TanSat is the 1st Chinese carbon dioxide (CO₂) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL‐FP) algorithm is implemented for TanSat nadir mode XCO₂retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8^thorder Fourier series. The spectrum‐correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O₂A band retrieval. Accordingly, we extend the previous TanSat single CO₂weak band retrieval to a combined O₂A and CO₂weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post‐screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO₂retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO₂retrieval. We show that our new approach produces a significant improvement on the XCO₂retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of −0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO₂retrieval from TanSat and subsequently the Level‐2 data production, and hence will be applied in the TanSat operational XCO₂processing.

Black carbon (BC) is a major light absorption material that acts as a climate change driver with high radiative forcing and as an air pollutant that reduces visibility and air quality. Thus, reducing the emission and ambient concentration of BC could help address climate change and improve air quality simultaneously. In this study, the mass concentration of atmospheric BC was continuously measured by an aethalometer in Shanghai in 2017. The annual BC concentration was 2.19 ± 1.28 μg/m³, with the highest loading in winter and the lowest loading in autumn. The BC concentrations varied with year and location when compared with previous studies in different locations in Shanghai. The hourly BC concentration had a bimodal distribution, with two peaks during the morning and evening traffic rush hours. Liquid fuels, biomass, and coal combustion contributed 65.7%, 21.5%, and 12.8%, respectively, of the total BC based on the advanced aethalometer model. The three sources varied in different seasons with a high contribution of liquid source in summer and more coal and biomass emissions in winter. High BC concentrations accumulated in the stable weather conditions in the four seasons and appeared when there were high wind speeds from the northwestern direction in winter. The Yangtze River Delta region was the most likely potential source region of high BC loading in the four seasons, and long‐range transport from North China in winter was another likely source region based on the results of cluster analysis and potential source contribution function analysis of backward trajectories.

High spatial resolution carbon dioxide (CO₂) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO₂ flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO₂ (XCO₂) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO₂ flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr⁻¹) and compared with the corresponding posterior CO₂ fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr⁻¹, standard deviation: 0.29 Pg C yr⁻¹, 9 models in total) and CT2017 (1.19 Pg C yr⁻¹). The uncertainty of the inverted CO₂ flux was reduced by 14.71% compared to the prior flux. The annual mean XCO₂ estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO₂ flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO₂ flux and flux measurements was 0.73 g C m⁻² d⁻¹, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO₂ concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO₂ emission hotspots indicated by the inverted annual CO₂ flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO₂ flux inversion system in supporting the global stocktake.

TanSat is the 1st Chinese carbon dioxide (CO) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8 order Fourier series. The model and a priori is developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O A band retrieval. Accordingly, we extend the previous TanSat single CO weak band retrieval to a combined O A and CO weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the stronger correlation with the XCO retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO retrieval. We show that our new approach produces a significant improvement on the XCO retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of -0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO processing.

Aerosols significantly affect carbon dioxide (CO₂) retrieval accuracy and precision by modifying the light path. Hyperspectral measurements in the near infrared and shortwave infrared (NIR/SWIR) bands from the generation of new greenhouse gas satellites (e.g., the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite, TanSat) contain aerosol information for correction of scattering effects in the retrieval. Herein, a new approach is proposed for optimizing the aerosol model used in the TanSat CO₂ retrieval algorithm to reduce CO₂ uncertainties associated with aerosols. The weighting functions of hyperspectral observations with respect to elements in the state vector are simulated by a forward radiative transfer model. Using the optimal estimation method (OEM), the information content and each component of the CO₂ column-averaged dry-air mole fraction (XCO₂) retrieval errors from the TanSat simulations are calculated for typical aerosols which are described by Aerosol Robotic Network (AERONET) inversion products at selected sites based on the a priori and measurement assumptions. The results indicate that the size distribution parameters (r_eff, v_eff), real refractive index coefficient of fine mode (a_r^f) and fine mode fraction (fmf) dominate the interference errors, with each causing 0.2–0.8 ppm of XCO₂ errors. Given that only 4–7 degrees of freedom for signal (DFS) of aerosols can be obtained simultaneously and CO₂ information decreases as more aerosol parameters are retrieved, four to seven aerosol parameters are suggested as the most appropriate for inclusion in CO₂ retrieval. Focusing on only aerosol-induced XCO₂ errors, forward model parameter errors, rather than interference errors, are dominant. A comparison of these errors across different aerosol parameter combination groups reveals that fewer aerosol-induced XCO₂ errors are found when retrieving seven aerosol parameters. Therefore, the model selected as the optimal aerosol model includes aerosol optical depth (AOD), peak height of aerosol profile (H_p), width of aerosol profile (H_w), effective variance of fine mode aerosol (v_eff^f), effective radius of coarse mode aerosol (r_eff^c), coefficient a of the real part of the refractive index for the fine mode and coarse mode (a_r^f and a_r^c), with the lowest error of less than 1.7 ppm for all aerosol and surface types. For marine aerosols, only five parameters (AOD, H_p, H_w, r_eff^c and a_r^c) are recommended for the low aerosol information. This optimal aerosol model therefore offers a theoretical foundation for improving CO₂ retrieval precision from real TanSat observations in the future.