Retrievals of atmospheric CO2 from simulated space-borne measurements of backscattered near-infrared sunlight: accounting for aerosol effects

Application and Analysis of XCO2 Data from OCO Satellite Using a Synthetic DINEOF–BME Spatiotemporal Interpolation Framework

Carbon dioxide (CO2) is one of the main greenhouse gases leading to global warming, and the ocean is the largest carbon reservoir on the earth that plays an important role in regulating CO2 concentration on a global scale. The column-averaged dry-air mole fraction of atmospheric CO2 (XCO2) is a key parameter in describing ocean carbon content. In this paper, the Data Interpolation Empirical Orthogonal Function (DINEOF) and the Bayesian Maximum Entropy (BME) methods are combined to interpolate XCO2 data of Orbiting Carbon Observatory 2 (OCO-2) and Orbiting Carbon Observatory 3 (OCO-3) from January to December 2020 occurring within the geographical range of 15–45°N and 120–150°E. At the first stage of our proposed analysis, spatiotemporal information was used by the DINEOF method to perform XCO2 interpolation that improved data coverage; at the second stage, the DINEOF-generated interpolation results were regarded as soft data and were subsequently assimilated using the BME method to obtain improved XCO2 interpolation values. The performance of the synthetic DINEOF–BME interpolation method was evaluated by means of a five-fold cross-validation method. The results showed that the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Bias of the DINEOF-based OCO-2 and OCO-3 interpolations were 2.106 ppm, 3.046 ppm, and 1.035 ppm, respectively. On the other hand, the MAE, RMSE, and Bias of the cross-validation results obtained by the DINEOF–BME were 1.285 ppm, 2.422 ppm, and −0.085 ppm, respectively, i.e., smaller than the results obtained by DINEOF. In addition, based on the in situ measured XCO2 data provided by the Total Carbon Column Observing Network (TCCON), the original OCO-2 and OCO-3 data were combined and compared with the interpolated products of the synthetic DINEOF–BME framework. The accuracy of the original OCO-2 and OCO-3 products is lower than the DINEOF–BME-generated XCO2 products in terms of MAE (1.751 ppm vs. 2.616 ppm), RMSE (2.877 ppm vs. 3.566 ppm) and Bias (1.379 ppm vs 1.622 ppm), the spatiotemporal coverage of XCO2 product also improved dramatically from 16% to 100%. Lastly, this study demonstrated the feasibility of the synthetic DINEOF–BME approach for XCO2 interpolation purposes and the ability of the BME method to be successfully combined with other techniques.

Study of mid-latitude retrieval XCO2 greenhouse gas: Validation of satellite-based shortwave infrared spectroscopy with ground-based TCCON observations

Carbon dioxide (CO₂) is a major greenhouse gas. This study investigated the performance of three common algorithms, namely NIES, ACOS, and Remo Tec (SRFP). These algorithms were compared using GOSAT observation satellite data with reference data obtained from TCCON during the period 2009-2021. According to statistical evaluation, the SRFP and NIES algorithms achieved the lowest and highest correlation values of the 13 year (2009_2021) average of all sites, respectively. The average bias error values of NIES and ACOS was estimated to be less than that of SRFP approximately 0.5 ppm, while the bias within SRFP was of about 2 ppm. Comparing the RMSE and CRMS error values showed that the highest and lowest error values were related to the SRFP and NIES algorithms respectively, which were 0.37-1.67 and ppm 1.46-7.9. The researchers also compared them with monthly time changes based on ground measurements, and observed a time series of CO₂ concentration changes that well matched the trend of gas concentration values at ground stations obtained by NIES algorithm. The results showed that in most cases NIES was an effective algorithm to retrieve carbon dioxide gas concentrations, allowing the researchers to identify the main sources of greenhouse gas emissions in different areas. The clustering result in the study area showed that the continental CO₂ columnar concentration has a specific seasonal cycle, with the maximum and minimum values appearing in winter-early spring and spring-late summer, respectively. In conclusion, cluster analysis can classify the surface CO₂ column concentration values and determine the spatial distribution pattern of CO₂.

Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity

The satellite-based remote-sensing detection of CO2 can provide long-term and continuous observations at the global scale, and is the primary observational approach to monitoring CO2 changes. Surface albedo and aerosol are two significant challenges for retrieving CO2 concentrations from near-infrared remote-sensing data. This research addressed the dependence of radiance sensitivities on CO2 concentration, aerosol, surface albedo, and water vapor. The band ratio method was used to retrieve CO2 with band selection of the high- and low-sensitivity channels to restrain the influence of surface albedo and aerosol. Results showed that the band ratio method had better efficiency at reducing the impact of aerosol and surface albedo than that of the optical estimation method. The retrieval error of aerosol was reduced by 2.5% overall. We validated the band ratio method retrieval results with two TCCON sites and the GOSAT L3 product. Pearson’s correlation coefficient, mean bias, mean absolute bias, and root mean squared error of the monthly retrieval data of the band ratio method showed high agreement between the TCCON in Park Falls and Wollongong. These results indicate that the band ratio method based on normalized sensitivity can effectively reduce the influence of surface albedo and aerosol.

Measuring Atmospheric CO2 Enhancements From the 2017 British Columbia Wildfires Using a Lidar

During the summer 2017 ASCENDS/ABoVE airborne science campaign, the NASA Goddard CO₂ Sounder lidar overflew smoke plumes from wildfires in the British Columbia, Canada. In the flight path over Vancouver Island on 8 August 2017, the column XCO₂ retrievals from the lidar measurements at flight altitudes around 9 km showed an average enhancement of 4 ppm from the wildfires. A comparison of these enhancements with those from the Goddard Global Chemistry Transport model suggested that the modeled CO₂ emissions from wildfires were underestimated by more than a factor of 2. A spiral-down validation performed at Moses Lake airport, Washington showed a bias of 0.1 ppm relative to in situ measurements and a standard deviation of 1 ppm in lidar XCO₂ retrievals. The results show that future airborne campaigns and spaceborne missions with this type of lidar can improve estimates of CO₂ emissions from wildfires and estimates of carbon fluxes globally.

Toward High Precision XCO2 Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements

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 XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order 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 O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 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 strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 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 XCO2 retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO2 processing.

Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra

The proxy method, using the ratio of total column CH4 to CO2 to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH4 from satellite data. The present study characterizes the remaining scattering effects in the CH4/CO2 ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH4 for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH4 and CO2 bands. The ratio of partial column CH4 reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites.

Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the TanSat observations using the ACOS (Atmospheric CO2 Observations from Space) algorithm. The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for 2 years of TanSat data from 2017 to 2018. Based on the correlation of the XCO2 error over land with goodness of fit in three spectral bands at 0.76, 1.61 and 2.06 μm, we applied an a posteriori bias correction to TanSat retrievals. For overpass averaged results, XCO2 retrievals show a standard deviation (SD) of ~2.45 ppm and a positive bias of ~0.27 ppm compared to collocated TCCON sites. The validation also shows a relatively higher positive bias and variance against TCCON over high-latitude regions. Three cases to evaluate TanSat target mode retrievals are investigated, including one field campaign at Dunhuang with measurements by a greenhouse gas analyzer deployed on an unmanned aerial vehicle and two cases with measurements by a ground-based Fourier-transform spectrometer in Beijing. The results show the retrievals of all footprints, except footprint-6, have relatively low bias (within ~2 ppm). In addition, the orbital XCO2 distributions over Australia and Northeast China between TanSat and the second Orbiting Carbon Observatory (OCO-2) on 20 April 2017 are compared. It shows that the mean XCO2 from TanSat is slightly lower than that of OCO-2 with an average difference of ~0.85 ppm. A reasonable agreement in XCO2 distribution is found over Australia and Northeast China between TanSat and OCO-2.

Sensitivity of Optimal Estimation Satellite Retrievals to Misspecification of the Prior Mean and Covariance, with Application to OCO-2 Retrievals

Optimal Estimation (OE) is a popular algorithm for remote sensing retrievals, partly due to its explicit parameterization of the sources of error and the ability to propagate them into estimates of retrieval uncertainty. These properties require specification of the prior distribution of the state vector. In many remote sensing applications, the true priors are multivariate and hard to characterize properly. Instead, priors are often constructed based on subject-matter expertise, existing empirical knowledge, and a need for computational expediency, resulting in a “working prior.” This paper explores the retrieval bias and the inaccuracy in retrieval uncertainty caused by explicitly separating the true prior (the probability distribution of the underlying state) from the working prior (the probability distribution used within the OE algorithm), with an application to Orbiting Carbon Observatory-2 (OCO-2) retrievals. We find that, in general, misspecifying the mean in the working prior will lead to biased retrievals, and misspecifying the covariance in the working prior will lead to inaccurate estimates of the retrieval uncertainty, though their effects vary depending on the state-space signal-to-noise ratio of the observing instrument. Our results point towards some attractive properties of a class of uninformative priors that is implicit for least-squares retrievals. Furthermore, our derivations provide a theoretical basis, and an understanding of the trade-offs involved, for the practice of inflating a working-prior covariance in order to reduce the prior’s impact on a retrieval (e.g., for OCO-2 retrievals). Finally, our results also lead to practical recommendations for specifying the prior mean and the prior covariance in OE.

A Theoretical Analysis for Improving Aerosol-Induced CO2 Retrieval Uncertainties Over Land Based on TanSat Nadir Observations Under Clear Sky Conditions

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 (reff, veff), real refractive index coefficient of fine mode (arf) 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 (Hp), width of aerosol profile (Hw), effective variance of fine mode aerosol (vefff), effective radius of coarse mode aerosol (reffc), coefficient a of the real part of the refractive index for the fine mode and coarse mode (arf and arc), with the lowest error of less than 1.7 ppm for all aerosol and surface types. For marine aerosols, only five parameters (AOD, Hp, Hw, reffc and arc) 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.

Optimization of the Photon Path Length Probability Density Function-Simultaneous (PPDF-S) Method and Evaluation of CO₂ Retrieval Performance Under Dense Aerosol Conditions

The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving column-averaged concentrations of carbon dioxide (XCO₂) and methane (XCH₄) from Greenhouse gases Observing Satellite (GOSAT) spectra in Short Wavelength InfraRed (SWIR). Using this method, light-path modification attributable to light reflection/scattering by atmospheric clouds/aerosols is represented by the modification of atmospheric transmittance according to PPDF parameters. We optimized PPDF parameters for a more accurate XCO₂ retrieval under aerosol dense conditions based on simulation studies for various aerosol types and surface albedos. We found a more appropriate value of PPDF parameters referring to the vertical profile of CO₂ concentration as a measure of a stable solution. The results show that the constraint condition of a PPDF parameter that represents the light reflectance effect by aerosols is sufficiently weak to affect XCO₂ adversely. By optimizing the constraint, it was possible to obtain a stable solution of XCO₂. The new optimization was applied to retrieval analysis of the GOSAT data measured in Western Siberia. First, we assumed clear sky conditions and retrieved XCO₂ from GOSAT data obtained near Yekaterinburg in the target area. The retrieved XCO₂ was validated through a comparison with ground-based Fourier Transform Spectrometer (FTS) measurements made at the Yekaterinburg observation site. The validation results showed that the retrieval accuracy was reasonable. Next, we applied the optimized method to dense aerosol conditions when biomass burning was active. The results demonstrated that optimization enabled retrieval, even under smoky conditions, and that the total number of retrieved data increased by about 70%. Furthermore, the results of the simulation studies and the GOSAT data analysis suggest that atmospheric aerosol types that affected CO₂ analysis are identifiable by the PPDF parameter value. We expect that we will be able to suggest a further improved algorithm after the atmospheric aerosol types are identified.

The TanSat mission: preliminary global observations

The Chinese global carbon dioxide monitoring satellite (TanSat) was launched successfully in December 2016 and has completed its on-orbit tests and calibration. TanSat aims to measure the atmospheric column-averaged dry air mole fractions of carbon dioxide (XCO₂) with a precision of 4 ppm at the regional scale, and in addition, to derive global and regional CO₂ fluxes. Progress towards these objectives is reviewed and the first scientific results from TanSat measurements are presented. TanSat on-orbit tests indicate that the Atmospheric Carbon dioxide Grating Spectrometer is in normal working status and is beginning to produce L1B products. The preliminary TanSat XCO₂ products have been retrieved by an algorithm and compared to NASA Orbiting Carbon Observatory-2 (OCO-2) measurements during an overlapping observation period. Furthermore, the XCO₂ retrievals have been validated against eight ground-site measurement datasets from the Total Carbon Column Observing Network, for which the preliminary conclusion is that TanSat has met the precision design requirement, with an average bias of 2.11 ppm. The first scientific observations are presented, namely, the seasonal distributions of XCO₂ over land on a global scale.

Spaceborne detection of localized carbon dioxide sources

Spaceborne measurements by NASA's Orbiting Carbon Observatory-2 (OCO-2) at the kilometer scale reveal distinct structures of atmospheric carbon dioxide (CO₂) caused by known anthropogenic and natural point sources. OCO-2 transects across the Los Angeles megacity (USA) show that anthropogenic CO₂ enhancements peak over the urban core and decrease through suburban areas to rural background values more than ~100 kilometers away, varying seasonally from ~4.4 to 6.1 parts per million. A transect passing directly downwind of the persistent isolated natural CO₂ plume from Yasur volcano (Vanuatu) shows a narrow filament of enhanced CO₂ values (~3.4 parts per million), consistent with a CO₂ point source emitting 41.6 kilotons per day. These examples highlight the potential of the OCO-2 sensor, with its unprecedented resolution and sensitivity, to detect localized natural and anthropogenic CO₂ sources.

Combining XCO2 measurements derived from SCIAMACHY and GOSAT for potentially generating global CO2 maps with high spatiotemporal resolution

Global warming induced by atmospheric CO₂ has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO₂ globally, the number of valid CO₂ measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO₂ sources and sinks. Thus, it is a potentially promising work to combine the currently available CO₂ measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO₂ measurements is proposed by fully considering the CO₂ global bias, averaging kernel, and spatiotemporal variations as well as the CO₂ retrieval errors. Based on this method, a global CO₂ map with certain UTC time can also be generated by employing the pattern of the CO₂ daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO₂ map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO₂ maps with higher temporal and spatial sampling by jointly using these two space-based XCO₂ datasets.

Simultaneous retrieval of atmospheric CO2 and light path modification from space-based spectroscopic observations of greenhouse gases: methodology and application to GOSAT measurements over TCCON sites

This paper presents an improved photon path length probability density function method that permits simultaneous retrievals of column-average greenhouse gas mole fractions and light path modifications through the atmosphere when processing high-resolution radiance spectra acquired from space. We primarily describe the methodology and retrieval setup and then apply them to the processing of spectra measured by the Greenhouse gases Observing SATellite (GOSAT). We have demonstrated substantial improvements of the data processing with simultaneous carbon dioxide and light path retrievals and reasonable agreement of the satellite-based retrievals against ground-based Fourier transform spectrometer measurements provided by the Total Carbon Column Observing Network (TCCON).