bias XCH4 over land with gain H from V03.00 is slightly larger than that from V02.90/91 in the match-up condition of ±2°, it is smaller in the stricter condition, ±0.1°.
Regarding the spatial variability in CH4, the results obtained with the stricter match-up condition are more reliable, and V03.00 improves the quality of XCH4. The standard deviations of the XCH4 differences between GOSAT and TCCON are similar for V02.90/91 and V03.00. Considering these validation results and the improvement in fitting accuracies, the quality 335 of the XCH4 from V03.00 is comparable to or better than that from V02.90/91. In addition, the investigation of site-to-site and temporal variability of XCO2 and XCH4 biases from V03.00 demonstrates that their site-to-site variabilities are the approximately same level as, and the temporal variabilities are slightly smaller than those from V02.90/91. The long-term trends of XCO2 from both product versions are evaluated via in situ measurements. The V03.00 product resolves the issue that the decadal CO2 growth rate estimated from the V02.90/91 products over the ocean is 1.7 ppm/decade 340 lower than that from the in situ measurements.
Although the V03 retrieval algorithm has an issue to be resolved for XCO2 over the ocean, the objectives of the update, increase in data, and improvement of the fitting accuracy are generally achieved over land. Notably, the increase in data of