Articles | Volume 15, issue 16
https://doi.org/10.5194/amt-15-4835-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-4835-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2022
Research article |
| 25 Aug 2022
| 25 Aug 2022
The Space Carbon Observatory (SCARBO) concept: assessment of XCO2 and XCH4 retrieval performance
Matthieu Dogniaux
CORRESPONDING AUTHOR
Laboratoire de Météorologie Dynamique/IPSL, CNRS,
École polytechnique, Institut Polytechnique de Paris, Sorbonne
Université, École Normale Supérieure, PSL Research
University, 91120 Palaiseau, France
Cyril Crevoisier
Laboratoire de Météorologie Dynamique/IPSL, CNRS,
École polytechnique, Institut Polytechnique de Paris, Sorbonne
Université, École Normale Supérieure, PSL Research
University, 91120 Palaiseau, France
Silvère Gousset
Institut de Planétologie et d'Astrophysique de Grenoble,
Université Grenoble-Alpes, 38058 Grenoble, France
Étienne Le Coarer
Institut de Planétologie et d'Astrophysique de Grenoble,
Université Grenoble-Alpes, 38058 Grenoble, France
Yann Ferrec
ONERA/DOTA, BP 80100, chemin de la Hunière, 91123 Palaiseau,
France
Laurence Croizé
ONERA/DOTA, BP 80100, chemin de la Hunière, 91123 Palaiseau,
France
Lianghai Wu
SRON Netherlands Institute for Space Research, Niels Bohrweg 4, 2333 CA Leiden, the Netherlands
now at: Remote
Sensing Unit, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium
Otto Hasekamp
SRON Netherlands Institute for Space Research, Niels Bohrweg 4, 2333 CA Leiden, the Netherlands
Bojan Sic
NOVELTIS, 31670 Labège, France
Laure Brooker
Airbus Defence and Space, 31 rue des Cosmonautes, 31402 Toulouse,
France
Related authors
No articles found.
Zihao Yuan, Bastiaan van Diedenhoven, Guangliang Fu, J. Vanderlei Martins, Andrew M. Sayer, Chamara Rajapakshe, Brian Cairns, Hai Xiang Lin, Jan Willem Erisman, and Otto P. Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2026-5631, https://doi.org/10.5194/egusphere-2026-5631, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This paper presents a systematic intercomparison of cloud droplet effective radius and effective variance from the PACE instruments SPEXone, HARP-2, and OCI. Comparison of one-year data shows reasonable consistency, but also show clear systematic differences. The comparison establishes a baseline understanding of PACE cloud retrieval performance, gives insight into retrieval uncertainties, and provides a guideline for using multi-instrument PACE data sets in aerosol–cloud interaction studies.
Victor Bon, Cyril Crevoisier, and Virginie Capelle
Atmos. Chem. Phys., 26, 13103–13119, https://doi.org/10.5194/acp-26-13103-2026, https://doi.org/10.5194/acp-26-13103-2026, 2026
Short summary
Short summary
To understand how fires influence climate, we need to understand the transport of their emissions. Using satellite observations, we detected day-to-day evolution of carbon dioxide high in the atmosphere (8–11 km) and traced the air back in time to identify fire sources. We showed that carbon dioxide and fire activity daily variability were strongly linked. Also, comparing observation data and a widely used modelling system showed model failed to reproduce the largest events.
Fiona Lippert, Andrew Gerald Barr, Marcos Herreras-Giralda, Masahiro Momoi, Fernando Rejano, Sha Lu, Otto Hasekamp, Oleg Dubovik, Edward Malina, and Jochen Landgraf
EGUsphere, https://doi.org/10.5194/egusphere-2026-4724, https://doi.org/10.5194/egusphere-2026-4724, 2026
Short summary
Short summary
Retrieving greenhouse gas concentrations from satellite measurements is computationally demanding. We show that machine learning can greatly accelerate this process while maintaining high accuracy and interpretability. Combining simplified atmospheric simulations with neural networks proved substantially more reliable than using machine learning alone. This hybrid approach could facilitate near-real time processing of rapidly increasing data volumes.
Nicolas Cézard, Matthieu Valla, Laurent Lombard, Béatrice Augere, Julien Lahyani, William Patiño-Rosas, Didier Goular, Christophe Planchat, Agnès Dolfi-Bouteyre, Anne Durecu, François Gustave, Anasthase Liméry, Mathieu Quatrevalet, Elias Wolkiewiez, and Cyril Crevoisier
EGUsphere, https://doi.org/10.5194/egusphere-2026-4286, https://doi.org/10.5194/egusphere-2026-4286, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Wind is an essential physical variable in atmospheric physics. The paper reports on high resolution wind measurements performed with a long-range airborne Coherent Doppler Lidar, during a campaign that took place in summer 2021, in France and in the Arctic. Measurements are found to be in close agreement with radiosondes data as well as with modelled wind fields. This lidar appears as a promising tool for campaigns dedicated to climate modelling and atmospheric dynamics.
Hailing Jia, David Neubauer, Yusuf Bhatti, Duncan Watson-Parris, Philip Stier, Johannes Quaas, Daniel Partridge, Ardit Arifi, Anne Kubin, Athanasios Nenes, Muhammed Irfan, Ulas Im, Carl Svenhag, Nick Schutgens, Bastiaan van Diedenhoven, Sylvaine Ferrachat, Ulrike Lohmann, Ina Tegen, Alice Henkes, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2026-3275, https://doi.org/10.5194/egusphere-2026-3275, 2026
Short summary
Short summary
Tiny aerosol particles affect Earth’s climate by interacting with clouds and sunlight, but their impact remains uncertain. Combining climate model simulations and emulators, we explored millions of model variants across different aerosol and cloud settings to identify the causes of the uncertainty. We found that aerosol emissions and cloud convection are key contributors. Our work suggests that better integration of existing observations could help reduce these uncertainties in the future.
Nick Schutgens, Elisabeth J. Andrews, Antti Arola, Yusuf Bhatti, Guanliang Fu, Otto Hasekamp, Pekka Kolmonen, Antti Lipponen, Tero Mielonen, Leighton Reygare, and Andrew M. Sayer
EGUsphere, https://doi.org/10.5194/egusphere-2026-2656, https://doi.org/10.5194/egusphere-2026-2656, 2026
Short summary
Short summary
Aerosol are fine particles in the air that affect public health and the climate. We develop a new method to study aerosol through patterns (called autocorrelations) in their time-series. We show that autocorrelations are linked to aerosol deposition processes and contain information on the important but uncertain aerosol lifetime. Autocorrelations can be observed from satellites and we provide the first ever global climatology of aerosol autocorrelations.
Florian Pignol, Sebastien Dorgan, Pablo Jais, Frédéric Romand, Gregoire Hein, Vincent Cassé, Raymond Armante, Nicolas Meilhac, and Cyril Crevoisier
EGUsphere, https://doi.org/10.5194/egusphere-2026-1269, https://doi.org/10.5194/egusphere-2026-1269, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Methane is a potent greenhouse gas, and emissions from industrial facilities are a significant driver of climate change. We present the GESat GEN1 mission that has been developed as a compact satellite system to detect and monitor such emissions from space. It relies on a miniaturized imaging instrument capable of producing high-resolution observations that allow methane plumes as small as 100kg/h to be traced back to their industrial point sources.
Yusuf A. Bhatti, Duncan Watson-Parris, Leighton A. Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Muhammed Irfan, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto P. Hasekamp
Atmos. Chem. Phys., 26, 269–293, https://doi.org/10.5194/acp-26-269-2026, https://doi.org/10.5194/acp-26-269-2026, 2026
Short summary
Short summary
Aerosols (small airborne particles) impact Earth's climate, but their extent is unknown. By running climate model simulations and using machine learning to emulate millions of additional variants with different settings, we found that natural emissions like sea spray and sulfur are key sources of uncertainty in climate predictions. Our work shows that understanding these natural processes better can help improve climate models and make future climate projections more accurate.
Félix Langot, Cyril Crevoisier, Thomas Lauvaux, Charbel Abdallah, Jérôme Pernin, Xin Lin, Marielle Saunois, Axel Guedj, Thomas Ponthieu, Julien Moyé, Michel Ramonet, Anke Roiger, Klaus-Dirk Gottschaldt, and Alina Fiehn
Atmos. Meas. Tech., 18, 5955–5983, https://doi.org/10.5194/amt-18-5955-2025, https://doi.org/10.5194/amt-18-5955-2025, 2025
Short summary
Short summary
Our study compares outputs from meteorological and atmospheric composition models to data from the MAGIC2021 campaign that took place in Sweden. Our results highlight performance differences among models, revealing strengths and weaknesses of different modelling techniques. We also found that wetland emission inventories overestimated emissions in regional simulations. This work helps to refine methane emission predictions, essential for understanding climate change.
Gerald Wetzel, Anne Kleinert, Sören Johansson, Felix Friedl-Vallon, Michael Höpfner, Jörn Ungermann, Tom Neubert, Valéry Catoire, Cyril Crevoisier, Andreas Engel, Thomas Gulde, Patrick Jacquet, Oliver Kirner, Erik Kretschmer, Thomas Kulessa, Johannes C. Laube, Guido Maucher, Hans Nordmeyer, Christof Piesch, Peter Preusse, Markus Retzlaff, Georg Schardt, Johan Schillings, Herbert Schneider, Axel Schönfeld, Tanja Schuck, Wolfgang Woiwode, Martin Riese, and Peter Braesicke
Atmos. Meas. Tech., 18, 5873–5894, https://doi.org/10.5194/amt-18-5873-2025, https://doi.org/10.5194/amt-18-5873-2025, 2025
Short summary
Short summary
We present vertical trace gas profiles from the first balloon flight of the newly developed GLORIA-B limb-imaging Fourier-Transform spectrometer. Longer-lived gases are compared to external measurements to assess the quality of the GLORIA-B observations. Diurnal changes of photochemically active species are compared to model simulations. GLORIA-B demonstrates the capability of balloon-borne limb imaging to provide high-resolution vertical profiles of trace gases up to the middle stratosphere.
Zihao Yuan, Guangliang Fu, Hai Xiang Lin, Jan Willem Erisman, and Otto P. Hasekamp
Atmos. Meas. Tech., 18, 5415–5434, https://doi.org/10.5194/amt-18-5415-2025, https://doi.org/10.5194/amt-18-5415-2025, 2025
Short summary
Short summary
This work develops an Neural-Network-based above cloud aerosol (ACA) detection and retrieval scheme for multi-angular polarimetric (MAP) instruments. On one year of the retrieval, the retrieved aerosol properties (aerosol optical thickness, AOT, Angstrom Exponent, AE, and Single Scattering Albedo, SSA) agree well with adjacent clear-sky aerosol retrievals. The seasonal global pattern of ACA events and above cloud AOT are also within expectation.
Matthieu Dogniaux and Cyril Crevoisier
Atmos. Meas. Tech., 17, 5373–5396, https://doi.org/10.5194/amt-17-5373-2024, https://doi.org/10.5194/amt-17-5373-2024, 2024
Short summary
Short summary
Many CO2-observing satellite concepts, with very different design choices and trade-offs, are expected to be put into orbit during the upcoming decade. This work uses numerical simulations to explore the impact of critical design parameters on the performance of upcoming CO2-observing satellite concepts.
Zihao Yuan, Guangliang Fu, Bastiaan van Diedenhoven, Hai Xiang Lin, Jan Willem Erisman, and Otto P. Hasekamp
Atmos. Meas. Tech., 17, 2595–2610, https://doi.org/10.5194/amt-17-2595-2024, https://doi.org/10.5194/amt-17-2595-2024, 2024
Short summary
Short summary
Currently, aerosol properties from spaceborne multi-angle polarimeter (MAP) instruments can only be retrieved in cloud-free areas or in areas where an aerosol layer is located above a cloud. Therefore, it is important to be able to identify cloud-free pixels for which an aerosol retrieval algorithm can provide meaningful output. The developed neural network cloud screening demonstrates that cloud masking for MAP aerosol retrieval can be based on the MAP measurements themselves.
Otto Hasekamp, Pavel Litvinov, Guangliang Fu, Cheng Chen, and Oleg Dubovik
Atmos. Meas. Tech., 17, 1497–1525, https://doi.org/10.5194/amt-17-1497-2024, https://doi.org/10.5194/amt-17-1497-2024, 2024
Short summary
Short summary
Aerosols are particles in the atmosphere that cool the climate by reflecting and absorbing sunlight (direct effect) and changing cloud properties (indirect effect). The scale of aerosol cooling is uncertain, hampering accurate climate predictions. We compare two algorithms for the retrieval of aerosol properties from multi-angle polarimetric measurements: Generalized Retrieval of Atmosphere and Surface Properties (GRASP) and Remote sensing of Trace gas and Aerosol Products (RemoTAP).
Zihan Zhang, Guangliang Fu, and Otto Hasekamp
Atmos. Meas. Tech., 16, 6051–6063, https://doi.org/10.5194/amt-16-6051-2023, https://doi.org/10.5194/amt-16-6051-2023, 2023
Short summary
Short summary
In order to conduct accurate aerosol retrieval over snow, the Remote Sensing of Trace Gases and Aerosol Products (RemoTAP) algorithm is extended with a bi-directional reflection distribution function for snow surfaces. The experiments with both synthetic and real data show that the extended RemoTAP maintains capability for snow-free pixels and has obvious advantages in accuracy and the fraction of successful retrievals for retrieval over snow, especially over surfaces with snow cover > 75 %.
Meng Gao, Bryan A. Franz, Peng-Wang Zhai, Kirk Knobelspiesse, Andrew M. Sayer, Xiaoguang Xu, J. Vanderlei Martins, Brian Cairns, Patricia Castellanos, Guangliang Fu, Neranga Hannadige, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Frederick Patt, Anin Puthukkudy, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 5863–5881, https://doi.org/10.5194/amt-16-5863-2023, https://doi.org/10.5194/amt-16-5863-2023, 2023
Short summary
Short summary
This study evaluated the retrievability and uncertainty of aerosol and ocean properties from PACE's HARP2 instrument using enhanced neural network models with the FastMAPOL algorithm. A cascading retrieval method is developed to improve retrieval performance. A global set of simulated HARP2 data is generated and used for uncertainty evaluations. The performance assessment demonstrates that the FastMAPOL algorithm is a viable approach for operational application to HARP2 data after PACE launch.
Athanasios Tsikerdekis, Otto P. Hasekamp, Nick A. J. Schutgens, and Qirui Zhong
Atmos. Chem. Phys., 23, 9495–9524, https://doi.org/10.5194/acp-23-9495-2023, https://doi.org/10.5194/acp-23-9495-2023, 2023
Short summary
Short summary
Aerosols are tiny particles of different substances (species) that can be emitted into the atmosphere by natural processes or by anthropogenic activities. However, the actual aerosol emission amount per species is highly uncertain. Thus in this work we correct the aerosol emissions used to drive a global aerosol–climate model using satellite observations through a process called data assimilation. These more accurate aerosol emissions can lead to a more accurate weather and climate prediction.
Edward Gryspeerdt, Adam C. Povey, Roy G. Grainger, Otto Hasekamp, N. Christina Hsu, Jane P. Mulcahy, Andrew M. Sayer, and Armin Sorooshian
Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, https://doi.org/10.5194/acp-23-4115-2023, 2023
Short summary
Short summary
The impact of aerosols on clouds is one of the largest uncertainties in the human forcing of the climate. Aerosol can increase the concentrations of droplets in clouds, but observational and model studies produce widely varying estimates of this effect. We show that these estimates can be reconciled if only polluted clouds are studied, but this is insufficient to constrain the climate impact of aerosol. The uncertainty in aerosol impact on clouds is currently driven by cases with little aerosol.
Anna Agustí-Panareda, Jérôme Barré, Sébastien Massart, Antje Inness, Ilse Aben, Melanie Ades, Bianca C. Baier, Gianpaolo Balsamo, Tobias Borsdorff, Nicolas Bousserez, Souhail Boussetta, Michael Buchwitz, Luca Cantarello, Cyril Crevoisier, Richard Engelen, Henk Eskes, Johannes Flemming, Sébastien Garrigues, Otto Hasekamp, Vincent Huijnen, Luke Jones, Zak Kipling, Bavo Langerock, Joe McNorton, Nicolas Meilhac, Stefan Noël, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Miha Razinger, Maximilian Reuter, Roberto Ribas, Martin Suttie, Colm Sweeney, Jérôme Tarniewicz, and Lianghai Wu
Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023, https://doi.org/10.5194/acp-23-3829-2023, 2023
Short summary
Short summary
We present a global dataset of atmospheric CO2 and CH4, the two most important human-made greenhouse gases, which covers almost 2 decades (2003–2020). It is produced by combining satellite data of CO2 and CH4 with a weather and air composition prediction model, and it has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. This dataset can be used for scientific studies in the field of climate change and the global carbon cycle.
Bastiaan van Diedenhoven, Otto P. Hasekamp, Brian Cairns, Gregory L. Schuster, Snorre Stamnes, Michael Shook, and Luke Ziemba
Atmos. Meas. Tech., 15, 7411–7434, https://doi.org/10.5194/amt-15-7411-2022, https://doi.org/10.5194/amt-15-7411-2022, 2022
Short summary
Short summary
The strong variability in the chemistry of atmospheric particulate matter affects the amount of water aerosols absorb and their effect on climate. We present a remote sensing method to determine the amount of water in particulate matter. Its application to airborne instruments indicates that the observed aerosols have rather low water contents and low fractions of soluble particles. Future satellites will be able to yield global aerosol water uptake data.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, Andre Butz, Otto P. Hasekamp, Lianghai Wu, and Jochen Landgraf
Atmos. Meas. Tech., 15, 6585–6603, https://doi.org/10.5194/amt-15-6585-2022, https://doi.org/10.5194/amt-15-6585-2022, 2022
Short summary
Short summary
The TROPOspheric Monitoring Instrument (TROPOMI) performs observations over ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the sun glint geometry the mirror-like reflection at the water surface provides a signal that is high enough to retrieve methane with high accuracy and precision. We present 4 years of methane concentrations over the ocean, and we assess its quality. We also show the importance of ocean observations to quantify total CH4 emissions.
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, Otto Hasekamp, Yongxiang Hu, Vanderlei Martins, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 15, 4859–4879, https://doi.org/10.5194/amt-15-4859-2022, https://doi.org/10.5194/amt-15-4859-2022, 2022
Short summary
Short summary
In this work, we assessed the pixel-wise retrieval uncertainties on aerosol and ocean color derived from multi-angle polarimetric measurements. Standard error propagation methods are used to compute the uncertainties. A flexible framework is proposed to evaluate how representative these uncertainties are compared with real retrieval errors. Meanwhile, to assist operational data processing, we optimized the computational speed to evaluate the retrieval uncertainties based on neural networks.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
Short summary
Short summary
We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Yoann Tellier, Cyril Crevoisier, Raymond Armante, Jean-Louis Dufresne, and Nicolas Meilhac
Geosci. Model Dev., 15, 5211–5231, https://doi.org/10.5194/gmd-15-5211-2022, https://doi.org/10.5194/gmd-15-5211-2022, 2022
Short summary
Short summary
Accurate radiative transfer models (RTMs) are required to improve climate model simulations. We describe the module named 4A-Flux, which is implemented into 4A/OP RTM, aimed at calculating spectral longwave radiative fluxes given a description of the surface, atmosphere, and spectroscopy. In Pincus et al. (2020), 4A-Flux has shown good agreement with state-of-the-art RTMs. Here, it is applied to perform sensitivity studies and will be used to improve the understanding of radiative flux modeling.
Athanasios Tsikerdekis, Nick A. J. Schutgens, Guangliang Fu, and Otto P. Hasekamp
Geosci. Model Dev., 15, 3253–3279, https://doi.org/10.5194/gmd-15-3253-2022, https://doi.org/10.5194/gmd-15-3253-2022, 2022
Short summary
Short summary
In our study we quantify the ability of the future satellite sensor SPEXone, part of the NASA PACE mission, to estimate aerosol emissions. The sensor will be able to retrieve accurate information of aerosol light extinction and most importantly light absorption. We simulate SPEXone spatial coverage and combine it with an aerosol model. We found that SPEXone will be able to estimate species-specific (e.g. dust, sea salt, organic or black carbon, sulfates) aerosol emissions very accurately.
Meloë S. F. Kacenelenbogen, Qian Tan, Sharon P. Burton, Otto P. Hasekamp, Karl D. Froyd, Yohei Shinozuka, Andreas J. Beyersdorf, Luke Ziemba, Kenneth L. Thornhill, Jack E. Dibb, Taylor Shingler, Armin Sorooshian, Reed W. Espinosa, Vanderlei Martins, Jose L. Jimenez, Pedro Campuzano-Jost, Joshua P. Schwarz, Matthew S. Johnson, Jens Redemann, and Gregory L. Schuster
Atmos. Chem. Phys., 22, 3713–3742, https://doi.org/10.5194/acp-22-3713-2022, https://doi.org/10.5194/acp-22-3713-2022, 2022
Short summary
Short summary
The impact of aerosols on Earth's radiation budget and human health is important and strongly depends on their composition. One desire of our scientific community is to derive the composition of the aerosol from satellite sensors. However, satellites observe aerosol optical properties (and not aerosol composition) based on remote sensing instrumentation. This study assesses how much aerosol optical properties can tell us about aerosol composition.
Cited articles
Ackerman, S. A. and Frey, R.: MODIS Atmosphere L2 Cloud Mask Product
(35_L2), NASA MODIS Adaptive Processing System, Goddard Space
Flight Center, https://doi.org/10.5067/MODIS/MOD35_L2.006, 2015a.
Ackerman, S. A. and Frey, R.: MODIS Atmosphere L2 Cloud Mask Product
(35_L2), NASA MODIS Adaptive Processing System, Goddard Space
Flight Center, https://doi.org/10.5067/MODIS/MYD35_L2.006, 2015b.
Bacour, C.: A surface reflectance DAtabase for ESA's earth observation,
Final report of the 6th Contract Change Request of the ADAM study,
NOV-FE-0724-NT-006, Issue 2 – Rev. 1., https://adam.noveltis.fr/?next=/pdfs/NOV-FE-0724-NT-006_v2.1.pdf (last access: 17 August 2022), 2019.
Bacour, C., Bréon, F.-M., Gonzalez, L., Price, I., Muller, J.-P., and
Straume, A. G.: Simulating Multi-Directional Narrowband Reflectance of the
Earth's Surface Using ADAM (A Surface Reflectance Database for ESA's Earth
Observation Missions), Remote Sens., 12, 1679, https://doi.org/10.3390/rs12101679, 2020.
Baldridge, A. M., Hook, S. J., Grove, C. I., and Rivera, G.: The ASTER
spectral library version 2.0, Remote Sens. Environ., 113, 711–715, https://doi.org/10.1016/j.rse.2008.11.007, 2009.
Boesch, H., Baker, D., Connor, B., Crisp, D., and Miller, C.: Global
Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission, Remote Sens., 3, 270–304, https://doi.org/10.3390/rs3020270, 2011.
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S.,
Rozanov, V. V., Chance, K. V., and Goede, A. P. H.: SCIAMACHY: Mission
objectives and measurement modes, J. Atmos. Sci., 56, 127–150,
https://doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2, 1999.
Bovensmann, H., Buchwitz, M., Burrows, J. P., Reuter, M., Krings, T., Gerilowski, K., Schneising, O., Heymann, J., Tretner, A., and Erzinger, J.: A remote sensing technique for global monitoring of power plant CO2 emissions from space and related applications, Atmos. Meas. Tech., 3, 781–811, https://doi.org/10.5194/amt-3-781-2010, 2010.
Brooker, L.: CONSTELLATION OF SMALL SATELLITES FOR THE MONITORING OF
GREENHOUSE GASES, in: 69th International Astronautical Congress (IAC), Bremen, Germany, 1–5 October 2018, 2018.
Broquet, G., Bréon, F.-M., Renault, E., Buchwitz, M., Reuter, M., Bovensmann, H., Chevallier, F., Wu, L., and Ciais, P.: The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities, Atmos. Meas. Tech., 11, 681–708, https://doi.org/10.5194/amt-11-681-2018, 2018.
Buchwitz, M., de Beek, R., Burrows, J. P., Bovensmann, H., Warneke, T., Notholt, J., Meirink, J. F., Goede, A. P. H., Bergamaschi, P., Körner, S., Heimann, M., and Schulz, A.: Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models, Atmos. Chem. Phys., 5, 941–962, https://doi.org/10.5194/acp-5-941-2005, 2005.
Buchwitz, M., Reuter, M., Bovensmann, H., Pillai, D., Heymann, J., Schneising, O., Rozanov, V., Krings, T., Burrows, J. P., Boesch, H., Gerbig, C., Meijer, Y., and Löscher, A.: Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization, Atmos. Meas. Tech., 6, 3477–3500, https://doi.org/10.5194/amt-6-3477-2013, 2013.
Chedin, A., Scott, N., Wahiche, C., and Moulinier, P.: The Improved
Initialization Inversion Method: A High Resolution Physical Method for
Temperature Retrievals from Satellites of the TIROS-N Series, J. Clim. Appl.
Meteorol., 24, 128–143, https://doi.org/10.1175/1520-0450(1985)024<0128:TIIIMA>2.0.CO;2, 1985.
Chevallier, F., Bréon, F.-M., and Rayner, P. J.: Contribution of the
Orbiting Carbon Observatory to the estimation of CO2 sources and sinks:
Theoretical study in a variational data assimilation framework, J. Geophys.
Res.-Atmos., 112, D09307, https://doi.org/10.1029/2006JD007375, 2007.
Ciais, P. and Joint Research Centre (European Commission): Towards a European operational
observing system to monitor fossil: CO2 emissions: final report from the
expert group, Publications Office, https://doi.org/10.2788/52148, 2016.
Ciais, P., Rayner, P., Chevallier, F., Bousquet, P., Logan, M., Peylin, P.,
and Ramonet, M.: Atmospheric inversions for estimating CO2 fluxes: methods
and perspectives, Clim. Change, 103, 69–92, https://doi.org/10.1007/s10584-010-9909-3, 2010.
Ciais, P., Dolman, A. J., Bombelli, A., Duren, R., Peregon, A., Rayner, P. J., Miller, C., Gobron, N., Kinderman, G., Marland, G., Gruber, N., Chevallier, F., Andres, R. J., Balsamo, G., Bopp, L., Bréon, F.-M., Broquet, G., Dargaville, R., Battin, T. J., Borges, A., Bovensmann, H., Buchwitz, M., Butler, J., Canadell, J. G., Cook, R. B., DeFries, R., Engelen, R., Gurney, K. R., Heinze, C., Heimann, M., Held, A., Henry, M., Law, B., Luyssaert, S., Miller, J., Moriyama, T., Moulin, C., Myneni, R. B., Nussli, C., Obersteiner, M., Ojima, D., Pan, Y., Paris, J.-D., Piao, S. L., Poulter, B., Plummer, S., Quegan, S., Raymond, P., Reichstein, M., Rivier, L., Sabine, C., Schimel, D., Tarasova, O., Valentini, R., Wang, R., van der Werf, G., Wickland, D., Williams, M., and Zehner, C.: Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system, Biogeosciences, 11, 3547–3602, https://doi.org/10.5194/bg-11-3547-2014, 2014.
Cogan, A. J., Boesch, H., Parker, R. J., Feng, L., Palmer, P. I., Blavier,
J.-F. L., Deutscher, N. M., Macatangay, R., Notholt, J., Roehl, C., Warneke,
T., and Wunch, D.: Atmospheric carbon dioxide retrieved from the Greenhouse
gases Observing SATellite (GOSAT): Comparison with ground-based TCCON
observations and GEOS-Chem model calculations, J. Geophys. Res.-Atmos.,
117, D21301, https://doi.org/10.1029/2012JD018087, 2012.
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Eastwood, M. L., Green, R. O.,
Dennison, P. E., Frankenberg, C., Heckler, J. W., Asner, G. P., and Miller,
C. E.: Quantifying Global Power Plant Carbon Dioxide Emissions With Imaging
Spectroscopy, AGU Advances, 2, e2020AV000350, https://doi.org/10.1029/2020AV000350, 2021.
Danielson, J. J., and Gesch, D. B.: Global multi-resolution terrain elevation
data 2010 (GMTED2010), U.S. Geol. Surv., Open-File, https://doi.org/10.3133/ofr20111073, 2011.
Dogniaux, M., Crevoisier, C., Armante, R., Capelle, V., Delahaye, T., Cassé, V., De Mazière, M., Deutscher, N. M., Feist, D. G., Garcia, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Morino, I., Notholt, J., Pollard, D. F., Roehl, C. M., Shiomi, K., Strong, K., Té, Y., Velazco, V. A., and Warneke, T.: The Adaptable 4A Inversion (5AI): description and first
Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B. N., Mishchenko, M., Yang,
P., Eck, T. F., Volten, H., Muñoz, O., Veihelmann, B., van der Zande, W.
J., Leon, J.-F., Sorokin, M., and Slutsker, I.: Application of spheroid
models to account for aerosol particle nonsphericity in remote sensing of
desert dust, J. Geophys. Res.-Atmos., 111,
D11208, https://doi.org/10.1029/2005JD006619, 2006.
Duren, R. M. and Miller, C. E.: Measuring the carbon emissions of
megacities, Nat. Clim. Change, 2, 560–562, https://doi.org/10.1038/nclimate1629, 2012.
Ferrec, Y., Bonnery, G., Brooker, L., Croizé, L., Gousset, S., and Le
Coarer, E.: NanoCarb part 1: compact snapshot imaging interferometer for
CO2 monitoring from space, in International Conference on Space Optics –
ICSO 2018, edited by: Sodnik, Z., Karafolas, N., and Cugny, B., SPIE, 11180, 771–779, https://doi.org/10.1117/12.2535992, 2019.
Flemming, J., Benedetti, A., Inness, A., Engelen, R. J., Jones, L., Huijnen, V., Remy, S., Parrington, M., Suttie, M., Bozzo, A., Peuch, V.-H., Akritidis, D., and Katragkou, E.: The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015, Atmos. Chem. Phys., 17, 1945–1983, https://doi.org/10.5194/acp-17-1945-2017, 2017.
Fougnie, B., Chimot, J., Vázquez-Navarro, M., Marbach, T., and Bojkov,
B.: Aerosol retrieval from space – how does geometry of acquisition impact
our ability to characterize aerosol properties, J. Quant. Spectrosc. Ra., 256, 107304, https://doi.org/10.1016/j.jqsrt.2020.107304, 2020.
Gousset, S., Croizé, L., Le Coarer, E., Ferrec, Y., Rodrigo-Rodrigo, J., and Brooker, L. for the SCARBO consortium: NanoCarb hyperspectral sensor: on
performance optimization and analysis for greenhouse gas monitoring from a
constellation of small satellites, CEAS Space Journal, 11, 507–524,
https://doi.org/10.1007/s12567-019-00273-9, 2019.
Guerlet, S., Butz, A., Schepers, D., Basu, S., Hasekamp, O. P., Kuze, A.,
Yokota, T., Blavier, J.-F., Deutscher, N. M., Griffith, D. W. T., Hase, F.,
Kyro, E., Morino, I., Sherlock, V., Sussmann, R., Galli, A., and Aben, I.:
Impact of aerosol and thin cirrus on retrieving and validating XCO2 from
GOSAT shortwave infrared measurements, J. Geophys. Res.-Atmos., 118,
4887–4905, https://doi.org/10.1002/jgrd.50332, 2013.
Hasekamp, O. P., Fu, G., Rusli, S. P., Wu, L., Di Noia, A., aan de Brugh,
J., Landgraf, J., Martijn Smit, J., Rietjens, J., and van Amerongen, A.:
Aerosol measurements by SPEXone on the NASA PACE mission: expected retrieval
capabilities, J. Quant. Spectrosc. Ra., 227, 170–184, https://doi.org/10.1016/j.jqsrt.2019.02.006, 2019.
Houweling, S., Hartmann, W., Aben, I., Schrijver, H., Skidmore, J., Roelofs, G.-J., and Breon, F.-M.: Evidence of systematic errors in SCIAMACHY-observed CO2 due to aerosols, Atmos. Chem. Phys., 5, 3003–3013, https://doi.org/10.5194/acp-5-3003-2005, 2005.
Jacquinet-Husson, N., Armante, R., Scott, N. A., et al.: The 2015 edition of the GEISA spectroscopic database, J. Mol. Spectrosc., 327, 31–72,
https://doi.org/10.1016/j.jms.2016.06.007, 2016.
Jervis, D., McKeever, J., Durak, B. O. A., Sloan, J. J., Gains, D., Varon, D. J., Ramier, A., Strupler, M., and Tarrant, E.: The GHGSat-D imaging spectrometer, Atmos. Meas. Tech., 14, 2127–2140, https://doi.org/10.5194/amt-14-2127-2021, 2021.
Kuhlmann, G., Broquet, G., Marshall, J., Clément, V., Löscher, A., Meijer, Y., and Brunner, D.: Detectability of CO2 emission plumes of cities and power plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission, Atmos. Meas. Tech., 12, 6695–6719, https://doi.org/10.5194/amt-12-6695-2019, 2019.
Lamouroux, J., Tran, H., Laraia, A. L., Gamache, R. R., Rothman, L. S.,
Gordon, I. E., and Hartmann, J.-M.: Updated database plus software for
line-mixing in CO2 infrared spectra and their test using laboratory spectra in the 1.5–2.3 µm region, J. Quant. Spectrosc. Ra., 111, 2321–2331, https://doi.org/10.1016/j.jqsrt.2010.03.006, 2010.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K.,
Ziegler, A. D., Chen, A., Gong, P., Chen, J., Hu, G., Chen, Y., Wang, S.,
Wu, Q., Huang, K., Estes, L., and Zeng, Z.: High-spatiotemporal-resolution
mapping of global urban change from 1985 to 2015, Nature Sustainability, 3,
564–570, https://doi.org/10.1038/s41893-020-0521-x, 2020.
Meijer, Y. and Earth and Mission Science Division: Copernicus CO2 Monitoring Mission Requirements Document,
https://esamultimedia.esa.int/docs/EarthObservation/CO2M_MRD_v2.0_Issued20190927.pdf (last access: 10 August 2022), 2019.
Nassar, R., Mastrogiacomo, J.-P., Bateman-Hemphill, W., McCracken, C.,
MacDonald, C. G., Hill, T., O'Dell, C. W., Kiel, M., and Crisp, D.: Advances
in quantifying power plant CO2 emissions with OCO-2, Remote Sens. Environ., 264, 112579, https://doi.org/10.1016/j.rse.2021.112579, 2021.
O'Dell, C. W., Eldering, A., Wennberg, P. O., Crisp, D., Gunson, M. R., Fisher, B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L., Merrelli, A., Natraj, V., Nelson, R. R., Osterman, G. B., Payne, V. H., Taylor, T. E., Wunch, D., Drouin, B. J., Oyafuso, F., Chang, A., McDuffie, J., Smyth, M., Baker, D. F., Basu, S., Chevallier, F., Crowell, S. M. R., Feng, L., Palmer, P. I., Dubey, M., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Roehl, C. M., Sha, M. K., Strong, K., Sussmann, R., Te, Y., Uchino, O., and Velazco, V. A.: Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm, Atmos. Meas. Tech., 11, 6539–6576, https://doi.org/10.5194/amt-11-6539-2018, 2018.
Palacios-Peña, L., Jiménez-Guerrero, P., Baró, R., Balzarini, A., Bianconi, R., Curci, G., Landi, T. C., Pirovano, G., Prank, M., Riccio, A., Tuccella, P., and Galmarini, S.: Aerosol optical properties over Europe: an evaluation of the AQMEII Phase 3 simulations against satellite observations, Atmos. Chem. Phys., 19, 2965–2990, https://doi.org/10.5194/acp-19-2965-2019, 2019.
Pandey, S., Gautam, R., Houweling, S., van der Gon, H. D., Sadavarte, P.,
Borsdorff, T., Hasekamp, O., Landgraf, J., Tol, P., van Kempen, T.,
Hoogeveen, R., van Hees, R., Hamburg, S. P., Maasakkers, J. D. and Aben, I.:
Satellite observations reveal extreme methane leakage from a natural gas
well blowout, P. Natl. Acad. Sci. USA, 116, 26376–26381,
https://doi.org/10.1073/pnas.1908712116, 2019.
Papayannis, A., Amiridis, V., Mona, L., Tsaknakis, G., Balis, D.,
Bösenberg, J., Chaikovski, A., De Tomasi, F., Grigorov, I., Mattis, I.,
Mitev, V., Müller, D., Nickovic, S., Pérez, C., Pietruczuk, A.,
Pisani, G., Ravetta, F., Rizi, V., Sicard, M., Trickl, T., Wiegner, M.,
Gerding, M., Mamouri, R. E., D'Amico, G., and Pappalardo, G.: Systematic
lidar observations of Saharan dust over Europe in the frame of EARLINET
(2000–2002), J. Geophys. Res., 113, D10204,
https://doi.org/10.1029/2007JD009028, 2008.
Pillai, D., Buchwitz, M., Gerbig, C., Koch, T., Reuter, M., Bovensmann, H., Marshall, J., and Burrows, J. P.: Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany, Atmos. Chem. Phys., 16, 9591–9610, https://doi.org/10.5194/acp-16-9591-2016, 2016.
Reuter, M., Buchwitz, M., Schneising, O., Heymann, J., Bovensmann, H., and Burrows, J. P.: A method for improved SCIAMACHY CO2 retrieval in the presence of optically thin clouds, Atmos. Meas. Tech., 3, 209–232, https://doi.org/10.5194/amt-3-209-2010, 2010.
Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., and
Burrows, J.: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral
Instruments Approximating Multiple Scattering – Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sens., 9, 1102, https://doi.org/10.3390/rs9111102, 2017.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, WORLD SCIENTIFIC, https://doi.org/10.1142/3171,
2000.
Rusli, S. P., Hasekamp, O., aan de Brugh, J., Fu, G., Meijer, Y., and Landgraf, J.: Anthropogenic CO2 monitoring satellite mission: the need for multi-angle polarimetric observations, Atmos. Meas. Tech., 14, 1167–1190, https://doi.org/10.5194/amt-14-1167-2021, 2021.
Scott, N. A. and Chédin, A.: A Fast Line-by-Line Method for Atmospheric
Absorption Computations: The Automatized Atmospheric Absorption Atlas, J.
Appl. Meteorol., 20, 802–812, 1981.
Smit, J. M., Rietjens, J. H. H., van Harten, G., Noia, A. Di, Laauwen, W.,
Rheingans, B. E., Diner, D. J., Cairns, B., Wasilewski, A., Knobelspiesse,
K. D., Ferrare, R., and Hasekamp, O. P.: SPEX airborne spectropolarimeter
calibration and performance, Appl. Optics, 58, 5695–5719,
https://doi.org/10.1364/AO.58.005695, 2019.
Smith, W. J.: Modern Optical Engineering: The Design of Optical Systems,
Fourth Edition, 4th edn., McGraw-Hill Education, New York, ISBN-13: 978-0071476874, 2008.
Snik, F., Karalidi, T., and Keller, C. U.: Spectral modulation for full
linear polarimetry, Appl. Optics, 48, 1337–1346, https://doi.org/10.1364/AO.48.001337,
2009.
Spurr, R. J. D.: Simultaneous derivation of intensities and weighting
functions in a general pseudo-spherical discrete ordinate radiative transfer
treatment, J. Quant. Spectrosc. Ra., 75, 129–175,
https://doi.org/10.1016/S0022-4073(01)00245-X, 2002.
Strandgren, J., Krutz, D., Wilzewski, J., Paproth, C., Sebastian, I., Gurney, K. R., Liang, J., Roiger, A., and Butz, A.: Towards spaceborne monitoring of localized CO2 emissions: an instrument concept and first performance assessment, Atmos. Meas. Tech., 13, 2887–2904, https://doi.org/10.5194/amt-13-2887-2020, 2020.
Tran, H. and Hartmann, J.-M.: An improved O2 A band absorption model and its consequences for retrievals of photon paths and surface pressures, J.
Geophys. Res.-Atmos., 113, D18104, https://doi.org/10.1029/2008JD010011, 2008.
van Amerongen, A., Rietjens, J., Campo, J., Dogan, E., Dingjan, J., Nalla,
R., Caron, J., and Hasekamp, O.: SPEXone: a compact multi-angle polarimeter,
in: International Conference on Space Optics – ICSO 2018, edited by: Sodnik, Z., Karafolas, N., and Cugny, B., SPIE, 11180, 223–236,
https://doi.org/10.1117/12.2535940, 2019.
Varon, D. J., Jacob, D. J., McKeever, J., Jervis, D., Durak, B. O. A., Xia, Y., and Huang, Y.: Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes, Atmos. Meas. Tech., 11, 5673–5686, https://doi.org/10.5194/amt-11-5673-2018, 2018.
Velazco, V. A., Buchwitz, M., Bovensmann, H., Reuter, M., Schneising, O., Heymann, J., Krings, T., Gerilowski, K., and Burrows, J. P.: Towards space based verification of CO2 emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation, Atmos. Meas. Tech., 4, 2809–2822, https://doi.org/10.5194/amt-4-2809-2011, 2011.
Wang, Y., Ciais, P., Broquet, G., Bréon, F.-M., Oda, T., Lespinas, F., Meijer, Y., Loescher, A., Janssens-Maenhout, G., Zheng, B., Xu, H., Tao, S., Gurney, K. R., Roest, G., Santaren, D., and Su, Y.: A global map of emission clumps for future monitoring of fossil fuel CO2 emissions from space, Earth Syst. Sci. Data, 11, 687–703, https://doi.org/10.5194/essd-11-687-2019, 2019.
Wilzewski, J. S., Roiger, A., Strandgren, J., Landgraf, J., Feist, D. G., Velazco, V. A., Deutscher, N. M., Morino, I., Ohyama, H., Té, Y., Kivi, R., Warneke, T., Notholt, J., Dubey, M., Sussmann, R., Rettinger, M., Hase, F., Shiomi, K., and Butz, A.: Spectral sizing of a coarse-spectral-resolution satellite sensor for XCO2, Atmos. Meas. Tech., 13, 731–745, https://doi.org/10.5194/amt-13-731-2020, 2020.
Wu, L., Hasekamp, O., Hu, H., Landgraf, J., Butz, A., aan de Brugh, J., Aben, I., Pollard, D. F., Griffith, D. W. T., Feist, D. G., Koshelev, D., Hase, F., Toon, G. C., Ohyama, H., Morino, I., Notholt, J., Shiomi, K., Iraci, L., Schneider, M., de Mazière, M., Sussmann, R., Kivi, R., Warneke, T., Goo, T.-Y., and Té, Y.: Carbon dioxide retrieval from OCO-2 satellite observations using the RemoTeC algorithm and validation with TCCON measurements, Atmos. Meas. Tech., 11, 3111–3130, https://doi.org/10.5194/amt-11-3111-2018, 2018.
Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J.,
Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The
Total Carbon Column Observing Network, Philos. T. Roy. Soc. A, 369, 2087–2112, https://doi.org/10.1098/rsta.2010.0240, 2011.
Short summary
The Space Carbon Observatory (SCARBO) concept proposes a constellation of small satellites that would carry a miniaturized Fabry–Pérot imaging interferometer named NanoCarb and an aerosol instrument named SPEXone. In this work, we assess the performance of this concept for the retrieval of the total weighted columns of CO2 and CH4 and show the interest of adding the SPEXone aerosol instrument to improve the CO2 and CH4 column retrieval.
The Space Carbon Observatory (SCARBO) concept proposes a constellation of small satellites that...
