A ground-based high-resolution Fourier transform spectrometer (FTS) station has been established in Hefei, China to remotely measure CO₂, CO and other greenhouse gases based on near-infrared solar absorption spectra. Total column measurements of atmospheric CO₂ and CO were successfully obtained from July 2014 to April 2016. The spectra collected with an InSb detector in the first year were compared with those collected by an InGaAs detector from July 2015, demonstrating that InGaAs spectra have better signal-to-noise ratios and rms of spectral fitting residuals relative to InSb spectra. Consequently, the measurement precision of the retrieved XCO₂ and XCO for InGaAs spectra is superior to InSb spectra, with about 0.04 and 0.09 % for XCO₂, and 1.07 and 2.00 % for XCO within clear-sky days respectively. Daily and monthly averages of column-averaged dry air mole fraction of CO₂ show a clear seasonal cycle, while the daily and monthly averages of XCO displayed no seasonal variation. Also, we analysed the relationship of the anomalies of XCO and XCO₂, found that the correlations are only observable for individual days, and the data under different prevailing wind conditions during the observations displayed weak correlation. The observations based on the high-resolution FTS were also compared with the temporally coinciding measurements taken with a low-resolution solar FTS instrument, the EM27/SUN. Ratioing the daily averaged XCO₂ of EM27 and FTS gives an overall calibration factor of 0.996 ± 0.001. We also compared ground-based observations from the Tsukuba TCCON station with our observations, the results showing that the variation in phase and seasonal amplitude of XCO₂ are similar to our results, but the variation of XCO in Tsukuba is quite different from our data in Hefei. To further evaluate our retrieved data, we made use of satellite measurements. The direct comparison of our observations with the Greenhouse Gases Observing Satellite (GOSAT) data shows good agreement of daily median XCO₂, with a bias of −0.52 ppm and standard deviation of 1.63 ppm. The correlation coefficient (R²) is 0.79 for daily median XCO₂ between our FTS and GOSAT observations. Daily median Orbiting Carbon Observatory 2 (OCO-2) data produce a positive bias of 0.81 ppm and standard deviation of 1.73 ppm relative to our ground-based data. Our daily median XCO₂ also show strong correlation with OCO-2 data, with correlation coefficient (R²) of 0.83. Although there were a limited number of data during the observations due to instrument downtime and adverse weather, the results confirm the suitability of the observatory for ground-based long-term measurements of greenhouse gases with high precision and accuracy, and fulfil the requirements of the Total Carbon Column Observing Network (TCCON).

Remote sensing of atmospheric hydrogen fluoride (HF) is challenging because it has weak absorption signatures in the atmosphere and is surrounded by strong absorption lines from interfering gases. In this study, we first present a multi-year time series of HF total columns over Hefei, China by using high-resolution ground-based Fourier transform infrared (FTIR) spectrometry. Both near-infrared (NIR) and mid-infrared (MIR) solar spectra suites, which are recorded following the requirements of Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC), respectively, are used to retrieve total column of HF (T_HF) and column-averaged dry-air mole fractions of HF (X_HF). The NIR and MIR observations are generally in good agreement with a correlation coefficient (R) of 0.87, but the NIR observations are found to be (6.90 ± 1.07 (1σ)) pptv, which is lower than the MIR observations. By correcting this bias, the combination of NIR and MIR observations discloses that the X_HF over Hefei showed a maximum monthly mean value of (64.05 ± 3.93) pptv in March and a minimum monthly mean value of (45.15 ± 2.93) pptv in September. The observed X_HF time series from 2015 to 2020 showed a negative trend of (−0.38 ± 0.22) % per year. The variability of X_HF is inversely correlated with the tropopause height, indicating that the variability of tropopause height is a key factor that drives the seasonal cycle of HF in the stratosphere. This study can enhance the understanding of ground-based high-resolution remote sensing techniques for atmospheric HF and its evolution in the stratosphere and contribute to forming new reliable remote sensing data for research on climate change.

Since its launch on 13 October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) mission has been measuring the solar radiation backscattered by Earth’s atmosphere and surface. In this study, we evaluate the TROPOMI operational methane (CH₄) and carbon monoxide (CO) products’ performance results covering about 3 years using the only two global Total Carbon Column Observing Network (TCCON) sites in China, i.e., the Hefei site and the Xianghe site. These two sites have recently joined the TCCON, and this study uses the both sites simultaneously to validate the TROPOMI products over China for the first time. We found that the systematic bias with rescaling between the TROPOMI CO products and the Hefei site is on average 1.78 ± 6.35 ppb or 1.18 ± 5.35%. The systematic bias with rescaling between the TROPOMI CO products and the Xianghe site is on average 5.33 ± 14.24 ppb or 3.85 ± 10.30%. Both the stations show a correlation above 0.9. The TROPOMI CO data are systematically higher than the two TCCON sites measurements in China. We found that the systematic bias with rescaling between the TROPOMI CH₄ products and the Hefei site is on average −4.13 ± 11.65 ppb or −0.22 ± 0.62%. The systematic bias between the TROPOMI CH₄ products and Xianghe site is on average −7.25 ± 10.72 ppb or −0.39 ± 0.57%. Both the stations show a correlation above 0.9. The TROPOMI CH₄ data are systematically lower than the two TCCON sites measurements in China. We found that the bias between the TROPOMI and the two sites’ data as a function of the coincident radius around the two sites is mostly affected by localized emissions for both CO and CH₄. We also observe a CO decreasing trend and a CH₄ increasing trend in the year-to-year relative changes from 2019 to 2021. Validating against reference from Hefei and Xianghe TCCON site demonstrates the high quality of TROPOMI CO and CH₄ data over China.

Synthetic halogenated organic chlorofluorocarbons (CFCs) play an important role in stratospheric ozone depletion and contribute significantly to the greenhouse effect. In this work, the mid-infrared solar spectra measured by ground-based high-resolution Fourier transform infrared spectroscopy (FTIR) were used to retrieve atmospheric CFC-11 (CCl₃F) and CFC-12 (CCl₂F₂) at Hefei, China. The CFC-11 columns observed from January 2017 to December 2020 and CFC-12 columns from September 2015 to December 2020 show a similar annual decreasing trend and seasonal cycle, with an annual rate of $-\mathrm{0.47}\pm \mathrm{0.06}$ % yr⁻¹ and $-\mathrm{0.68}\pm \mathrm{0.03}$ % yr⁻¹, respectively. So the decline rate of CFC-11 is significantly lower than that of CFC-12. CFC-11 total columns were higher in summer, and CFC-12 total columns were higher in summer and autumn. Both CFC-11 and CFC-12 total columns reached the lowest in spring. Further, FTIR data of NDACC (Network for the Detection of Atmospheric Composition Change) candidate station Hefei were compared with the ACE-FTS (Atmospheric Chemistry Experiment Fourier transform spectrometer) satellite data, WACCM (Whole Atmosphere Community Climate Model) data, and the data from other NDACC-IRWG (InfraRed Working Group) stations (St. Petersburg, Jungfraujoch, and Réunion). The mean relative difference between the vertical profiles observed by FTIR and ACE-FTS is $-\mathrm{5.6}\pm \mathrm{3.3}$ % and 4.8±0.9 % for CFC-11 and CFC-12 for an altitude of 5.5 to 17.5 km, respectively. The results demonstrate that our FTIR data agree relatively well with the ACE-FTS satellite data. The annual decreasing rate of CFC-11 measured from ACE-FTS and calculated by WACCM is $-\mathrm{1.15}\pm \mathrm{0.22}$ % yr⁻¹ and $-\mathrm{1.68}\pm \mathrm{0.18}$ % yr⁻¹, respectively. The interannual decreasing rates of atmospheric CFC-11 obtained from ACE-FTS and WACCM data are higher than that from FTIR observations. Also, the annual decreasing rate of CFC-12 from ACE-FTS and WACCM is $-\mathrm{0.85}\pm \mathrm{0.15}$ % yr⁻¹ and $-\mathrm{0.81}\pm \mathrm{0.05}$ % yr⁻¹, respectively, close to the corresponding values from the FTIR measurements. The total columns of CFC-11 and CFC-12 at the Hefei and St. Petersburg stations are significantly higher than those at the Jungfraujoch and Réunion (Maïdo) stations, and the two values reached the maximum in local summer or autumn and the minimum in local spring or winter at the four stations. The seasonal variability at the three stations in the Northern Hemisphere is higher than that at the station in the Southern Hemisphere.

As an important greenhouse gas (GHG) in the atmosphere, Carbon dioxide (CO ₂ ) has a great impact on global climate change. Accurate knowledge of the spatio-temporal variations of CO ₂ is of great significance for understanding the carbon cycle and evaluating the effectiveness of carbon emission reduction. In recent years, several satellites with CO ₂ sensors have been launched and a series of atmospheric CO ₂ concentration products have been developed using different retrieval algorithms. This study validated nine satellite XCO ₂ products derived from Greenhouse gases Observing SATellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory-2 (OCO-2) and OCO-3: including ACOS-GOSAT, NIES-GOSAT, BESD-GOSAT, OCFP-GOSAT, SRFP-GOSAT, EMMA, GOSAT-2, OCO-2, and OCO-3 XCO ₂ . The remotely sensed XCO ₂ products were compared with the XCO ₂ observations from six Total Carbon Column Observing Network (TCCON) stations in East Asia for validation. The results showed that the OCO-2 XCO ₂ product outperformed other products, with the highest R ² of 0.94 and the lowest MAE of 1.24 ppm. The ACOS-GOSAT and EMMA-GOSAT XCO ₂ products also showed favorable accuracies, both achieving the R ² of 0.93 and corresponding MAE values of 1.29 and 1.31 ppm, respectively. The GOSAT-2 XCO ₂ product showed the poorest accuracy, with an R ² of 0.77 and an MAE of 3.28 ppm. There was a significant overestimation of the bias-uncorrected GOSAT-2 XCO ₂ product in East Asia, and it indicated that bias correction must be performed for this XCO ₂ product. The accuracy of TCCON XCO ₂ was not consistent with remotely sensed XCO ₂ at different stations. The RJ, JS, AN, and TK TCCON stations generally showed better agreements between satellite estimates and TCCON observations, except for the GOSAT-2 XCO ₂ product.

High resolution Fourier transform infrared (FTIR) measurement of direct sunlight does not only provide information of trace gas total columns, but also vertical distribution. Measured O₃, CO, CH₄, and N₂O can be separated into multiple partial columns using the optimal estimation method (OEM). The retrieval of trace gas profiles is sensitive to the instrument line shape (ILS) of the FTIR spectrometer. In this paper, we present an investigation of the influence of ILS degradation on the partial column retrieval of O₃, CO, CH₄, and N₂O. Sensitivities of the partial column, error, and degrees of freedom (DOFs) of each layer to different levels of ILS degradation for O₃, CO, CH₄, and N₂O are estimated. We then evaluate the impact of ILS degradation on the long-term measurements. In addition, we derive the range of ILS degradation corresponding to the acceptable uncertainties of O₃, CO, CH₄, and N₂O results. The results show that the uncertainties induced by the ILS degradation on the absolute value, error, and the DOFs of the partial column are altitude and gas species dependent. The uncertainties of the partial columns of O₃ and CO are larger than those on CH₄ and N₂O. The stratospheric partial columns are more sensitive to the ILS degradation compared to the tropospheric part. Our result improves the understanding of the ILS degradation on the FTIR measurements, which is important for the quantification of the measurement uncertainties and minimizes the bias of the inter-comparison between different measurement platforms. This is especially useful for the validation of satellite observations, the data assimilation of chemical model simulations, and the quantification of the source/sink/trend from the FTIR measurements.