This session invites contributions on all aspects of remote sensing of CO2 and CH4, covering current missions (e.g., GOSAT/2/GW, OCO-2/3, S5P/S5, IASI-NG, Carbon Mapper, GHGSat), upcoming and planned missions (e.g., CO2M, MicroCarb, Merlin, TANGO), as well as ground-based (e.g., TCCON, COCCON), aircraft, and other remote sensing instruments. We welcome advances in retrieval techniques, instrumental concepts, and validation activities, with a particular emphasis on interpreting observations related to natural fluxes or anthropogenic emissions.
Was There a Tropical Land Carbon Sink During 2015-2023? Results from an Ensemble of Global
Inversion Models Constrained by OCO-2 Version 11 and the Global In Situ Network.
The lack of sufficient in situ observations over the Tropics makes the carbon balance and seasonality for
critical terrestrial regions such as the Amazon, the tropical rain forests in central Africa, and Tropical Asia
difficult to constrain with confidence. In some years the carbon budget is dominated by biomass burning,
while in other years, the dominant factors seem to be driven by climate forcing due to the El Niño Southern
Oscillation (ENSO). Previous studies using OCO-2 and the global in situ network (Liu et al, 2017; Palmer et
al, 2017; Crowell et al, 2019; Peiro et al, 2022; Byrne et al, 2024) have shown a more dynamic tropical
carbon cycle than what was understood previously. The duration and quality of the satellite XCO2 record is
now sufficient to begin investigating interannual variability in regional terrestrial carbon fluxes in the Tropics
as well as how the carbon fluxes respond to environmental and climatic drivers like temperature,
precipitation, vapor pressure deficit, and fire.
In this work, we will present preliminary results from the OCO-2 model intercomparison project (OCO-2
MIP) that estimates fluxes from numerous independent models using different data assimilation schemes
using surface and airborne in situ data as well as OCO-2 Version 11 retrievals. As noted in the past, the
strong El Niño of 2015-2016 resulted in strong tropical efflux in all tropical regions. The results also continue
to suggest that in the last decade, the Tropics have been net-zero or small source of carbon rather than being a
carbon sink. In this presentation, we will also discuss the causes of uncertainty associated with these
estimates, including the significant uncertainty due to modeled transport errors.
How to cite: Crowell, S.: Was There a Tropical Land Carbon Sink During 2015-2023? Results from an Ensemble of Global Inversion Models Constrained by OCO-2 Version 11 and the Global In Situ Network., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14356, https://doi.org/10.5194/egusphere-egu26-14356, 2026.
Accurately estimating and attributing methane (CH4) emissions is critical for climate change analysis and mitigation policy, but complex meteorology and terrain and incomplete knowledge of source geospatial distrubiton challenges current satellite-based and transport model-based methods. One of the largest coal mining regions by density is found in the high-elevation and mountainous regions of Shanxi China, and represents an ideal laboratory to quantify how emitted CH4 from such regions transports into the free troposphere and is subsequently mis-attributed downwind. We employ Empirical Orthogonal Function (EOF) analysis on five years of daily TROPOMI satellite observations, and WRF-STILT simulation to reveal the most important spatial-temporal causes of changes in methane concentration in the areas overlapping with the high coal mine methane in Shanxi and subsequent urban and agricultural downwind regions in Henan. Then we use ground observation data as physical constraints to construct a physics-based lightweight methane emission method to estimate methane emissions, while explicitly considering the impact of satellite data uncertainty on the emission results.
This work found that the contribution of the global background methane concentration to the methane in this region is approximately 40% and matches well with the upward trend modulated by seasonal cycles. Next this work identified a local mode on 5.3% of days that reveals a slow build-up and rapid release of CH4 from mining areas to middle-tropospheric loadings over downwind agricultural areas. During the 32 days of most substantial atmospheric transport, 0.064Mt of coal mine methane emissions slowly built up over basins in Shanxi and were transported over agricultural areas of Henan, accounting for over two thirds of the net 0.10Mt downwind increase. Applying our light-weight physically constrained emissions framework properly identifies the sources in these regions and effectively filters these observed long-range transported events, enabling more reliable emission estimates from existing satellite data. Failure to filter these events will lead to a substantial underestimation of fossil-fuel methane sources, since current isotopic constraint approaches do not sample middle tropospheric air.
How to cite: Zheng, B., Cohen, J. B., Qin, K., Hu, W., Lu, L., Liu, Y., Tiwari, P., Lolli, S., Garzelli, A., and Su, H.: Long-Range Transport of Coal Mine Methane Emissions Causes Source Mis-Attribution: A Satellite-Based Estimation Approach Incorporating Data Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6176, https://doi.org/10.5194/egusphere-egu26-6176, 2026.
Reducing global methane emissions is vital in combating climate change. Satellite-based instruments provide a way to independently monitor methane emissions from various sources at different scales, helping to assess the progress toward emission reduction targets. In this study, we apply several data-driven methods to estimate methane emissions from the Secunda CTL (coal-to-liquids) synthetic fuel plant in South Africa, utilizing satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S5P) satellite, and the GHGSat fleet of high-resolution commercial satellites. We find annual mean emissions of about 13-22 t/h based on S5P/TROPOMI observations. These results are consistent with estimates from an automated TROPOMI methane plume detection and quantification method. Estimates based on GHGSat observations from individual sources within the plant sum to about 6 t/h on average. For comparison, Sasol, the operator of the Secunda CTL facility, reported methane emissions of 11.5 t/h for the period July 2023-June 2024, a value that falls between the TROPOMI- and GHGSat-based estimates. Our results highlight the value of satellite observations as a useful audit complementing reported emissions and demonstrate the importance of combining coarse- and fine-resolution data to monitor methane emissions at plant and intra-facility level in complex sources. This research is part of the activities carried out at the Finnish Meteorological Institute for the development of the Emission Observatory platform (https://www.emissionobservatory.org) and within the METSA (METhane emissions from South Africa's Secunda synthetic fuel plant) ESA Third Party Mission project. Most of the results of the project are included in the journal article by Virta et al. (Environ. Sci. Technol. Lett., 2026).
How to cite: Ialongo, I., Virta, H., Hakkarainen, J., Tamminen, J., Girard, M., Schuit, B., and Maasakkers, J.: Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant using Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11856, https://doi.org/10.5194/egusphere-egu26-11856, 2026.
Industrial parks are major GHG sources and key actors in mitigation. Although satellite remote sensing has advanced since 2020—driven by initiatives like the Global Methane Pledge—it still excels mainly at isolated, strong methane point sources and struggles with dense source clusters, making facility-level attribution difficult. Two issues dominate: (1) the spectral–spatial trade-off—together with limited spectral resolution and SNR of current hyperspectral sensors—constrains XCH4 precision, pushing weak-source enhancements below retrieval noise; and (2) spatial overlap in large parks masks weak signals with nearby strong emitters. Even so, long-term matched-filter time series retain valuable, if hard-to-quantify, information.
We introduce an adaptive framework to apportion sub-source contributions within complex parks. The approach fuses sensors across scales: Sentinel-5P/TROPOMI constrains park-level totals, then time-series AHSI observations attribute emissions to individual facilities. This satellite-based method enables transparent, accurate facility-scale GHG reporting for industrial parks, supporting mitigation planning and the energy transition.
How to cite: Huang, Y., Yi, J., Han, G., Zhang, Y., Zhang, H., Shi, T., Pei, Z., and Gong, W.: Preliminary Top-Down Remote Sensing-Based Modeling of Facility-Level Methane Emission Attribution in the Oil and Gas Sector, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2722, https://doi.org/10.5194/egusphere-egu26-2722, 2026.
The intensification of global climate change has created an urgent need for high-precision monitoring of fossil fuel carbon dioxide (ffCO₂) emissions. The Paris Agreement emphasizes that countries must be able to rapidly and accurately track changes in carbon emissions to support effective policymaking and implementation. Achieving this goal depends on building accurate and verifiable carbon accounting systems. Precise estimation of ffCO₂ emissions is essential for climate prediction and the formulation of mitigation strategies. Here, we present a city-scale ffCO₂ inversion framework that integrates active and passive satellite observations to improve emission quantification. Using satellite-derived NO₂ data and CO₂–NOₓ emission ratios, we first constructed spatial maps of urban ffCO₂ emissions. We then incorporated XCO₂ observations from the DQ-1 satellite’s ACDL instrument to estimate monthly ffCO₂ emissions for several major cities worldwide. Unlike conventional top-down methods that rely heavily on prior emission inventories, our approach derives emission information directly from satellite observations. This innovation substantially reduces uncertainties caused by the temporal delays and spatial biases inherent in traditional bottom-up inventories, offering a more reliable and timely means of monitoring fossil fuel CO₂ emissions.
The framework combines high-resolution NO₂ column observations from Sentinel-5P/TROPOMI with column-averaged CO₂ (XCO₂) measurements from the world’s first spaceborne CO₂ lidar, the DQ-1 Atmospheric CO₂ Differential Absorption Lidar (ACDL). TROPOMI NO₂ data are first used to derive gridded urban NOₓ emissions through a mass-balance approach that explicitly accounts for wind divergence, chemical lifetime, and vertical distribution. These NOₓ emissions are then converted into prior ffCO₂ distributions using city-specific CO₂-to-NOₓ emission ratios. Subsequently, DQ-1 XCO₂ along-track observations are assimilated within a Bayesian inversion framework driven by high-resolution WRF-STILT simulations to constrain total urban ffCO₂ emissions.
This study demonstrates the unique value of combining active CO₂ lidar and passive NO₂ observations for rapid, observation-driven verification of urban anthropogenic CO₂ emissions, and provides a unified framework for city-scale carbon monitoring under limited or uncertain inventory conditions.
How to cite: Yi, J., Huang, Y., Han, G., Zhang, H., Pei, Z., Shi, T., Li, S., and Gong, W.: Inventory-Free Inversion of Urban ffCO₂ Emissions Using Combined Observations from Sentinel-5P and DQ-1, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2069, https://doi.org/10.5194/egusphere-egu26-2069, 2026.
The Integrated Greenhouse Gas Monitoring System (ITMS) aims to establish an operational top-down monitoring framework for greenhouse gases (GHG) in Germany by combining atmospheric in situ and remote-sensing observations with atmospheric transport modelling and inverse estimation techniques. Power plants and large industrial facilities account for more than half of global anthropogenic CO₂ emissions and are therefore key targets. However, the limited temporal and spatial resolution of satellite GHG observations makes complementary ground-based measurements necessary for robust emission quantification at the facility scale.
This work contributes to ITMS by assessing the capability and uncertainties of quantifying GHG emissions from a major point source using ground-based observations and atmospheric transport modelling. The study focuses on the Bremen steelworks, comprising two blast furnaces and a blast-furnace-gas-fired power plant, emitting approximately 5 Mt CO2 yr⁻¹ and accounting for nearly half of the city’s total emissions.
The campaign measurements conducted between April and June 2024 and 2025 targeted the plumes of the steelworks: Two portable Bruker EM27/SUN FTIR spectrometers measured column-averaged abundances of CO2, CO and CH4, while background values were provided by the Bruker 125HR FTIR spectrometer at the University of Bremen. Mobile zenith-sky DOAS observations of co-emitted NO2 constrained plume width and trajectory, surface CO2 concentrations were measured in situ, and wind profiles were obtained from a Doppler wind lidar. Plume transport was simulated with a Gaussian plume model and combined with excess CO and CO2 measurements in an inversion framework to derive emission ratios and emission estimates.
The derived CO/CO2 emission ratio is 3.46% ± 0.85%, consistent with emission inventories (3.33%, Umweltbundesamt). Constraining the model with real-time DOAS plume observations yielded preliminary emission estimates ranging from 40% to 179% of inventory values, with an average of 79% ± 49%. These results highlight both the promise and current limitations of ground-based remote sensing in reducing uncertainties of facility-level emission quantification.
How to cite: Grosch, L., Brink, M., Butz, A., Feld, L., Hase, F., Löw, B., Ohlendorf, J.-H., Richter, A., Visarius, T., and Warneke, T.: Facility-scale greenhouse gas emission quantification at the Bremen steelworks using ground-based remote sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9561, https://doi.org/10.5194/egusphere-egu26-9561, 2026.
The mitigation of methane emissions is one of the prime targets of global climate policy due to methane’s large contribution to global warming. Satellite instruments have proven to be very effective in mapping and tracking methane super-emitters. There are several new Copernicus Contributing Missions (CCMs) that are able to provide high resolution (~ 25m) methane abundance data that enables detection of emissions from individual facilities. We introduce a new Copernicus Atmosphere Monitoring Service (CAMS) service where we will use these CCMs to pinpoint individual methane sources all around the world to provide insights on emissions in support of mitigation efforts. The service will start with GHGSat data and aims to incorporate GEISAT and GESat data later. We will obtain observations over hundreds of methane hot spots and sites of interest around the world in support of climate policy. We process the satellite data starting from the methane abundance data provided by the CCMs using the SRON-developed HyperGas package. The data are standardized and potential methane plumes in the abundance field are automatically masked. Multiple expert operators then determine and agree on which masked features are true methane plumes. The automatically generated masks are then used for emission rate estimation using the integrated mass enhancement (IME) method. This methodology is calibrated using instrument-specific synthetic observations and will be evaluated using observations of controlled releases. Our semi-supervised approach allows for a consistent quantification of plumes over the full range of observations. Our processing is done independently from the analysis done by the CCMs themselves and thus serves as an evaluation. We also compare our results with bottom-up emission estimates such as included in the “TNO Emission Atlas”. This way, our work can provide a crucial link between satellite methane observations and facility level bottom-up inventories. We present our approach in consistently handling this large volume of data as well as initial results and interesting cases.
How to cite: Bosman, A. D., van 't Hoff, J., Zhang, X., Maasakkers, J. D., van der Velde, I. R., Dellaert, S., Denier van der Gon, H., Kountouris, P., Steinig, S., and Aben, I.: A new Copernicus Atmosphere Monitoring Service for methane emissions at facility scale using atmospheric Copernicus Contributing Missions data., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17726, https://doi.org/10.5194/egusphere-egu26-17726, 2026.
The Copernicus sentinel expansion missions include the first European CO2 measuring satellite, CO2M, scheduled to be launched in late 2027. To utilize the CO2M satellite data, it requires a good knowledge of the variation in anthropogenic and natural sources of CO2 from models and observations. In this project, we present new modelling results of the temporal and spatial variation in atmospheric CO2 across Denmark. This is done by employing an updated version of the DEHM-SPA-CO2 model system, being developed.
The DEHM-SPA-CO2 model system consists of the Danish Eulerian Hemispheric Model (DEHM) adapted for CO2 and coupled to the mechanistic biosphere flux model Soil-Plant-Atmosphere (SPA). The model has been updated with a new high-resolution emissions dataset for Denmark, as well as simulated traffic activity from COMPASS, following different experimental policies for impacting emissions. The model system is evaluated against the ICOS station network, and preliminary results show good agreement.
Additionally, data from a new urban observation site in Copenhagen are used together with satellite data from OCO-2 and GOSAT-2 to evaluate the modelling results. GOSAT-1 was also considered but only few observations are available for Denmark. The Urban CO2 station is located on top of a building in Copenhagen and will be used to evaluate the model representation of Traffic emissions. The OCO-2 and GOSAT-2 data are used to fit a seasonal variation for Denmark.
Lastly, we present a discussion of the possibility of doing mass flux estimates for emissions from Copenhagen using OCO-2 overpasses in optimal wind conditions. This, however, still faces the challenge of a low signal-to-noise ratio. While it is typically manageable for point sources, the more diffuse emissions from a city such as Copenhagen result in a lower ratio, complicating the inference of CO2 signals.
How to cite: Hvidberg, N. S., Shi, H., Lansø, A. S., Christensen, J. H., Høyer, J., and Skjoth, C. A.: Modelling Atmospheric CO2 in Denmark for Remote Sensing applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21524, https://doi.org/10.5194/egusphere-egu26-21524, 2026.
Iceland represents one of the few emerged portions of the Mid-Atlantic-Ridge, a 16,000 km-long tectonic boundary separating the North American and Eurasian plates. This unique position gives rise to Iceland’s characteristic geothermal manifestations and volcanic complexes (Figure 1). Here, CO2 from volcanic sources is released alongside other natural and anthropogenic emissions, as part of the global carbon cycle.
Figure 1. Map of Iceland with distribution of averaged NDVI from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) for June 2018. The red triangles show locations of volcanoes and fumaroles and the grey squares represent pixels where OCO-2 XCO2 retrievals are available.
The quantification of these fluxes in relation to the plate tectonic system provides valuable insights into the nature of long-term CO2 migration and retention, which, in turn, can help assess the potential for leakage and migration pathways in the context of geological storage of CO2. However, volcanogenic sources of CO2 remain poorly quantified, partly because such studies rely on ground-based measurements or airborne remote sensing, which can be challenging and hazardous during periods of volcanic unrest.
NASA’s Orbiting Carbon Observatory-2 (OCO-2), launched in 2014, has been proven to be capable of observing localised point source signals thanks to its unprecedented instrument precision and resolution. In this study, we explore the use of OCO-2’s column-averaged dry-air mole fractions of CO2 (XCO2) retrievals to assess the onshore CO2 fluxes in Iceland.
Because volcanogenic sources of CO2 typically exhibit small enhancements above background concentrations, a robust quantification of the influence of non-volcanic CO2 contributors on OCO-2 retrievals is needed. Therefore, we analysed nearly a decade of OCO-2 data to reveal the long-term anthropogenic emissions trends and the seasonal variations due to biogenic fluxes. We observe a steady increase of ~2 ppm per year in average atmospheric CO2 concentrations (Figure 2a), attributed to anthropogenic emissions, and an inverse trend between monthly XCO2 averages and the Normalised Difference Vegetation Index (NDVI) (Figure 2b), reflecting seasonal vegetation growth as a carbon sink. However, due to severe weather conditions and prolonged winter darkness at high latitudes, no data was available from October to March, limiting our window of observation.
Figure 2. a) Evolution of OCO-2 XCO2 observations over Iceland since 2015 with yearly means in red squares.
b) Evolution of mean OCO-2 XCO2 observations and NDVI values over Iceland in 2018.
The observed trends provide the reference framework required for isolating volcanogenic CO2 contributions and are informative for understanding the carbon cycle in this region. Despite observational challenges posed by Iceland’s location in the high North, our analysis of these OCO-2 retrievals brings important insights into resolving spatiotemporal CO2 patterns from space over volcanically active regions. The ensuing step will be to quantify the relative contribution of known volcanogenic CO2 sources (e.g., volcanos, fumaroles, and diffuse soil degassing, etc.) to OCO-2 retrievals.
How to cite: Delbet, C., Ringrose, P., and Eidsvik, J.: Quantifying spatiotemporal CO2 trends in surface fluxes over Iceland using OCO-2 XCO2 retrievals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10846, https://doi.org/10.5194/egusphere-egu26-10846, 2026.
Human heating- and cooling-induced CO2 emissions exhibit substantially different responses to extreme temperatures across countries, leading to satellite-detectable enhancements in atmospheric concentration. Quantifying this relationship is crucial for understanding human-climate interactions and informing targeted carbon mitigation policies. However, a global, systematic assessment has been hindered by the lack of timely, reliable, high-resolution carbon monitoring data. Satellite observations of the column-averaged dry-air mole fraction of CO2 (XCO2) always suffer from cloud-induced data gaps and overwhelming interference from natural fluxes, impeding anthropogenic signal extraction. Here, we integrate OCO-2/3 XCO2 and TROPOMI NO2 observations via machine learning to reconstruct global daily 0.1° XCO2 fields (2021-2023), and use local Moran’s I statistics to isolate anthropogenic XCO2 enhancements (XCO2en). The resulting XCO2en data exhibit distinct seasonal cycles across the world’s top-emitting regions, characterized by elevated levels in summer and winter. SHapley Additive exPlanations (SHAP) analysis reveals a consistent V-shaped relationship globally between temperature and its contribution to XCO2, indicating that both high and low temperature extremes elevate the SHAP value, with an optimal temperature (To) yielding the minimum value. Notably, despite vast differences in national temperature distributions, the To across countries converges around 21°C, suggesting a common human thermal adaptation threshold. Moreover, the slope of the V-shaped curve, representing the sensitivity of the XCO2en response to temperature, exhibits a significant positive correlation with national GDP per capita (R=0.50, p<0.01) and a negative correlation with the share of renewable energy consumption (R=-0.34, p<0.05). Our model effectively delineates the spatiotemporal patterns of anthropogenic XCO2en by leveraging carbon-nitrogen synergy, providing critical insights for decarbonization strategies and renewable energy transitions in diverse economies under carbon neutrality goals.
How to cite: Wang, L., Li, T., Song, X., Dou, X., Yu, Y., and Liu, Z.: Global anthropogenic XCO2 enhancements response to temperature changes with country-specific adaptability and sensitivity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10829, https://doi.org/10.5194/egusphere-egu26-10829, 2026.
The European Space Agency (ESA), in collaboration with the European Commission (EC) and EUMETSAT, is developing as part of the EC’s Copernicus Sentinel expansion programme, a space-borne observing system for quantification of the emissions of the main greenhouse gases: anthropogenic carbon dioxide (CO2) and methane (CH4). The anthropogenic CO2 monitoring (CO2M) mission will be implemented as a constellation of three identical LEO satellites, to be operated over a period of at least 7 years and measuring CO2 concentration in terms of column-averaged dry air mole fraction (denoted as XCO2). Each satellite will continuously image XCO2 along the satellite track on the sun-illuminated part of the orbit, with a swath width of 250 km. Observations will be provided at a spatial resolution better than 2 x 2 km2, with high precision (<0.7 ppm) and accuracy (bias <0.5 ppm), which are required to resolve the small atmospheric gradients in XCO2 originating from anthropogenic activities.
The demanding mission requirements necessitate a payload composed of four instruments, which simultaneously perform co-located measurements. The main instrument, called CO2I, consists of a push-broom imaging spectrometer which will perform co-located measurements of top-of-atmosphere radiances in the Near Infrared (NIR) and Short-Wave Infrared (SWIR) at high to moderate spectral resolution (NIR: 747- 773nm @0.1nm, SWIR-1: 1595-1675nm @0.3nm, SWIR-2: 1990-2095nm @0.35nm) for retrieving XCO2 and XCH4. These observations are complemented by a second instrument called NO2I in the same spectrometer acquiring measurements in the visible spectral range (405-490 nm @0.6nm), providing vertical column measurements of nitrogen dioxide (NO2) that serve as a tracer to high temperature combustion of fossil-fuel and related emission plumes. High quality retrievals of XCO2 will be ensured even in the presence of aerosol loading, thanks to co-located measurements of aerosol properties resulting from a third instrument called Multiple Angle Polarimeter (MAP). Polarimetric measurements are performed over 40 angular views and in six spectral channels in the range 410 nm to 865 nm. A fourth instrument is a three-band Cloud Imager (CLIM) that will provide the required capacity to detect small tropospheric clouds and cirrus cover with an accuracy of 1% to 5% and a sampling better than 400 m. Indeed, cloud contamination has a strong impact on the XCO2 retrieval.
Starting by a summary of the main scientific drivers, this paper will provide an overview of the progress of the space segment development: platform, payload as well as the end-to-end simulator. CO2M is now in phase D, with manufacturing, integration and testing of the first two flight models (PFM and FM2) on-going.
How to cite: Durand, Y., Ouslimani, H., Bazalgette, G., Martinez Fernandez, M., Bastirmaci, T., Birtwhistle, A., Meijer, Y., and Fernandez, V.: Progress of CO2M implementation, the Copernicus mission for monitoring anthropogenic carbon dioxide from space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22948, https://doi.org/10.5194/egusphere-egu26-22948, 2026.
In the support of the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, Space Research Organization Netherlands (SRON) developed the Remote sensing of Trace gas and Aerosol Product (RemoTAP) algorithm. RemoTAP is the only algorithm with the capability to simultaneously retrieve trace gases and aerosol using measurements from the Multi-Angle Polarimeter (MAP) and CO2I Instrument. At the same time, it has the capability to perform the retrieval of trace gas from only CO2I measurements.
This study evaluates the performance of RemoTAP for combined CO2I-MAP and CO2I-only retrievals, respectively. We base our evaluation on synthetic CO2M measurements simulated for realistic atmospheric (aerosol, cirrus), surface, geometry conditions. CO2I-MAP retrieval method can reduce the regional bias in column-averaged dry-air mole fraction of CO2 (XCO2) by a factor of 3 for both land and ocean. It shows that only by the inclusion of MAP measurements, the large aerosol-induced biases can be mitigated, resulting in the retrievals that meet the mission requirement (precision <0.7 ppm and bias <0.5 ppm).
To further improve the accuracy of trace-gas retrievals, we develop a Neural Network (NN) approach to provide an accurate first guess of aerosol and surface parameters. We also develop a bias correction and quality filtering using an NN approach.
How to cite: Lu, S., Fu, G., and Hasekamp, O.: Retrieving XCO2 from the CO2M mission: Joint use of Multi-Angle Polarimeter and spectrometer measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11587, https://doi.org/10.5194/egusphere-egu26-11587, 2026.
Top-down estimation of greenhouse gas emissions requires the combination of reliable atmospheric concentration measurements with atmospheric inversions. The German Integrated Greenhouse Gas Monitoring System (ITMS) combines atmospheric in situ, remote sensing and satellite measurements, transport modelling, and inverse estimation techniques, aiming at an operational top-down monitoring of greenhouse gas emissions. We contribute to this effort by establishing highly consistent and accurate observations of column-average mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) using eight COCCON (EM27/SUN) and two TCCON FTIR instruments across Germany.
We operate Collaborative Carbon Column Observing Network (COCCON, EM27/SUN) and Total Column Carbon Observing Network (TCCON) spectrometers located such that the measurements cover spatial gradients on the urban, regional and national scale. We ensure excellent consistency among all stations by operating an additional EM27/SUN as travel standard, performing regular side-by-side measurements with all network instruments. As such, we calibrate all instruments to a common scale and, via TCCON, tie them to the World Meteorological Organization (WMO) scale. These measurements provide the means to validate both satellite observations and modelling results on the spatial scales relevant for future emission inversions.
After the second year of operation, we present the extended dataset, as well as our solidified uncertainty analysis based on the continued side-by-side measurements throughout the years 2024 and 2025. Additionally, we present our approach to better quantify residual systematic uncertainty contributions by employing an Acetylene absorption cell during regular atmospheric measurements.
How to cite: Löw, B., Feld, L., Grosch, L., Klappenbach, F., Kleinschek, R., Luther, A., Oliveira Makowski, M., Stauber, J., Chen, J., Hase, F., Warneke, T., and Butz, A.: The ITMS-FTIR network for Germany: Second year of consistent XCO2, XCH4 and XCO data for satellite and model validation on the urban, regional and national scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11266, https://doi.org/10.5194/egusphere-egu26-11266, 2026.
The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission is a small satellite mission to be launched in 2028, under the ESA Scout Programme tapping into NewSpace to quickly deliver affordable and innovative science, as part of ESA’s FutureEO Programme a schedule of three years from mission kick-off to launch. Designed to complement the Copernicus atmospheric monitoring missions Sentinel-5 Precursor, Sentinel‑4/5, and the CO2M carbon dioxide monitoring mission, TANGO will observe carbon dioxide and methane emissions from human activities to support verification of the Paris Agreement. The mission is anticipated to generate >10,000 emission estimates per year for major industrial facilities and power plants. The scientific community will be able to propose specific observation targets, which will be incorporated into mission planning alongside routine observations aimed at enhancing current state-of-the-art point-source emission inventories.
Two agile CubeSat-class satellite buses, each carrying an imaging spectrometer, will operate in close formation with a temporal separation of less than 1 minute, enabling near-sequential observations of the same target area. Platform agility is ensured by three-axis stabilized reaction wheel control, which permits flexible spectrometer pointing with a roll capability of ±30° and forward motion compensation. This forward motion compensation increases the effective integration time by up to a factor of five, thereby enhancing spatial coverage and improving the precision of the retrieved geophysical quantities. As part of the mission implementation, a dedicated ground segment will be established to provide the scientific user community with open and freely accessible data products. These will include calibrated top-of-atmosphere radiance measurements (Level-1b), column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4), as well as tropospheric NO2 column densities (Level-2), and corresponding emission estimates for CO2, CH4, and NO2 (Level-4). TANGO’s first satellite, TANGO-Carbon, will measure solar-reflected radiances in the 1.6 µm spectral region with a spectral resolution of 0.45 nm, enabling the detection of moderate to strong CH4 emissions (≥ 5 kt yr⁻¹) and CO2 emissions (≥ 2.5 Mt yr⁻¹). The TANGO-Nitro instrument will provide collocated NO2 observations derived from radiance measurements in the visible spectral range with a spectral resolution ≤ 0.6 nm, facilitating plume detection and the use of the CO2/NO2 ratio for improved source characterization and emission quantification. In this contribution, we describe the status of the TANGO mission, the planned data products, the associated scientific opportunities, and the mechanisms for engagement of the scientific community in data exploitation.
How to cite: Landgraf, J., Veefkind, P., Ludewig, A., Leune, B., van Amelrooy, E., van Hoek, M., Borsdorff, T., Laasner, R., van Hees, R., Cooney, R., Louzada, K., Alpay Koc, N., de Grooij, B., Dellaert, S., Denier van der Gon, H., Lejault, J.-P., Pastena, M., Sanders, B., de Groot, Z., and Marasini, C.: TANGO The Twin Anthropogenic Greenhouse Gas Observers Mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18371, https://doi.org/10.5194/egusphere-egu26-18371, 2026.
Urban regions are major contributors to anthropogenic CO₂ emissions, yet satellite-based observations of column-averaged CO₂ remain spatially and temporally sparse, limiting high-resolution urban monitoring. This study presents a machine-learning framework for predicting daily CO₂ over European urban areas at a spatial resolution of 0.02°, integrating satellite NO₂ observations, reanalysis meteorological variables, and surface data. High-density OCO-2 target-mode observations are used as ground truth, enabling robust learning of the relationships between CO₂ and its atmospheric and surface drivers.
Two predictive scenarios are evaluated. The first, a sample-based prediction designed primarily for spatial gap filling, achieves an R² of 0.98 ± 0.00 and an RMSE of 0.60 ± 0.02 ppm using 10-fold cross-validation. The second scenario assesses spatiotemporal generalization, yielding an average R² of 0.91 ± 0.02 and RMSE of 1.17 ± 0.14 ppm for temporal transfer, and R² of 0.85 ± 0.09 with RMSE of 1.25 ± 0.20 ppm for spatial transfer across European regions. Independent validation against ground-based CO₂ measurements from the Munich Urban Carbon Column network (MUCCnet) shows strong agreement, with R² values between 0.95 and 0.97 and RMSE ranging from 0.50 to 0.72 ppm.
The results demonstrate the potential of the proposed framework to fill observational gaps and generate reliable, high-resolution CO₂ fields over urban environments and their surroundings, supporting improved monitoring of anthropogenic CO₂ emissions where accurate information is most critical.
How to cite: Abu-Hani, A., Chen, J., Balamurugan, V., Osterman, G., and Kiel, M.: Daily High-Resolution CO₂ Mapping over European Urban Areas from Targeted Satellite Observations Using Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12839, https://doi.org/10.5194/egusphere-egu26-12839, 2026.
Satellite retrievals of atmospheric greenhouse gas concentrations are used to obtain information on their sources and sinks via inverse modelling. Such an application requires very high accuracy as even small biases of the retrieved concentrations may result in large errors of the inferred emissions or sink strength. For example, for the upcoming Copernicus satellite mission dedicated to carbon dioxide monitoring (CO2M) the accuracy of the dry-air column-averaged CO2 mole fraction (XCO2) is required to be better than 0.5 ppm. Here we investigate a potentially important systematic error source, namely XCO2 biases due to sub-pixel variability of surface reflectivity (albedo) and altitude. We show that the XCO2 bias can exceed the accuracy requirements up to three times over low-mountain ranges in Germany especially if surface albedo and altitude are spatially correlated within single ground pixels. To minimize this error source we motivate that the use of albedo-weighted surface altitude better represents the satellite’s spatial sample than the unweighted average. We use Copernicus Sentinel-2 data combined with Copernicus Digital Elevation Model (DEM) data and the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm and create a variety of self-consistent experiments to confirm this theory. First we conduct experiments with defined conditions and second we apply the methodology to some examples with real topography and surface albedo. In all these examples, we find that using the albedo-weighted average of the surface altitude is needed to reduce biases at locations with heterogeneous surface structure to values below the requirements for future satellite missions. We show that the use of the albedo-weighted surface altitude in the retrieval process results in significant reduction of the XCO2 bias compared to the use of the unweighted mean altitude, as currently used in most retrieval schemes.
This work is funded by the ESA CO2M Science Study under contract no. 4000138164/22/NL/SD and by the German Federal Ministry of Research, Technology and Space (BMFTR) project "Integrated Greenhouse Gas Monitoring System for Germany – Observations (ITMS-B)" under grant number 01LK2103A .
How to cite: Weimer, M., Reuter, M., Hilker, M., Noël, S., Buchwitz, M., Meijer, Y., Lang, R., Marshall, J., Bovensmann, H., Burrows, J. P., and Bösch, H.: Importance of subpixel Earth surface reflectance and altitude for atmospheric trace gas retrievals from nadir satellite instruments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1861, https://doi.org/10.5194/egusphere-egu26-1861, 2026.
As the dominant greenhouse gas, the spatiotemporal distribution of carbon dioxide (CO2) concentration is a key parameter in global climate change research. Satellite remote sensing has become a crucial means for detecting CO2 on a global scale. The infrared laser occultation technology employed by Low-Earth Orbit (LEO) satellites offers a novel technical approach for obtaining vertical profiles of atmospheric CO2, capitalizing on its advantages of high vertical resolution, high precision, and strong global coverage capability. This paper systematically investigates the CO2 retrieval method for this technology. The core principle involves a transmission-reception mode based on a LEO-LEO dual-satellite constellation. An infrared laser source onboard the transmitting satellite emits laser pulses at specific wavenumbers, which traverse the atmosphere and are captured by the receiving satellite. Leveraging the selective absorption characteristics of CO2 in specific infrared bands and the principle of differential absorption, concentration retrieval is achieved. Firstly, an infrared laser occultation signal link model is constructed to optimize the selection of detection wavenumbers. Through simulation analysis of the sensitivity of CO2 detection precision at different wavenumbers, an optimal wavenumber pair is determined. This wavenumber pair effectively mitigates interference from other atmospheric components, ensuring the specificity of the CO2 absorption signal. Secondly, a complete retrieval calculation procedure is established. The dual-wavelength transmittance is calculated from the ratio of transmitted power to received signal strength, subsequently enabling the solution of the differential optical depth along the entire optical path. Utilizing the Abel integral transform, the differential optical depth is converted into the CO2 differential absorption coefficient at the atmospheric path tangent point. Combined with the ideal gas law and the atmospheric quasi-static equation, concentration conversion is performed, ultimately yielding the vertical CO2 concentration profile. The retrieval method proposed in this study effectively addresses the issues of low vertical resolution and uneven regional coverage inherent in traditional satellite remote sensing for CO2 profile detection. It provides core algorithmic support for the engineering implementation of spaceborne infrared laser occultation CO2 detection systems. The high-precision global CO2 concentration data obtained can offer significant scientific data support for carbon source/sink assessment, climate change prediction, and related policy formulation.
How to cite: Zong, X., Wang, X., and Zhang, Z.: Retrieval Method for Carbon Dioxide Using Infrared Laser Occultation from Low-Earth Orbit Satellites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9413, https://doi.org/10.5194/egusphere-egu26-9413, 2026.
As the international community moves towards the second Global Stocktake under the Paris Agreement, the demand for independent, transparent, and verifiable greenhouse gas (GHG) emission estimates has never been more critical. While satellite-based monitoring offers a powerful verification tool, the uncertainty of top-down flux estimates is currently dominated by substantial uncertainties in local wind speed input, which typically relies on coarse meteorological reanalysis models. This dependency introduces potential biases and correlated errors that undermine the scientific integrity required for high-stakes climate policy. Addressing this bottleneck, we present a comprehensive science study dedicated to developing an independent, data-driven in-plume wind retrieval framework designed specifically for potential future satellite missions equipped with multi-angle or multi-platform observations. By simulating the data products of such missions using high-resolution Large-Eddy Simulations (LES), we generated a robust dataset of realistic plume dynamics to develop and validate our algorithms. Exploring the temporal information embedded across consecutive plume images, we propose and evaluate two distinct, complementary methodologies for deriving wind velocity fields directly from plume imagery. First, we apply an Multi-Image Correlation Image Velocimetry (CIV) algorithm, optimized to dynamically correct temporal centering errors by averaging correlation surfaces across the observations sequence. Second, we introduce CVision-CIV, a novel deep learning approach based on the UnLiteFlowNet-PIV architecture, which utilizes Convolutional Neural Networks (CNNs) to extract morphological flow features directly from noisy imagery sets. Through an application on simulated CO2 emission plumes, we demonstrate that while physical CIV methods provide robust baselines, the CVision-CIV model exhibits superior stability in low signal-to-noise regimes, effectively suppressing sensor noise where traditional correlation breaks down. By validating these parallel pathways on LES-generated observations, this work establishes a comprehensive algorithmic foundation for defining observational requirements for future missions aiming to replace reanalysis proxies with precise, observation-based wind products for improved GHG monitoring. We will discuss the methods’ sensitivity to observational noise, number of images, time-difference between images and resolution.
How to cite: van Diedenhoven, B., Patel, P., Reerink, K., Borsdorff, T., and Landgraf, J.: From Snapshots to Fluxes: Independent Wind Retrieval Algorithms for Next-Generation Multi-image Greenhouse Gas Satellites via Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20751, https://doi.org/10.5194/egusphere-egu26-20751, 2026.
Atmospheric methane climate data sets are crucial for monitoring and mitigating global warming and associated climate change impacts. The Greenhouse Observing Satellite (GOSAT) has been monitoring atmospheric methane since 2009 with near-surface sensitivity and forms a robust long-term climate data record that is essential for tracking global emission trends and understanding methane budget. University of Leicester has developed an operational global methane dataset from level 1 GOSAT data using a proxy retrieval method that is advantageous in mitigating the effects of aerosol scattering and instrumental errors. Year-round monitoring of atmospheric methane at high-latitudes is important in the context of Arctic amplification due to excessive warming that can trigger several climate feedbacks loops such as, thawing permafrost-carbon release feedback and snow - albedo feedback. However, data acquisition over high-latitudes is limited by challenging due to frequent cloud cover and low solar illumination resulting in a significant data deficit during winter season. As an attempt to resolve this limitation, this study has used a Non-dominated Sorting Genetic Algorithm II (NSGA II) approach to optimise the post-retrieval quality filters of University of Leicester GOSAT Proxy methane datasets to increase the data volume with least impact on the data quality compared to the ground-observations. If we loosen the QA filters to capture data under challenging conditions, data quality will naturally deteriorate due to introduction of noisy measurements. NSGA method is specifically suited for addressing problems with inherently conflicting objectives like in this case. In our problem fitness of each solution is evaluated based on a combination of the number of valid GOSAT observations obtained at high latitudes and the Root Mean Square Error (RMSE) between collocated GOSAT retrievals and ground-based data from the Total Carbon Column Observing Network (TCCON) network. Results suggest GA-optimized QA filters lead to an approximately 20% increase in valid satellite soundings over high latitudes with less than 1 ppb increase in RMSE between GOSAT and TCCON soundings. The optimised data set has significant data gain over the high-latitudes with more than double gain during winter. This work demonstrates the potential of GAs for improving greenhouse gas measurement coverage and volume in challenging high-latitude regions while maintaining while maintaining the accuracy of the data.
How to cite: Bharathan, L., Parker, R., Cartwright, M., Orr, D., Di Noia, A., Somkuti, P., Webb, A., and Boesch, H. B.: Expanding high-latitude satellite methane data using a genetic algorithm optimisation technique. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10453, https://doi.org/10.5194/egusphere-egu26-10453, 2026.
Following the Paris Agreement, there has been an increasing need to better understand the global sources and sinks of greenhouse gases (GHG). Most previous space missions aiming to measure GHG concentrations did so using point-based observations of the solar radiation reflected by Earth’s surface (corresponding to the short-wave infrared domain). These measurements – with resolutions in the tens of kilometers – allowed to estimate column average of CO2 and CH4 through the inversion of the radiative transfer equation.
This discrete measurement method presents nonetheless the disadvantage of being limited in spatial coverage and resolution, which entails that the satellite can miss important emission zones such as large cities or power plants. New GHG observation missions therefore use spectro-imagers to provide surface-based measurements: this is the case of the new GOSAT-GW satellite, as well as the exploratory mode (called city mode) of the new MicroCarb satellite. Both were launched in the summer of 2025 and have similar measurement techniques as the upcoming CO2M mission. They will allow to cover large areas of tens of kilometers consistently and with a kilometer-scale precision.
However, analysing such images with the traditionnal method based on optimal estimation is doomed to be too computationally expensive, and there is a need to develop a faster retrieval technique which would allow to reach the same precision level while keeping into consideration constraints such as instrumental noise and limited spectral bands.
The use of machine learning seems to present a convenient solution to this problem: by training neural networks with known atmospheric datasets, the machine learning model can learn to extract the column average of the gases with an inversion of the radiative transfer. This presentation thus aims to present a retrieval method based on a neural network designed using the radiative transfer model 4A/OP and the spectroscopic database GEISA, as well as atmospheric and surface variables and their effect on spectroscopy. The combination with a Bayesian approach will provide an uncertainty for the retrievals through the use of a Bayesian neural network. Exploiting the spatial information contained in the spectral images could further improve the retrieval process.
The method will be applied to two synthetic spectral images which could be observed by the MicroCarb city mode, representing emission plumes of a power plant near Berlin and a factory near Reims, and the performance of the retrieval process will be assessed using both systematic and random estimation errors. A perspective on future missions will also be presented.
How to cite: Corazza, L., Crevoisier, C., Armante, R., and Capelle, V.: Estimation of CO2 total columns from synthetic MicroCarb spectral images using machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12809, https://doi.org/10.5194/egusphere-egu26-12809, 2026.
Accurate quantification of the global carbon cycle is essential for projecting future climate change, yet significant uncertainties remain in partitioning regional land and ocean CO2 fluxes. This study presents a comprehensive evaluation of XCO₂ retrievals from four major satellite missions, such as GOSAT, GOSAT-2, OCO-2 (v11), and OCO-3 (v11), against four Global Carbon Project (GCP) atmospheric transport models (MIROC4-ACTM, COLA, NISMON, and GCASv2). We employ a harmonised approach utilising averaging kernel convolution and data-driven bias correction for the year 2020 to facilitate a consistent model-satellite inter-comparison. Our results demonstrate that while the transport models generally reproduce global and seasonal CO₂ distributions, significant regional biases persist, notably over boreal and tropical land areas where discrepancies often exceed ±2 ppm. We identify that structural differences between satellite observations are primarily attributed to distinct sampling patterns. Specifically, comparisons between the sun-synchronous OCO-2 and the ISS-mounted OCO-3 reveal systematic differences driven by OCO-3's wider range of Local Solar Hour (LSH) sampling. This sampling captures diurnal CO₂ variability that is not fully resolved by current transport models, particularly in regions with strong diurnal cycling. Furthermore, multi-model flux analyses for the 2016–2019 period highlight that the largest uncertainties in surface fluxes occur over the high-latitude oceans and tropical land regions. These flux uncertainties correlate strongly with the observed model-satellite mismatches, underscoring the need for improved representation of diurnal cycles, vertical transport, and surface exchanges in atmospheric inversion systems. This integrated assessment provides crucial diagnostics for advancing the fidelity of global carbon cycle monitoring and modelling.
How to cite: Aparajita, A., Kunchala, R. K., Patra, P. K., and Chandra, N.: Understanding Global CO₂ Fluxes and Concentrations using Multi-Model Simulations and Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-728, https://doi.org/10.5194/egusphere-egu26-728, 2026.
The world invests significant resources every year on remote sensing platforms that aim to quantify the carbon cycle, whether through measurements of atmospheric composition or the land surface. Composition measurements – such as CO₂ and CH₄ – cannot be used directly but must be interpreted through inverse models of different complexity to estimate surface sinks and emissions, from global to urban scales. Flux inversion models aim to quantify the upwind fluxes associated with the downwind concentrations using knowledge of the atmospheric transport between. Therefore, inverse models and their associated inferred flux estimates are critically dependent upon an assumed transport operator.
We use multiple lines of evidence to explore the variability in the global transport of long-lived trace gases like CO2 and SF6. We present results from the CATRINE and OCO-2 SF6 Model Intercomparison Projects (MIPs) which explore the variability in the transport of long lived gases across Chemical Transport Models and their high-resolution General Circulation Model parent models. Additionally, we use the first dual transport atmospheric inversion framework, WOMBATv3, to further explore the relationship between inferred fluxes of CO2 and inversion model transport assumptions, e.g. GEOS-Chem and TM5.
How to cite: Schuh, A.: Evaluating Transport Model Uncertainty on Atmospheric Flux Inversions of CO2, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15598, https://doi.org/10.5194/egusphere-egu26-15598, 2026.
Temperate North America (TNA) consists of moisture-limited ecosystems in the west and mainly forests and croplands in the east, each exhibiting distinct responses of net ecosystem exchange (NEE) to climate variability. A dense in-situ network exists over TNA and is complemented with satellite XCO2 measurements providing strong constraints on sub-continental flux variability, but NEE response to environmental drivers remains poorly understood (Byrne et al., 2020).
In this work, we develop a regional inversion systems that can fully exploit the available observational density to better understand NEE response to climate anomalies on high spatial resolution.
We combine in-situ observations with column-averaged CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT) for a full year. For each observation we compute source-receptor relationships using the Lagrangian Particle Dispersion Model FLEXPART. These footprints serve as the forward model to link surface fluxes to atmospheric measurements. We optimize weekly total surface fluxes on a 2° × 2° grid across TNA and derive NEE by subtracting contributions from fossil fuel emissions, biomass burning, and ocean fluxes.
We assess the sensitivity of inferred NEE to key methodological choices, including model data mismatch errors, assumed spatial and temporal error correlations and the representation of the diurnal cycle in biospheric exchange. For a preferred configuration, we discuss the spatial patterns of NEE in TNA. We also compare the resulting NEE fluxes to those from the global TM5-4DVar inversion and find good agreement in both spatial patterns and temporal variability, while our regional system provides enhanced spatial detail. We conclude by outlining next steps for improvements and discuss opportunities enabled by the high-resolution inversion for diagnosing TNA's carbon–climate processes.
How to cite: Grandke, S., Kühn, P., Metz, E.-M., Lüken-Winkels, C., Basu, S., Butz, A., and Vardag, S. N.: GOSAT and in-situ based inversion of North American CO2 fluxes using FLEXPART , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10102, https://doi.org/10.5194/egusphere-egu26-10102, 2026.
The rapidly warming Arctic is expected to experience significant carbon release as permafrost thaws, yet large uncertainties remain regarding both the rate and the carbon species released (e.g. CO2 versus CH4). As part of the Arctic Methane and Permafrost Challenge, co-led by the European and North American Space Agencies, we present an analysis of current monitoring gaps and future needs, alongside a case study evaluating the capability of existing and future top-down observing systems (including passive and active satellites and atmospheric towers )to detect local changes in CH4 emissions.
This presentation focuses on the top down observing systems, as well as the infrastructure and models required to support them. Currently, XCH4 satellite observations in high latitudes lack coverage during wintertime, nighttime, overcast conditions, and over most ocean regions. While future missions such as MERLIN may alleviate some of these limitations, high-latitude-specific challenges such as large observational angles combined with darkness and snow or ice reflectance are expected to persist for most platforms. Although many years of data are already available from a wide range of observing systems, with a substantial increase anticipated in the near future, the community is not yet prepared to fully utilise these data.
First, we lack data standards that allow both direct CH4 observations and auxiliary variables to be combined optimally, particularly across disciplinary boundaries. To address this, we propose a high-latitude data and model intercomparison experiment. A second major challenge is the sheer volume of data being produced, which will require dedicated infrastructure and enhanced modelling capabilities for effective ingestion and analysis, particularly given the current difficulties in fully utilizing TROPOMI data. Despite significant efforts, major gaps remain in ground-based observations, which are essential for the validation and calibration of satellite and airborne systems.
Translating observations into pan-Arctic methane budgets necessarily involves modelling. In this context, missing information on soil and wetland related characteristics emerges as a key limitation. In addition, the freeze-thaw cycle closely linked to microbial activity and soil moisture remains inadequately monitored. Finally, disturbances are both difficult to observe and predict, yet play a critical role in present-day and future Arctic CH4 emissions.
Overall, we present a roadmap for the research priorities and infrastructure investments required to reliably quantify an integrated Arctic methane budget.
How to cite: Pallandt, M. and the Arctic Methane and Permafrost Challenge team: Monitoring CH4 in high latitudes: gaps and future needs identified through the Arctic Methane and Permafrost Challenge, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19155, https://doi.org/10.5194/egusphere-egu26-19155, 2026.
The role of tropical wetlands in the rise of atmospheric methane over the past two decades remains unclear. Current models define wetlands by surface water coverage, not making the distinction between high-emitting inundated vegetation and much weaker-emitting open water areas of wetlands. Here we use 30-m Landsat satellite data to identify tropical inundated vegetation, and combine it with chamber- and flux tower-derived emission intensities per unit wetland area to produce a Wetland Methane Emission Inventory (WMEI) at 0.1o ×0.1o spatial resolution and quarterly temporal resolution for 2004-2023. We find that tropical wetland emissions increased steadily by 16.3 Tg a-1 or 18% over the 2004-2023 period, with contributions from Africa (11.3 Tg a-1) and Asia (5.0 Tg a-1) and no net increase from South America. Tropical wetlands thus account for 21% of the global methane rise over that period though they do not appear to have contributed significantly to the 2020-2022 methane surge. The long-term trend and interannual variability of wetland emissions correlates strongly with vegetation activity as measured by solar-induced fluorescence (SIF) but not with temperature or precipitation.
How to cite: Chen, Z., Jacob, D., Lin, H., Balasus, N., Hancock, S., Estrada, L., East, J., Zhang, Y., Wang, X., He, M., Liu, M., and Varon, D.: Tropical wetland methane emissions and trends (2004–2023) inferred from Landsat-based inundated vegetation data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8675, https://doi.org/10.5194/egusphere-egu26-8675, 2026.
Quantifying and understanding methane emissions of cities are of great importance since cities are a key focus of current and future mitigation efforts to combat climate change. However, it remains challenging to routinely characterize and verify city scale emission inventories. Previous studies of urban methane emissions have employed a range of flux quantification methods, leading to inconsistent estimates and different associated uncertainties. As a result, a robust and widely applicable framework for quantifying urban emissions remains lacking. In this study, we have developed and tested an advanced emissions calculation method that uses mass balance accounting and satellite observations from TROPOMI to estimate net bulk (city-level) methane emissions and corresponding emissions uncertainties due to the method. We test and validate the method and demonstrate that the novel integration of boundary layer height and hourly-resolved wind data enables a more robust assessment of methane emissions from urban areas compared with methods that do not take such factors into account. Initial assessments with this approach were tested for three megacities (London, Los Angeles and New York) from 2021 to 2023. Our results suggest that emission inventories generally underestimate methane emissions, but by widely varying proportions, and with substantial differences year-to-year. The estimates range from 0.3 to 9.2 times higher than spatially gridded bottom-up inventories. For example, in New York city in 2021, the estimated CH4 emission rate is 43.54 ± 17.77 t h-1, compared with the reported inventory value of 7.18 t h-1 from the U.S. Environmental Protection Agency. In London, the emission estimate is only slightly higher than the NAEI inventory. We also find generally lower but overall consistent emissions when compared with previous top-down studies that use different quantification methods. Our results provide evidence that satellites can serve as a promising technology for ongoing city emissions monitoring, reconciliation and reporting through long-term monitoring across the globe, which can be used to help build methane emission characteristics and track whether stated emission targets are being met.
How to cite: Long, H., Allen, G., Tsivlidou, M., and Ricketts, H.: Satellite-based global monitoring of urban-scale methane emissions using TROPOMI, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6699, https://doi.org/10.5194/egusphere-egu26-6699, 2026.
With ongoing climate change and rising global temperatures, monitoring and quantifying anthropogenic greenhouse gas (GHG) emissions has become increasingly critical. One of the recent activities responding to the needs of accessing the effectiveness of strategies for Carbon Dioxide (CO2) emission reduction is the upcoming Copernicus CO2 Monitoring mission (CO2M), scheduled to launch in 2027.
To support the CO2M, the data-driven emission quantification (ddeq) - Python library was developed as a shared library of various lightweight approaches focusing on quantifying CO2 and NOx emissions from synthetic CO2M data. One of these lightweight approaches is the divergence method, which was originally used for estimating NOx emissions from TROPOMI NO2 retrievals and later applied also for CO2. The divergence method is based on the continuity equation in steady state and requires computing the flux using differentiation. However, unlike the other methods in the ddeq, the divergence method requires temporal averaging to mitigate the noise gained from the numerical differentiation over a noisy data. Unfortunately this makes cross-validation between the divergence and the other methods in the ddeq a challenge.
In the divergence method, it suffices to compute the quantity called advection defined as a dot product between the wind vector and the spatial gradient of the total vertical column density (TVCD).
Traditionally, the gradient is computed using some numerical differentiation scheme, such as a finite difference, but they often unable to produce reasonable estimates for the derivatives in a noisy environment. To solve this issue, we utilized a Gaussian process (GP) to estimate flux of the TVCD. Due to the properties of the GP, the partial derivatives can be computed analytically based on the optimized GP and the chosen kernel function.
Given a noisy measurement z of our signal f at locations s*, we can model the signal f as a zero-mean GP with a covariance kernel K. We require that the kernel function defining the positive definite kernel matrix is (at least) twice differentiable and that the noise is i.i.d. Gaussian with mean 
and variance 
. Mathematically, this can be expressed as
Our objective is to study a linear transformation 
of the signal, where 
is some linear operator. Due to properties of Gaussian processes, is also a Gaussian process. As a consequence, the mean and the covariance matrix of the transformed signal conditionally to the observed data can be computed analytically.
Assuming that the observations of the TVCD near a source are prominent enough, we are able to optimize hyper parameters of a GP. This GP can then used to estimate the advection which should be elevated at the immediate proximity of the source. As per Gauss' divergence theorem, the emission rate of a source can be computed by integrating the advection field over the vicinity of the point source.
How to cite: Koskinen, A., Nurmela, J., Härkönen, T., and Tamminen, J.: A divergence method approach utilizing gaussian processes for carbon dioxide emission estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11354, https://doi.org/10.5194/egusphere-egu26-11354, 2026.
There is increasing interest in using uncrewed aerial vehicles (UAVs) to make measurements of atmospheric trace gases. However, integrating these observations with atmospheric models is challenging because of the often high spatial and temporal resolution of the UAV observations and the relatively coarse resolution of atmospheric models. This scale mismatch is particularly challenging for inverse modeling of surface emissions of environmentally important trace gases, such as carbon dioxide (CO2), when using these data. Consequently, studies estimating CO2 emissions from UAV observations typically use a mass balance or Gaussian plume inverse modeling approach. Here we quantify CO2 emissions based on UAV observations from an offshore oil and gas facility by explicitly modeling atmospheric transport processes using a large-eddy simulation (LES) with the Weather Research and Forecasting (WRF) model. In situ CO2 observations were made by the Airborne Ultralight Spectrometer for Environmental Application (AUSEA) sensor on a UAV and the data were incorporated into a Bayesian inversion approach with WRF-LES simulations at a spatial resolution of 10 m. The reported emissions from the facility at the time of the UAV measurements were 42–48 tCO2/h. Starting from prior estimates that ranged from 30 ± 20 tCO2/h to 60 ± 40 tCO2/h, the inversion results suggested posterior emission estimates from 35.6 ± 5.8 tCO2/h to 43.7 ± 7 tCO2/h, respectively, which is a range that is consistent with the reported emissions. We also used the mass balance method and estimated emissions of 38.4 ± 14.4 tCO2/h, which are in agreement with the Bayesian inversion results as well as the reported emission estimates. Our results demonstrate the potential utility of high-resolution modeling in the context of the Bayesian inversion analysis to estimate point source emissions using UAV observations.
How to cite: Xiao, Z., Jones, D., Blanco, B., Fathi, S., and Quettier, A.: Inverse Modeling of CO2 Emissions from Point Sources using Large-Eddy Simulations with Observations from Uncrewed Aerial Vehicles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14237, https://doi.org/10.5194/egusphere-egu26-14237, 2026.
Using Satellite and Lagrangian Modeling to Estimate Urban CO2 Emissions in HongkongYue Zhou and Hui SuUrban areas are responsible for emitting more than 70% of global fossil fuel carbon dioxide (CO2) emissions. Hongkong is one of the most world’s populated cities and accurate inversion of CO2 emissions using satellite form this region remains high uncertainty. However, urban CO₂ emissions are mainly derived from satellite using simple statistical methods, and there is less research to link between upwind emission sources and downwind CO₂ enhancements using models.To better understand of urban emission estimation from space, we use X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT”) (Wu et al., 2018) to simulate XCO2 enhancements in the summer of 2024 based on the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission inventory for Hongkong. Independent satellite observations from NASA’s Orbiting Carbon Observatory‐3 (OCO‐3) satellite can provide wide area column average dry air mole fraction of carbon dioxide (XCO2) of entire urban areas. Furthermore, we perform a CarbonTracker-Lagrange inverse model to compare XCO2 simulations with observations. We found X-STILT model is able to reproduce most XCO2 enhancement observations. This study provides valuable insights into both urban emissions quantifying and mitigation decision making.References: Wu et al.,: A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian
Transport model (“X-STILT v1”)
How to cite: Zhou, Y. and Su, H.: Using Satellite and Lagrangian Modeling to Estimate Urban CO2 Emissions in Hongkong, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15573, https://doi.org/10.5194/egusphere-egu26-15573, 2026.
Recent advancements in satellite-based CO₂ observations have significantly improved our ability to quantify local and urban emissions. In recent years, methodological advancements have included linking total column CO₂ (XCO2) observations to emission sources, deriving emission estimates that are less dependent on prior inventories, using solar-induced fluorescence (SIF) to distinguish between biospheric and anthropogenic fluxes in cities, and resolving fine-scale urban CO₂ gradients to attribute emissions to specific sectors. Together, these developments lead to more accurate and reliable emission estimates from space in urban areas.
Long term OCO-2 and OCO-3 XCO2 measurements have been particularly valuable in advancing urban CO₂ studies. Further, OCO-3’s Snapshot Area Mapping (SAM) observations provide high-density CO₂ measurements over targeted areas, improving our understanding of emissions from entire cities down to individual point sources. Techniques such as Gaussian plume modeling, cross-sectional flux methods, and integrated mass enhancement allow to analyze these measurements in great detail. Additionally, using multi-species approaches and combining SAM data with collocated observations of nitrogen dioxide (NO2), carbon monoxide (CO), and other atmospheric gases from space- and ground-based sensors have the potential to provide insights into combustion efficiencies, sector-specific emissions, and more.
This presentation will cover recent advances and studies in urban CO₂ monitoring, focusing on how OCO-2 and OCO-3 observations and current methods are contributing to the development of independent satellite-based greenhouse gas measurement, reporting, and verification (MRV) systems, and discussing the limitations of current systems.
How to cite: Kiel, M., Chatterjee, A., Ahn, D., Lin, J., Roten, D., and Payne, V.: Advancements in Satellite-Based CO2 Monitoring: OCO-2 and OCO-3 Observations and Their Role in Urban Emission Quantification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22834, https://doi.org/10.5194/egusphere-egu26-22834, 2026.
During the ASIA-AQ international field campaign in February 2024, ground-based greenhouse gas observations were conducted at urban and background sites in South Korea. Portable Fourier Transform Infrared (FTIR) spectrometers, EM27/SUN, as a component of the COllaborative Carbon Column Observing Network (COCCON), were deployed at Ewha Womans University and Olympic Park in Seoul to measure column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4). In parallel, a high-resolution stationary FTIR spectrometer, the IFS125HR, operated as a reference instrument of the Total Carbon Column Observing Network (TCCON), conducted background observations at Anmyeondo. Prior to the campaign, a side-by-side intercomparison involving two EM27/SUN instruments and the IFS125HR was carried out at Anmyeondo to assess instrument consistency. The results showed that temporal concentration variability and short-term enhancement events were consistently captured by all three instruments, although systematic biases were identified among them. To align the EM27/SUN measurements with the TCCON reference scale, calibration factors for both EM27/SUN instruments were derived based on comparisons with the IFS125HR and subsequently applied to the measurements acquired during the campaign period. During the campaign, three days of coincident observations among the three instruments were obtained. Analysis of these data revealed that both XCO2 and XCH4 exhibited the highest concentrations at Ewha Womans University, followed by Olympic Park and Anmyeondo. In addition, daily variability of greenhouse gas concentrations differed by site and varied from day to day. Following the campaign, extended EM27/SUN observations were conducted at Ewha Womans University through May 2024 and were used to evaluate consistency with satellite retrievals. Comparisons with S5P TROPOMI showed that XCH4 agreed within approximately 2% and exhibited a correlation coefficient of 0.68. This study shows that ground-based XCO2 and XCH4 observations obtained during the study period are meaningful for intercomparison among ground-based sites and comparison with satellite observations.
How to cite: Kang, M., Ahn, M.-H., Kim, S., Oh, Y.-S., and Lee, J.: Ground-Based FTIR Observations of XCO2 and XCH4 during the ASIA-AQ Campaign in Seoul, South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16229, https://doi.org/10.5194/egusphere-egu26-16229, 2026.
High-resolution ground-based Fourier Transform InfraRed (FTIR) spectroscopy is a key technique for monitoring atmospheric composition, in particular, greenhouse gases, providing vertically integrated information essential for climate studies, emission estimates, and satellite data validation. Within the framework of the PNRR EMM and ITINERIS projects, two new ground-based FTIR spectrometers have been installed at the CNR research area in Bologna, Italy, significantly enhancing the national observational capability in these fields.
The first instrument, a Bruker IFS 125HR, operates in the infrared spectral range from approximately 850 to 9000 cm⁻¹, with a spectral resolution of about 0.0036 cm⁻¹, enabling the retrieval of a wide set of atmospheric trace gases and minor atmospheric constituents, including CO₂, CH₄, N₂O, CO, O₃, HNO₃, HCl, HF, C₂H₆, HCN, and HDO. These measurements are crucial for investigating changes in atmospheric composition and their impact on the Earth’s radiative balance, as well as for deriving greenhouse gas emissions and trends.
The second instrument is a Bruker EM27/SUN, which operates in the NIR range from 4000 to 12000 cm⁻¹, thus allowing the retrieval of CO2, CH4, CO, and H2O. Being part of the COCCON network, its data are analyzed on a daily basis, and the resulting GEOMS files are regularly delivered to the network for public distribution.
The FTIR spectrometers are located in the Po Valley, one of the most polluted regions in Europe, offering a unique opportunity to observe greenhouse gases and air pollutants under conditions of strong anthropogenic influence. The IFS 125HR is fully compliant with the NDACC and TCCON network requirements, ensuring standardized data acquisition, calibration, and processing procedures and thereby allowing direct comparison with other ground-based FTIR sites worldwide and with satellite observations. This installation represents the first FTIR facility of its kind in Italy and the central Mediterranean region, providing significant scientific value at both national and international levels.
How to cite: Papandrea, E., Castelli, E., Pettinari, P., Achilli, A., and Calzolari, F.: A New NDACC- and TCCON-Compliant FTIR Observatory in Bologna (Italy) for Greenhouse Gas and Trace Gas Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11360, https://doi.org/10.5194/egusphere-egu26-11360, 2026.
The UK has a long-term goal in place to achieve net-zero greenhouse gas (GHG) emissions by 2050. As part of the UK Greenhouse gas Emissions Measurement Modelling Advancement programme (GEMMA), which aims to provide regular, timely, data-driven emissions estimates for the UK at a regional scale, scientists from the National Centre for Earth Observation have set up a network of ground-based shortwave infrared spectrometers around the UK. This network, called GEMINI-UK (Greenhouse gas Emissions Monitoring network to Inform Net-zero Initiatives for the UK), will provide continuous observations of the column concentrations of carbon dioxide and methane during cloud-free conditions from locations around the country.
Through the GEMMA programme data from GEMINI-UK will be used in a Bayesian inversion framework, along with other sources of GHG concentration data in the UK, to constrain regional flux estimates of carbon dioxide and methane. We have designed GEMINI-UK to deliver the biggest uncertainty reductions in carbon dioxide flux estimates, working closely with host partners that include UK universities, a research institute and a secondary school to promote the open access and transparency of the collected data. The network comprises ten new Bruker EM27/SUN spectrometers, which we operate in automated weatherproof enclosures using a design developed by University of Edinburgh researchers, allowing year-round autonomous observations across multiple sites.
In this presentation we describe the status, network design, first data, and longer-term goals of GEMINI-UK, including an ongoing evaluation of the GEMINI-UK station located alongside the high resolution TCCON (Total Carbon Column Observing Network) spectrometer at the Rutherford Appleton Laboratory in Harwell, Oxfordshire. In addition, we show the potential for GEMINI-UK data to constrain carbon dioxide and methane fluxes for the UK using a regional Bayesian inversion framework, and demonstrate the opportunities that GEMINI-UK provides for regional scale validation of existing and future greenhouse gas observing satellite missions.
How to cite: Humpage, N., Palmer, P., Kurganskiy, A., Feng, L., Woodwark, J., Morrison, W., Doniki, S., Weidmann, D., Parker, R., and Bharathan, L.: GEMINI-UK: Towards improved carbon flux estimates for the UK using a national network of ground-based greenhouse gas observing spectrometers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18382, https://doi.org/10.5194/egusphere-egu26-18382, 2026.
Satellite remote sensing of global greenhouse gas (GHG) concentrations provides invaluable information about GHG sources and sinks, supporting efficient climate mitigation policies. Recently, the accuracy targets of upcoming GHG satellite missions have become increasingly stringent (±2 ppb for CH4; ±1 ppm for CO2).
Up to now, calibration and traceability of satellite GHG observations relies on two networks of ground-based remote sensing stations: the Total Carbon Column Observing Network (TCCON) and the COllaborative Carbon Column Observing Network (COCCON). Both networks are able to observe the same quantity as the satellite instruments: column-averaged dry-air mole fraction of CO2 and CH4. They also observe N2O, which will likely become another key GHG to be monitored in the future. For traceability, both networks rely on regular aircraft and balloon measurements with in-situ instruments that are traceable to the WMO scale for GHGs.
In 2025, the new project 24GRD06 MetCTG was started to improve the traceability of remote-sensing observations of atmospheric greenhouse gases, both from the ground as well as from satellites. This will be achieved by greatly improving the accuracy of the underlying spectral line parameters through theoretical work as well as in the lab. Methods will be established to make these improved parameters SI-traceable from end to end. The results will be validated in the field with in situ and ground-based observations. This will establish an alternative traceability chain to SI for ground-based and satellite retrievals. In the long term, the work should improve the consistency among ground-based sites and reduce the need for costly aircraft calibrations. Satellite GHG missions, which rely on these ground-based observations for calibration and validation, will benefit from an improved data comparability across missions and products.
More information about the 24GRD06 MetCTG project is available at https://www.metctg.ptb.de/.
Acknowledgments: The project 24GRD06 MetCTG receives funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.
How to cite: Feist, D. G. and the MetCTG Team: A new way to implement SI-traceability in greenhouse gas remote-sensing observations: the Metrology for Comparable and Trustworthy Greenhouse gas remote sensing datasets (24GRD06 MetCTG) project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14207, https://doi.org/10.5194/egusphere-egu26-14207, 2026.
Current spectrometers provide high-quality absorption spectra from both ground-based direct sun measurements and spaceborne backscatter measurements. Accurate retrievals of atmospheric CO2 concentrations from these measured spectra are fundamental for modelling large-scale atmosphere-surface exchange fluxes. When retrieving CO2 concentrations from measured spectra, high-quality spectroscopic reference data are essential to drive radiative transfer simulations and to enable accurate retrievals. Here, we investigate how various modern molecular absorption cross-section datasets affect CO2 retrievals in the 1.6 μm and 2 μm wavelength regions. This includes recent parameter sets derived from laboratory measurements at the German Aerospace Center (DLR e.V.) for line-mixing parameterizations (Birk et al., 2024) with separate continuum data. We compare these new data to those from HITRAN 2020 (Gordon et al., 2022) with and without speed-dependent Voigt profile extension as well as to the ABSCO tables (Benner et al., 2016), (Devi et al., 2016).
To evaluate the quality of the spectroscopic databases, we submit high-resolution direct-sun spectra collected by the TCCON (Total Carbon Column Observing Network) spectrometer at Karlsruhe to our RemoTeC retrieval algorithm under variation of the driving spectroscopic parameters. We evaluate systematic spectral residuals as well as spurious dependencies of the retrieved CO2 columns on slant airmass. We further retrieve CO2 separately from the P- and R-branches within a spectral window to assess potential mismatches. In addition, we use one year of GOSAT satellite measurements to investigate whether and how differences in CO2 concentrations retrieved under variation of the spectroscopic parameters show dependencies on geophysical parameters such as latitude, season or surface type. Our analyses show that the DLR cross-sections lead to noticeable improvements in spectral line modelling in the strong 2 µm band, which in turn reduces airmass dependencies. Including the CO2 continuum from the DLR dataset further reduces airmass-dependent biases, although this improvement is not reflected in the spectral residuals. Using the ABSCO tables results in residuals comparable to those using the HITRAN 2020 cross-sections. However, the airmass bias is low and comparable to the DLR cross-sections. In the weaker 1.6 µm bands, fit quality is comparable across all datasets, with small differences in airmass dependence. For the 2 μm band, the comparison of P- and R-branch retrievals reveals differences of up to 0.2 % and a pronounced airmass-dependent bias in the HITRAN 2020 R-branch. This inter-branch difference vanishes in the 1.6 μm bands when using DLR cross-sections but persists for HITRAN. Furthermore, retrievals with DLR cross-sections show significantly improved agreement between the 2 µm and 1.6 μm bands compared to HITRAN. In the GOSAT analysis, in addition to airmass-dependent effects leading to latitudinal and seasonal biases, we found surface albedo to strongly correlate with differences in retrieved CO2 concentrations.
Benner et al., 2016: https://doi.org/10.1016/j.jms.2016.02.012
Devi et al., 2016: https://doi.org/10.1016/j.jqsrt.2015.12.020
Birk et al., 2024: https://elib.dlr.de/208834/
Gordon et al., 2022: https://doi.org/10.1016/j.jqsrt.2021.107949
How to cite: Thiemann, L., Sindram, M. M., Schmitt, T. D., Birk, M., Röske, C., Wagner, G., and Butz, A.: Investigating the Impact of Modern Absorption Cross-Section Databases on CO2 Retrievals , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6537, https://doi.org/10.5194/egusphere-egu26-6537, 2026.
As of late 2025, the GHGSat constellation has expanded to 14 high-resolution (~25 m GSD) methane-sensitive satellites capable of detecting, attributing, and quantifying emissions down to ~100 kg/hr. To enhance the actionable value of these observations, current research focuses on refining source rate quantification and deepening the understanding of the parameters that dictate detection limits in diverse environments.
We are characterizing the influence of observation geometry, wind speed, and retrieval noise on the probability of detection (PoD) for specific source rates. By isolating these factors, we aim to provide more site-specific performance metrics across the global constellation. Simultaneously, to improve quantification accuracy, we are investigating how wind field variability, local elevation, and source geometry (e.g., point vs. area sources) affect plume transport and subsequent flux estimates. This integrated approach is particularly critical for characterizing emissions in complex industrial environments like landfills.
Finally, we provide a status update on the GHGSat fleet, including our dedicated CO₂ sensor and an overview of upcoming satellite launches.
How to cite: Strupler, M., Deslieres, A., Girard, M., Jervis, D., MacLean, J.-P. W., Marshall, D., Mckeever, J., Ramier, A., and Tarrant, E.: Greenhouse Gas Emission Monitoring with the GHGSat Constellation: Advancing High-Resolution Methane Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14349, https://doi.org/10.5194/egusphere-egu26-14349, 2026.
A joint retrieval algorithm for CO₂ and CH₄ concentrations is presented for the Multi‑spectral Imaging Carbon Observatory (MUSICO). This instrument is based on a Fabry‑Pérot (FP) imaging spectrometer to be deployed on the Chinese Space Station in 2026, and is designed to quantify facility‑scale emissions at 100 m spatial resolution. MUSICO acquires high‑resolution spectra in the CO₂ (1595–1620 nm) and CH₄ (1630–1655 nm) bands.
A central challenge for point-source imaging is systematic bias from atmospheric aerosol scattering, which perturbs top-of-atmosphere radiance and degrades gas retrievals. We address this within an optimal-estimation framework that integrates aerosol correction by combining multi-angle observations, enabled by steerable pointing, with auxiliary channels (O₂ near 765 nm and aerosol-sensitive bands at 440, 685, and 865 nm). This synergy improves the separation of aerosol-induced radiance perturbations from true gas absorption.
The forward model is based on MODTRAN and includes spectrally resolved gas absorption, surface reflectance, and multiple scattering. End-to-end simulations show that the integrated methodology effectively mitigates aerosol-induced biases and enables the mission to meet its accuracy targets for monitoring anthropogenic point sources.
How to cite: Pan, F., Zhai, C., and Su, H.: Joint Retrieval of CO₂ and CH₄ Concentrations for the Multi‑Spectral Imaging Carbon Observatory (MUSICO), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17022, https://doi.org/10.5194/egusphere-egu26-17022, 2026.
Carbon dioxide (CO₂) and methane (CH₄) are the two most important anthropogenic greenhouse gases and are the primary drivers of ongoing climate change. Satellite-based remote sensing of their column-average dry-air mole fractions (XCO2 and XCH4) contributes to an improved understanding of the climate system and natural carbon fluxes, enables the quantification of anthropogenic emissions, and supports the monitoring of emission reduction measures. Many of these applications have demanding requirements on the accuracy of the underlying satellite data. In particular, climate studies benefit from long-term climate data records with high inter-sensor consistency.
For decades, climate modellers use ensemble approaches to calculate the ensemble median and to estimate uncertainties of climate projections where no ground-truth is available. Following this concept, the EnseMble Median Algorithm (EMMA) enables the combination of multiple XCO2 and XCH4 data sets from different satellite instruments into a single, consistent data product with high accuracy and quantified uncertainties. Since 2016, EMMA-based XCO2 and XCH₄ climate data records have been generated and made publicly available within the framework of the Copernicus Climate Change Service (C3S).
The latest EMMA version, v5.1, represents a significant update of the algorithm. It enables, for the first time, the meaningful integration of very large data sets, such as those from Sentinel-5P, and allows the generation of data products suitable also for the analysis of small-scale emission sources. Our presentation, will introduce the updated algorithm, the generated XCO2 and XCH4 climate data records, and present validation results.
How to cite: Reuter, M., Fuentes Andrade, B., Buchwitz, M., Noël, S., Hilker, M., Schneising, O., Bovensmann, H., and Bösch, H.: Satellite-derived XCO2 and XCH4 climate data records generated with the latest version of the EnseMble Median Algorithm (EMMA), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20749, https://doi.org/10.5194/egusphere-egu26-20749, 2026.
Tango consists of two CubeSat platforms, Tango-Carbon and Tango-Nitro, flying in formation.
NO2, while not a greenhouse gas itself, can serve as a constraint on CO2 emissions, because both NO2 and CO2 are frequently co-emitted from similar combustion processes. Due to the low atmospheric background concentration of NO2 better detection of CO2 emission plumes is possible.
Analysis of NO2 levels provides additional information about the magnitude of CO2 emissions.
Detector requirements of the Nitro and Carbon instruments are analyzed using The Tango End-to-end simulator. It consists of a suite of modules that address all aspects of the mission data flow.
In this contribution we will describe the NO2 branch of the simulator.
How to cite: van Hoek, M., Veefkind, P., Landgraf, J., Ludewig, A., Leune, B., van Amelrooy, E., Borsdorff, T., Laasner, R., van Hees, R., Cooney, R., Louzada, K., Alpay Koc, N., de Groejj, B., van der Gron, H. D., de Groot, Z., and Marasini, C.: Tango mission NO2 End-to-end simulator, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21363, https://doi.org/10.5194/egusphere-egu26-21363, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 5
EGU26-1603 | ECS | Posters virtual | VPS3
BASIC: A Boosted Aerosol-Size-Integrated XCO2 Retrieval AlgorithmTue, 05 May, 14:00–14:03 (CEST) vPoster spot 5
Accurate retrieval of the dry-air mole fraction of CO₂ (XCO₂) is essential for tracking emissions and supporting mitigation. However, aerosols significantly alter photon path lengths through scattering and absorption, making them the largest variable error source in XCO₂ retrieval. Current efforts to improve aerosol treatment in XCO₂ retrievals, such as the Atmospheric CO₂ Observations from Space (ACOS) algorithm for the Orbiting Carbon Observatory-2 (OCO-2), primarily focus on enhancing prior estimates of aerosol optical depth (AOD), vertical distribution, and optical properties. Yet, aerosol particle size distribution (PSD) parameters—a critical microphysical factor contributing to nonlinear variations in aerosol optical properties—are held fixed and excluded from the ACOS retrieval, thereby introducing additional biases into the XCO₂ results.
To address this challenge, we developed a Boosted Aerosol-Size-Integrated XCO₂ (BASIC) retrieval algorithm that concurrently retrieves XCO₂ and aerosol PSD parameters from OCO-2 observations. Validation at five Total Carbon Column Observing Network (TCCON) sites in East Asia shows that BASIC reduces the root-mean-square error (RMSE) by 30% and 13% compared to the standard and bias-corrected OCO-2 products, respectively. The improvement primarily stems from BASIC’s ability to generate forward-modeled spectra that more closely match observations than those from ACOS, particularly in the O₂ A-band, which is highly sensitive to aerosols. These results highlight the importance of incorporating variable aerosol PSD in retrievals and demonstrate that BASIC more accurately represents aerosol effects on radiative transfer. Our findings suggest that PSD-aware retrievals can significantly improve the accuracy of satellite-derived XCO₂ estimates under highly variable aerosol loading conditions, such as those in East Asia.
How to cite: Li, Z. and Li, S.: BASIC: A Boosted Aerosol-Size-Integrated XCO2 Retrieval Algorithm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1603, https://doi.org/10.5194/egusphere-egu26-1603, 2026.
