TCCON Measurements Research Articles

Extracting XCO2-NASA data with XCODEX: a Python package designed for data extraction and structuration.

Accurately monitoring atmospheric carbon dioxide (XCO2) is fundamental to advancing climate change research. However, the intricate netCDF4 data format used by NASA's OCO-2 satellite complicates efficient data extraction and organization, limiting researchers' ability to fully utilize these datasets. To address this challenge, we developed XCODEX, a user-friendly Python package that automates the retrieval and structuring of daily XCO2 measurements from OCO-2 data. XCODEX processes raw files by defining variables, matching dates, and extracting targeted data points for multiple geographic locations, while minimizing missing data through intelligent reprocessing. Validation against ground-based TCCON measurements and Mauna Loa observations demonstrated high accuracy and reliability, with adjusted R2 values above 0.97 and root mean square errors below 1 ppm. Additionally, a regional analysis of XCO2 concentrations was conducted across 10 sites worldwide, including locations in both the Northern Hemisphere and Southern Hemisphere. This analysis revealed significant regional differences with a consistent rising trend of approximately 2.4 ppm per year, aligned with global increases in atmospheric CO2 influenced by natural and anthropogenic factors. By streamlining data handling and providing results in accessible Pandas DataFrame formats, XCODEX empowers researchers to focus on analytical insights rather than data preprocessing challenges. This package represents a valuable tool for global carbon cycle studies and contributes to improved environmental monitoring and climate modeling.

Instrument Line Shape Calibration and Comparison with TCCON Measurements for Greenhouse Gas Monitoring at a New COCCON Site in Korea

Instrument Line Shape Calibration and Comparison with TCCON Measurements for Greenhouse Gas Monitoring at a New COCCON Site in Korea

Global Daily Column Average CO2 at 0.1° × 0.1° Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning

Accurate global carbon dioxide (CO2) distribution with high spatial and temporal resolution is essential for understanding its dynamics and impacts on climate change. This study tackles the challenge of data gaps in satellite observations of greenhouse gases, caused by orbital and observational limitations. We reconstructed a comprehensive dataset of Column-averaged CO2 (XCO2) concentrations by integrating re-analyzed data from the Copernicus Atmosphere Monitoring Service (CAMS) with observations from GOSAT and OCO-3 satellites. Using two advanced data reconstruction methods—Data Interpolating Empirical Orthogonal Functions (DINEOF) and Convolutional Auto-Encoder (DINCAE)—we imputed missing data, preserving spatial and temporal consistency. The combined approach achieved high accuracy, with Pearson correlation values between 0.94 and 0.95 against TCCON measurements, and we also reported root mean square error (RMSE) to assess model performance further. Our results indicate that these techniques generate a daily, high-resolution, gap-free XCO2 dataset, enabling improved CO2 monitoring, climate modeling, and policy development.

Satellite-Based Reconstruction of Atmospheric CO2 Concentration over China Using a Hybrid CNN and Spatiotemporal Kriging Model

Although atmospheric CO2 concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R2 = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R2 = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO2 concentrations, thereby supporting policy formulation and actions to address climate change.

Retrieval of Global Carbon Dioxide From TanSat Satellite and Comprehensive Validation With TCCON Measurements and Satellite Observations

To cope with global climate change and monitor global CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentration distribution, the first Chinese carbon dioxide satellite (TanSat) has been successfully launched in December 2016. In this study, we implemented a CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> retrieval scheme by calibrating the TanSat sun-glint (GL) mode spectra and adapting the Iterative Maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Posteriori</i> Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> spectral retrieval. The global terrestrial CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> total vertical column density (VCD) and column-averaged dry-air mole fractions of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{X}_{\text {CO2}}$ </tex-math></inline-formula> ) were simultaneously retrieved from TanSat GL spectral observations. Then, a comprehensive verification was performed between TanSat CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> retrieval and other measurements including Total Carbon Column Observing Network (TCCON), the Japanese Greenhouse gases Observing SATellite (GOSAT), and the US Orbiting Carbon Observatory-2 (OCO-2). Further comparisons between our TanSat CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> retrieval and ground-based FTIR measurements from TCCON indicated a good correlation with the mean bias of −0.78 ppm, the standard deviation at 1.75 ppm, and the Pearson correlation coefficient of 0.81. In addition, cross-satellite CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> validations of TanSat with GOSAT and OCO-2 showed consistently spatiotemporal trends for both CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> VCD and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{X}_{\text {CO2}}$ </tex-math></inline-formula> . In summary, we can conclude that the presented CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> retrieval scheme has achieved global CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> retrieval from TanSat GL mode spectra with high precision and accuracy, as suggested by the results of independent ground-based and satellite validations.

Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity

NASA's Orbiting Carbon Observatory-3 (OCO-3) was designed to support the quantification and monitoring of anthropogenic CO2 emissions. Its Snapshot Area Map (SAM) and target mode measurements provide an innovative dataset for carbon studies on sub-city scales. Unlike any other current space-based instrument, OCO-3 has the ability to scan large contiguous areas of emission hot spots like cities, power plants, and volcanoes. These measurements result in dense, fine-scale spatial maps of column averaged dry-air mole fractions of carbon dioxide (XCO2). For the first time, we present and analyze XCO2 distributions over the Los Angeles megacity (LA) derived from OCO-3 SAM and target mode observations. Urban XCO2 enhancements range from 0 − 6 ppm (median enhancements ≃ 2 ppm) relative to a clean background and show excellent agreement with nearby ground-based TCCON measurements of XCO2. OCO-3's dense observations reveal intra-urban variations of XCO2 over the city that have never been observed from space before. The spatial variations are mainly driven by the complex fossil fuel emission patterns and meteorological conditions in the LA Basin and are in good agreement with those from co-located TROPOMI measurements of co-emitted NO2. Differences between measured and simulated XCO2 enhancements from two models (WRF-Chem and X-STILT) are typically below 1 ppm with larger differences for some sub regions. Both models capture the observed intra-urban XCO2 gradients. Further, OCO-3's multi-swath measurements capture about three times as much of the city emissions compared to single-swath overpasses. OCO-3's frequent target and SAM mode observations will pave the way to constrain urban emissions at finer, sub-city scales.

An intercomparison of total column-averaged nitrous oxide between ground-based FTIR TCCON and NDACC measurements at seven sites and comparisons with the GEOS-Chem model

Abstract. Nitrous oxide (N2O) is an important greenhouse gas and it can also generate nitric oxide, which depletes ozone in the stratosphere. It is a common target species of ground-based Fourier transform infrared (FTIR) near-infrared (TCCON) and mid-infrared (NDACC) measurements. Both TCCON and NDACC networks provide a long-term global distribution of atmospheric N2O mole fraction. In this study, the dry-air column-averaged mole fractions of N2O (XN2O) from the TCCON and NDACC measurements are compared against each other at seven sites around the world (Ny-Ålesund, Sodankylä, Bremen, Izaña, Réunion, Wollongong, Lauder) in the time period of 2007–2017. The mean differences in XN2O between TCCON and NDACC (NDACC–TCCON) at these sites are between −3.32 and 1.37 ppb (−1.1 %–0.5 %) with standard deviations between 1.69 and 5.01 ppb (0.5 %–1.6 %), which are within the uncertainties of the two datasets. The NDACC N2O retrieval has good sensitivity throughout the troposphere and stratosphere, while the TCCON retrieval underestimates a deviation from the a priori in the troposphere and overestimates it in the stratosphere. As a result, the TCCON XN2O measurement is strongly affected by its a priori profile. Trends and seasonal cycles of XN2O are derived from the TCCON and NDACC measurements and the nearby surface flask sample measurements and compared with the results from GEOS-Chem model a priori and a posteriori simulations. The trends and seasonal cycles from FTIR measurement at Ny-Ålesund and Sodankylä are strongly affected by the polar winter and the polar vortex. The a posteriori N2O fluxes in the model are optimized based on surface N2O measurements with a 4D-Var inversion method. The XN2O trends from the GEOS-Chem a posteriori simulation (0.97±0.02 (1σ) ppb yr−1) are close to those from the NDACC (0.93±0.04 ppb yr−1) and the surface flask sample measurements (0.93±0.02 ppb yr−1). The XN2O trend from the TCCON measurements is slightly lower (0.81±0.04 ppb yr−1) due to the underestimation of the trend in TCCON a priori simulation. The XN2O trends from the GEOS-Chem a priori simulation are about 1.25 ppb yr−1, and our study confirms that the N2O fluxes from the a priori inventories are overestimated. The seasonal cycles of XN2O from the FTIR measurements and the model simulations are close to each other in the Northern Hemisphere with a maximum in August–October and a minimum in February–April. However, in the Southern Hemisphere, the modeled XN2O values show a minimum in February–April while the FTIR XN2O retrievals show different patterns. By comparing the partial column-averaged N2O from the model and NDACC for three vertical ranges (surface–8, 8–17, 17–50 km), we find that the discrepancy in the XN2O seasonal cycle between the model simulations and the FTIR measurements in the Southern Hemisphere is mainly due to their stratospheric differences.

Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3 µm reflectance measurements

Abstract. We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311–2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data were processed with the Shortwave Infrared CO Retrieval algorithm (SICOR) that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA's Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites. For Ny-Ålesund, Lauder, Mauna Loa and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. The situation improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6. 0 ppb and a strong correlation between the validation and the SCIAMACHY results with a mean Pearson correlation coefficient r = 0. 7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set. For example, at the cities Tehran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171. 2 ppb and 57.9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. For cloudy-sky retrievals, the validation improves significantly. Here the retrieved column is mainly sensitive to CO above the cloud and so not affected by the strong local surface emissions. Adjusting the MOZAIC/IAGOS measurements to the vertical sensitivity of the retrieval, the mean bias adds up to b = 52. 3 ppb and 5.0 ppb for Tehran and Beijing. At the less urbanised region around the airport Windhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15. 5 ppb, but can be even further improved for cloudy SCIAMACHY observations with a mean bias of b = 0. 2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short-wave infrared measurements present a major extension of the clear-sky-only data set, which more than triples the amount of data and adds unique observations over the oceans. Moreover, the study represents the first application of the S5P algorithm for operational CO data processing on cloudy observations prior to the launch of the S5P mission.

Retrieving XCO2 from GOSAT FTS over East Asia Using Simultaneous Aerosol Information from CAI

In East Asia, where aerosol concentrations are persistently high throughout the year, most satellite CO2 retrieval algorithms screen out many measurements during quality control in order to reduce retrieval errors. To reduce the retrieval errors associated with aerosols, we have modified YCAR (Yonsei Carbon Retrieval) algorithm to YCAR-CAI to retrieve XCO2 from GOSAT FTS measurements using aerosol retrievals from simultaneous Cloud and Aerosol Imager (CAI) measurements. The CAI aerosol algorithm provides aerosol type and optical depth information simultaneously for the same geometry and optical path as FTS. The YCAR-CAI XCO2 retrieval algorithm has been developed based on the optimal estimation method. The algorithm uses the VLIDORT V2.6 radiative transfer model to calculate radiances and Jacobian functions. The XCO2 results retrieved using the YCAR-CAI algorithm were evaluated by comparing them with ground-based TCCON measurements and current operational GOSAT XCO2 retrievals. The retrievals show a clear annual cycle, with an increasing trend of 2.02 to 2.39 ppm per year, which is higher than that measured at Mauna Loa, Hawaii. The YCAR-CAI results were validated against the Tsukuba and Saga TCCON sites and show an root mean square error of 2.25, a bias of −0.81 ppm, and a regression line closer to the linear identity function compared with other current algorithms. Even after post-screening, the YCAR-CAI algorithm provides a larger dataset of XCO2 compared with other retrieval algorithms by 21% to 67%, which could be substantially advantageous in validation and data analysis for the area of East Asia. Retrieval uncertainty indicates a 1.39 to 1.48 ppm at the TCCON sites. Using Carbon Tracker-Asia (CT-A) data, the sampling error was analyzed and was found to be between 0.32 and 0.36 ppm for each individual sounding.

On the effect of spatial variability and support on validation of remote sensing observations of CO2

On the effect of spatial variability and support on validation of remote sensing observations of CO2

Carbon monoxide total columns from SCIAMACHY 2.3 µm atmospheric reflectance measurements: towards a full-mission data product (2003–2012)

Abstract. We present a full-mission data product of carbon monoxide (CO) vertical column densities using the 2310–2338 nm SCIAMACHY reflectance measurements over clear-sky land scenes for the period January 2003–April 2012. The retrieval employs the SICOR algorithm, which will be used for operational data processing of the Sentinel-5 Precursor mission. The retrieval approach infers simultaneously carbon monoxide, methane and water vapour column densities together with a Lambertian surface albedo from individual SCIAMACHY measurements employing a non-scattering radiative transfer model. To account for the radiometric instrument degradation including the formation of an ice-layer on the 2.3 µm detector array, we consider clear-sky measurements over the Sahara as a natural calibration target. For these specific measurements, we spectrally calibrate the SCIAMACHY measurements and determine a spectral radiometric offset and the width of the instrument spectral response function as a function of time for the entire operational phase of the mission. We show that the smoothing error of individual clear-sky CO retrievals is less than ±1 ppb and thus this error contribution does not need to be accounted for in the validation considering the much higher retrieval noise. The CO data product is validated against measurements of ground-based Fourier transform infrared spectrometers at 27 stations of the NDACC-IRWG and TCCON network and MOZAIC/IAGOS aircraft measurements at 26 airports worldwide. Overall, we find a good agreement with TCCON measurements with a mean bias b = −1.2 ppb and a station-to-station bias with σ = 7.2 ppb. The negative sign of the bias means a low bias of SCIAMACHY CO with respect to TCCON. For the NDACC-IRWG network, we obtain a larger mean station bias of b = −9.2 ppb with σ = 8.1 ppb and for the MOZAIC/IAGOS measurements we find b = −6.4 ppb with σ = 5.6 ppb. The SCIAMACHY data set is subject to a small but significant bias trend of 1.47 ± 0.25 ppb yr−1. After trend correction, the bias with respect to MOZAIC/IAGOS observation is 2.5 ppb, with respect to TCCON measurements it is −4.6 ppb and with respect to NDACC-IRWG measurements −8.4 ppb. Hence, a discrepancy of 3.8 ppb remains between the global biases with NDACC-IRWG and TCCON, which is confirmed by directly comparing NDACC-IRWG and TCCON measurements. Generally, the scatter of the individual SCIAMACHY CO retrievals is high and dominated by large measurement noise. Hence, for practical usage of the data set, averaging of individual retrievals is required. As an example, we show that monthly mean SCIAMACHY CO retrievals, averaged separately over Northern and Southern Africa, reflect the spatial and temporal variability of biomass burning events in agreement with the global chemical transport model TM5.

XCO2 satellite retrieval experiments in short-wave infrared spectrum and ground-based validation

Based on the optimal estimation method, a satellite XCO2 retrieval algorithm was constructed by combining LBLRTM with VLIDORT. One-year GOSAT/TANSO observations over four TCCON stations were tested by our algorithm, and retrieval results were compared with GOSAT L2B products and ground-based FTS measurements. Meanwhile, the influence of CO2 line mixing effect on retrieval was estimated, and the research showed that neglecting CO2 line mixing effect could result in approximately 0.25% XCO2 underestimation. The accuracy of XCO2 retrievals was similar to GOSAT L2B products at cloud-free footprints with aerosol optical depth less than 0.3, and 1% accuracy of XCO2 retrievals can be reached based on the validation result with TCCON measurements.

Corrigendum to &amp;quot;A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements&amp;quot; published in Atmos. Chem. Phys., 14, 3991–4012, 2014

Corrigendum to &amp;quot;A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements&amp;quot; published in Atmos. Chem. Phys., 14, 3991–4012, 2014

A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements

Abstract. This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003–2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27–35 Tg yr−1. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr−1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.