Articles | Volume 18, issue 9
https://doi.org/10.5194/amt-18-2005-2025
© Author(s) 2025. 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-18-2005-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 May 2025
Research article |
| 07 May 2025
| 07 May 2025
Aerosol composition retrieval from a combination of three different spaceborne instruments: information content analysis
Ulrike Stöffelmair
CORRESPONDING AUTHOR
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
Institute of Environmental Physics (IUP), University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Thomas Popp
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
Marco Vountas
Institute of Environmental Physics (IUP), University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Hartmut Bösch
Institute of Environmental Physics (IUP), University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
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Ulrike Stöffelmair, Diana Dermann, and Thomas Popp
EGUsphere, https://doi.org/10.5194/egusphere-2026-2885, https://doi.org/10.5194/egusphere-2026-2885, 2026
This preprint is open for discussion and under review for Earth Observation (EO).
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The study focuses on enhancing the usability of previously published satellite-based Climate Data Record datasets. We developed a method to quantitatively evaluate the consistency between datasets retrieved from the same instruments using different algorithms. The study includes an exemplary application of the method to Aerosol Optical Depth datasets. The goal is to provide clear guidance for the user on how to optimally exploit the complementarity datasets retrieved from instruments.
Ulrike Stöffelmair, Diana Dermann, and Thomas Popp
EGUsphere, https://doi.org/10.5194/egusphere-2026-2885, https://doi.org/10.5194/egusphere-2026-2885, 2026
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The study focuses on enhancing the usability of previously published satellite-based Climate Data Record datasets. We developed a method to quantitatively evaluate the consistency between datasets retrieved from the same instruments using different algorithms. The study includes an exemplary application of the method to Aerosol Optical Depth datasets. The goal is to provide clear guidance for the user on how to optimally exploit the complementarity datasets retrieved from instruments.
Liang Feng, Paul I. Palmer, Haolin Wang, Robert Parker, Hartmut Boesch, Sébastien C. Biraud, Łukasz Chmura, Emanuel Gloor, László Haszpra, Elena Kozlova, Euan G. Nisbet, Steffen M. Noe, Thomas Röckmann, Rodrigo Augusto Ferreira de Souza, and Martin Steinbacher
EGUsphere, https://doi.org/10.5194/egusphere-2026-2662, https://doi.org/10.5194/egusphere-2026-2662, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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This study examines the relative roles of emissions and oxidation in recent rapid methane growth. Using an ensemble Kalman filter, we quantify global methane sources and sinks for 2018–2024. Emission changes dominate the increase, except in 2020 when a COVID‑19‑related OH decline strongly affected sinks. Atmospheric photochemistry—including OH and CH₄ variability and temperature‑dependent oxidation—also significantly shapes the methane budget.
Alexander Mchedlishvili, Marco Vountas, and Hartmut Bösch
The Cryosphere, 20, 2895–2922, https://doi.org/10.5194/tc-20-2895-2026, https://doi.org/10.5194/tc-20-2895-2026, 2026
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Air circulation over Greenland affects ice melt by changing temperature, moisture, and wind. We study how much sunlight the ice reflects and how this relates to extreme melting. Focusing on the summers of 2012, 2019, and 2023, we explore the weather patterns that caused unusually warm conditions and how the Greenland ice sheet responded.
Oliver Schneising, Heinrich Bovensmann, Michael Buchwitz, Matthias Buschmann, Nicholas M. Deutscher, David W. T. Griffith, Jonas Hachmeister, Frank Hase, Laura T. Iraci, Rigel Kivi, Isamu Morino, Hirofumi Ohyama, Christof Petri, Maximilian Reuter, John Robinson, Coleen Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Wei Wang, Thorsten Warneke, Damien Weidmann, Debra Wunch, Minqiang Zhou, and Hartmut Bösch
Atmos. Meas. Tech., 19, 2407–2435, https://doi.org/10.5194/amt-19-2407-2026, https://doi.org/10.5194/amt-19-2407-2026, 2026
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We present an improved version of the TROPOMI/WFMD (TROPOspheric Monitoring Instrument/Weighting Function Modified Differential) algorithm for the simultaneous retrieval of atmospheric methane and carbon monoxide from satellite observations. The updated data product combines higher data yield with better precision and accuracy, expanding its suitability for a wider range of scientific applications. These substantial advances are mainly due to refined quality filtering, enabling more reliable identification of cloudy scenes and mitigating specific aerosol-related issues.
Michael Weimer, Maximilian Reuter, Michael Hilker, Stefan Noël, Michael Buchwitz, Yasjka Meijer, Rüdiger Lang, Julia Marshall, Heinrich Bovensmann, John P. Burrows, and Hartmut Bösch
EGUsphere, https://doi.org/10.5194/egusphere-2026-1458, https://doi.org/10.5194/egusphere-2026-1458, 2026
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Greenhouse gas column measurements from space using reflected sunlight have to make assumptions about the Earth's surface elevation. Usually, it assumed that the elevation representing the satellite's spatial sample is the unweighted average of high-resolution surface elevation data. We find and show with many examples that the surface elevation should be weighted by the surface reflectance in the respective wavelength bands. This improves errors in the retrieval of trace gases.
Lakshmi N. Bharathan, Robert J. Parker, Michael Cartwright, Dan Orr, Antonio Di Noia, Peter Somkuti, Alex Webb, and Hartmut Bösch
EGUsphere, https://doi.org/10.5194/egusphere-2026-1029, https://doi.org/10.5194/egusphere-2026-1029, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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The study aims to enhance satellite- based methane measurements over the Arctic high-latitudes, a critical region where rapid warming feeds the permafrost thawing-methane cycle and poise high concern for global climate stability. A 20 % gain in GOSAT satellite methane data throughput has been achieved using a Genetic algorithm optimisation approach, enhancing our capacity to track methane emissions over most rapidly warming, climatically sensitive regions on the Earth.
Stephen J. Harris, Sven Krautwurst, Jorg Hacker, Mark Lunt, Borchardt Jakob, Mei Bai, Hartmut Boesch, Tarra Brain, John Philip Burrows, Shakti Chakravarty, Robert A. Field, Rebecca E. Fisher, James L. France, Konstantin Gerilowski, Oke Huhs, Wolfgang Junkermann, Bryce F. J. Kelly, Martin Kumm, Mathias Lanoisellé, Wolfgang Lieff, Andrew McGrath, Adrian Murphy, Thomas Röckmann, Zoe Salmon, Josua Schindewolf, Jakob Thoböll, Carina van der Veen, and Heinrich Bovensmann
EGUsphere, https://doi.org/10.5194/egusphere-2026-772, https://doi.org/10.5194/egusphere-2026-772, 2026
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The accuracy of methods used to estimate and report fugitive methane emissions from Australian coal mines remains unclear. This study compares airborne emission rate estimates with reported estimates from 17 coal mines in the Bowen Basin, a region accounting for 45 % of national coal production. Results show good agreement for underground coal mines, but poor agreement for surface coal mines, suggesting improvements to surface coal mine reporting methods is needed to improve inventory reporting.
Simon Laffoy, Marco Vountas, Linlu Mei, and Hartmut Bösch
Atmos. Meas. Tech., 19, 293–306, https://doi.org/10.5194/amt-19-293-2026, https://doi.org/10.5194/amt-19-293-2026, 2026
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Aerosol are particles in the atmosphere such as dust, salt, soot and sulfates. They may be measured by applying algorithms to satellite images of the Earth. We attempt to apply data from the new Environmental Mapping and Analysis Program (EnMAP) satellite to the existing XBAER algorithm, which was previously applied to data from the Ocean Land and Colour Instrument (OLCI) satellite. This paper compares the satellite inputs and aerosol outputs of the XBAER algorithm and finds good results.
Sven Krautwurst, Christian Fruck, Sebastian Wolff, Jakob Borchardt, Oke Huhs, Konstantin Gerilowski, Michał Gałkowski, Christoph Kiemle, Mathieu Quatrevalet, Martin Wirth, Christian Mallaun, John P. Burrows, Christoph Gerbig, Andreas Fix, Hartmut Bösch, and Heinrich Bovensmann
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Anomalously high CH4 emissions from landfills in Madrid, Spain, have been observed by satellite measurements in recent years. Our investigations of these waste facilities, using passive and active airborne remote sensing measurements, confirm these high emission rates with values of up to 13 th−1 during the overflight and show excellent agreement between the two techniques. A large fraction of the emissions is attributed to active landfill sites.
Peter Somkuti, Gregory McGarragh, Christopher O'Dell, Antonio Di Noia, Leif Vogel, Sean Crowell, Lesley E. Ott, and Hartmut Bösch
Atmos. Meas. Tech., 18, 4647–4663, https://doi.org/10.5194/amt-18-4647-2025, https://doi.org/10.5194/amt-18-4647-2025, 2025
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In space-based estimates of atmospheric methane concentrations, one can often observe biases that look like imprints of surface features. We performed realistic simulation experiments and find the root cause to be unaccounted aerosols. Since good knowledge of aerosols is difficult to achieve for operational science data processing, we conclude that a comprehensive surface bias correction scheme is highly important for missions utilizing the 2.3 µm spectral band for methane retrievals.
Neil Humpage, Hartmut Boesch, William Okello, Jia Chen, Florian Dietrich, Mark F. Lunt, Liang Feng, Paul I. Palmer, and Frank Hase
Atmos. Meas. Tech., 17, 5679–5707, https://doi.org/10.5194/amt-17-5679-2024, https://doi.org/10.5194/amt-17-5679-2024, 2024
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We used a Bruker EM27/SUN spectrometer within an automated weatherproof enclosure to measure greenhouse gas column concentrations over a 3-month period in Jinja, Uganda. The portability of the EM27/SUN allows us to evaluate satellite and model data in locations not covered by traditional validation networks. This is of particular value in tropical Africa, where extensive terrestrial ecosystems are a significant store of carbon and play a key role in the atmospheric budgets of CO2 and CH4.
Oliver Schneising, Michael Buchwitz, Maximilian Reuter, Michael Weimer, Heinrich Bovensmann, John P. Burrows, and Hartmut Bösch
Atmos. Chem. Phys., 24, 7609–7621, https://doi.org/10.5194/acp-24-7609-2024, https://doi.org/10.5194/acp-24-7609-2024, 2024
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Large quantities of CO and CO2 are emitted during conventional steel production. As satellite-based estimates of CO2 emissions at the facility level are challenging, co-emitted CO can indicate the carbon footprint of steel plants. We estimate CO emissions for German steelworks and use CO2 emissions from emissions trading data to derive a sector-specific CO/CO2 emission ratio for the steel industry; it is a prerequisite to use CO as a proxy for CO2 emissions from similar steel production sites.
Basudev Swain, Marco Vountas, Aishwarya Singh, Nidhi L. Anchan, Adrien Deroubaix, Luca Lelli, Yanick Ziegler, Sachin S. Gunthe, Hartmut Bösch, and John P. Burrows
Atmos. Chem. Phys., 24, 5671–5693, https://doi.org/10.5194/acp-24-5671-2024, https://doi.org/10.5194/acp-24-5671-2024, 2024
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Arctic amplification (AA) accelerates the warming of the central Arctic cryosphere and affects aerosol dynamics. Limited observations hinder a comprehensive analysis. This study uses AEROSNOW aerosol optical density (AOD) data and GEOS-Chem simulations to assess AOD variability. Discrepancies highlight the need for improved observational integration into models to refine our understanding of aerosol effects on cloud microphysics, ice nucleation, and radiative forcing under evolving AA.
Stefan Noël, Michael Buchwitz, Michael Hilker, Maximilian Reuter, Michael Weimer, Heinrich Bovensmann, John P. Burrows, Hartmut Bösch, and Ruediger Lang
Atmos. Meas. Tech., 17, 2317–2334, https://doi.org/10.5194/amt-17-2317-2024, https://doi.org/10.5194/amt-17-2317-2024, 2024
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FOCAL-CO2M is one of the three operational retrieval algorithms which will be used to derive XCO2 and XCH4 from measurements of the forthcoming European CO2M mission. We present results of applications of FOCAL-CO2M to simulated spectra, from which confidence is gained that the algorithm is able to fulfil the challenging requirements on systematic errors for the CO2M mission (spatio-temporal bias ≤ 0.5 ppm for XCO2 and ≤ 5 ppb for XCH4).
Blanca Fuentes Andrade, Michael Buchwitz, Maximilian Reuter, Heinrich Bovensmann, Andreas Richter, Hartmut Boesch, and John P. Burrows
Atmos. Meas. Tech., 17, 1145–1173, https://doi.org/10.5194/amt-17-1145-2024, https://doi.org/10.5194/amt-17-1145-2024, 2024
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We developed a method to estimate CO2 emissions from localized sources, such as power plants, using satellite data and applied it to estimate CO2 emissions from the Bełchatów Power Station (Poland). As the detection of CO2 emission plumes from satellite data is difficult, we used observations of co-emitted NO2 to constrain the emission plume region. Our results agree with CO2 emission estimations based on the power-plant-generated power and emission factors.
Basudev Swain, Marco Vountas, Adrien Deroubaix, Luca Lelli, Yanick Ziegler, Soheila Jafariserajehlou, Sachin S. Gunthe, Andreas Herber, Christoph Ritter, Hartmut Bösch, and John P. Burrows
Atmos. Meas. Tech., 17, 359–375, https://doi.org/10.5194/amt-17-359-2024, https://doi.org/10.5194/amt-17-359-2024, 2024
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Aerosols are suspensions of particles dispersed in the air. In this study, we use a novel retrieval of satellite data to investigate an optical property of aerosols, the aerosol optical depth, in the high Arctic to assess their direct and indirect roles in climate change. This study demonstrates that the presented approach shows good quality and very promising potential.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
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Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Nicholas Balasus, Daniel J. Jacob, Alba Lorente, Joannes D. Maasakkers, Robert J. Parker, Hartmut Boesch, Zichong Chen, Makoto M. Kelp, Hannah Nesser, and Daniel J. Varon
Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, https://doi.org/10.5194/amt-16-3787-2023, 2023
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We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
Ruosi Liang, Yuzhong Zhang, Wei Chen, Peixuan Zhang, Jingran Liu, Cuihong Chen, Huiqin Mao, Guofeng Shen, Zhen Qu, Zichong Chen, Minqiang Zhou, Pucai Wang, Robert J. Parker, Hartmut Boesch, Alba Lorente, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, https://doi.org/10.5194/acp-23-8039-2023, 2023
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We compare and evaluate East Asian methane emissions inferred from different satellite observations (GOSAT and TROPOMI). The results show discrepancies over northern India and eastern China. Independent ground-based observations are more consistent with TROPOMI-derived emissions in northern India and GOSAT-derived emissions in eastern China.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Basudev Swain, Marco Vountas, Adrien Deroubaix, Luca Lelli, Aishwarya Singh, Yanick Ziegler, Sachin S. Gunthe, and John P. Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2023-730, https://doi.org/10.5194/egusphere-2023-730, 2023
Preprint archived
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Aerosols are suspensions of particles distributed in the air. Depending on their chemical composition, they scatter and/or absorb sunlight and thus cool or warm the earth's atmosphere and its surface. They also provide as a surface in the atmosphere upon which ice or liquid clouds droplets nucleate and grow. In this study, we use satellite observations and model simulations to investigate the properties of aerosols with the goal of assessing their direct and indirect role in climate change.
Peter Joyce, Cristina Ruiz Villena, Yahui Huang, Alex Webb, Manuel Gloor, Fabien H. Wagner, Martyn P. Chipperfield, Rocío Barrio Guilló, Chris Wilson, and Hartmut Boesch
Atmos. Meas. Tech., 16, 2627–2640, https://doi.org/10.5194/amt-16-2627-2023, https://doi.org/10.5194/amt-16-2627-2023, 2023
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Methane emissions are responsible for a lot of the warming caused by the greenhouse effect, much of which comes from a small number of point sources. We can identify methane point sources by analysing satellite data, but it requires a lot of time invested by experts and is prone to very high errors. Here, we produce a neural network that can automatically identify methane point sources and estimate the mass of methane that is being released per hour and are able to do so with far smaller errors.
Liang Feng, Paul I. Palmer, Robert J. Parker, Mark F. Lunt, and Hartmut Bösch
Atmos. Chem. Phys., 23, 4863–4880, https://doi.org/10.5194/acp-23-4863-2023, https://doi.org/10.5194/acp-23-4863-2023, 2023
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Our understanding of recent changes in atmospheric methane has defied explanation. Since 2007, the atmospheric growth of methane has accelerated to record-breaking values in 2020 and 2021. We use satellite observations of methane to show that (1) increasing emissions over the tropics are mostly responsible for these recent atmospheric changes, and (2) changes in the OH sink during the 2020 Covid-19 lockdown can explain up to 34% of changes in atmospheric methane for that year.
Luca Lelli, Marco Vountas, Narges Khosravi, and John Philipp Burrows
Atmos. Chem. Phys., 23, 2579–2611, https://doi.org/10.5194/acp-23-2579-2023, https://doi.org/10.5194/acp-23-2579-2023, 2023
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Arctic amplification describes the recent period in which temperatures have been rising twice as fast as or more than the global average and sea ice and the Greenland ice shelf are approaching a tipping point. Hence, the Arctic ability to reflect solar energy decreases and absorption by the surface increases. Using 2 decades of complementary satellite data, we discover that clouds unexpectedly increase the pan-Arctic reflectance by increasing their liquid water content, thus cooling the Arctic.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
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Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Haiyue Tan, Lin Zhang, Xiao Lu, Yuanhong Zhao, Bo Yao, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 22, 1229–1249, https://doi.org/10.5194/acp-22-1229-2022, https://doi.org/10.5194/acp-22-1229-2022, 2022
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Methane is the second most important anthropogenic greenhouse gas. Understanding methane emissions and concentration growth over China in the past decade is important to support its mitigation. This study analyzes the contributions of methane emissions from different regions and sources over the globe to methane changes over China in 2007–2018. Our results show strong international transport influences and emphasize the need of intensive methane measurements covering eastern China.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
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We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Stefan Kinne, Peter North, Kevin Pearson, and Thomas Popp
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-954, https://doi.org/10.5194/acp-2021-954, 2021
Publication in ACP not foreseen
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To monitor aerosol properties and quantify aerosol climate impacts, ESA's Climate Change Initiative (CCI) supported the retrieval development for their dual-view sensors. Global maps of monthly AOD and AODf data are presented for 4 years: 1998 using ATSR-2, 2008 using AATSR and 2019 and 2020 using SLSTR sensor data. Application goals of this paper are to address decadal aerosol trends, to identify possible Covid-19 impacts in 2020 and to associate retrieved AOD with climate impacts.
Mark F. Lunt, Alistair J. Manning, Grant Allen, Tim Arnold, Stéphane J.-B. Bauguitte, Hartmut Boesch, Anita L. Ganesan, Aoife Grant, Carole Helfter, Eiko Nemitz, Simon J. O'Doherty, Paul I. Palmer, Joseph R. Pitt, Chris Rennick, Daniel Say, Kieran M. Stanley, Ann R. Stavert, Dickon Young, and Matt Rigby
Atmos. Chem. Phys., 21, 16257–16276, https://doi.org/10.5194/acp-21-16257-2021, https://doi.org/10.5194/acp-21-16257-2021, 2021
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We present an evaluation of the UK's methane emissions between 2013 and 2020 using a network of tall tower measurement sites. We find emissions that are consistent in both magnitude and trend with the UK's reported emissions, with a declining trend driven by a decrease in emissions from England. The impact of various components of the modelling set-up on these findings are explored through a number of sensitivity studies.
Chris Wilson, Martyn P. Chipperfield, Manuel Gloor, Robert J. Parker, Hartmut Boesch, Joey McNorton, Luciana V. Gatti, John B. Miller, Luana S. Basso, and Sarah A. Monks
Atmos. Chem. Phys., 21, 10643–10669, https://doi.org/10.5194/acp-21-10643-2021, https://doi.org/10.5194/acp-21-10643-2021, 2021
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Methane (CH4) is an important greenhouse gas emitted from wetlands like those found in the basin of the Amazon River. Using an atmospheric model and observations from GOSAT, we quantified CH4 emissions from Amazonia during the previous decade. We found that the largest emissions came from a region in the eastern basin and that emissions there were rising faster than in other areas of South America. This finding was supported by CH4 observations made on aircraft within the basin.
Cited articles
Anderson, K. S., Hansen, C. W., Holmgren, W. F., Jensen, A. R., Mikofski, M. A., and Driesse, A.: pvlib python: 2023 project update, Journal of Open Source Software, 8, 5994, https://doi.org/10.21105/joss.05994, 2023. a
Andersson, A.: A Model for the Spectral Dependence of Aerosol Sunlight Absorption, ACS Earth and Space Chemistry, 1, 533–539, https://doi.org/10.1021/acsearthspacechem.7b00066, 2017. a
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., Naik, V., Palmer, M., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J., Storelvmo, T., Thorne, P., Trewin, B., Rao, K. A., Adhikary, B., Allan, R., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J., Cassou, C., Cherchi, A., Collins, W., Collins, W., Connors, S., Corti, S., Cruz, F., Dentener, F., Dereczynski, C., Luca, A. D., Niang, A. D., Doblas-Reyes, F., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V., Fischer, E., Forster, P., Fox-Kemper, B., Fuglestvedt, J., Fyfe, J., Gillett, N., Goldfarb, L., Gorodetskaya, I., Gutierrez, J., Hamdi, R., Hawkins, E., Hewitt, H., Hope, P., Islam, A., Jones, C., Kaufman, D., Kopp, R., Kosaka, Y., Kossin, J., Krakovska, S., Lee, J.-Y., Li, J., Mauritsen, T., Maycock, T., Meinshausen, M., Min, S.-K., Monteiro, P., Ngo-Duc, T., Otto, F., Pinto, I., Pirani, A., Raghavan, K., Ranasinghe, R., Ruane, A., Ruiz, L., Sallée, J.-B., Samset, B., Sathyendranath, S., Seneviratne, S., Sörensson, A., Szopa, S., Takayabu, I., Tréguier, A.-M., van den Hurk, B., Vautard, R., von Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Technical Summary, Cambridge University Press, 35–144, https://doi.org/10.1017/9781009157896.002, 2021. a, b
Barton, I. J., Zavody, A. M., O'Brien, D. M., Cutten, D. R., Saunders, R. W., and Llewellyn-Jones, D. T.: Theoretical algorithms for satellite-derived sea surface temperatures, J. Geophys. Res.-Atmos., 94, 3365–3375, https://doi.org/10.1029/jd094id03p03365, 1989. a
Bevan, S. L., North, P. R., Los, S. O., and Grey, W. M.: A global dataset of atmospheric aerosol optical depth and surface reflectance from AATSR, Remote Sens. Environ., 116, 199–210, https://doi.org/10.1016/j.rse.2011.05.024, 2012. a
Blumstein, D., Chalon, G., Carlier, T., Buil, C., Hebert, P., Maciaszek, T., Ponce, G., Phulpin, T., Tournier, B., Simeoni, D., Astruc, P., Clauss, A., Kayal, G., and Jegou, R.: IASI instrument: technical overview and measured performances, Proc. SPIE 5543, Infrared Spaceborne Remote Sensing XII, Optical Science and Technology, SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States, https://doi.org/10.1117/12.560907, 2004. a, b
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X.: Clouds and Aerosols, Cambridge University Press, https://doi.org/10.1017/CBO9781107415324.016, 2013. a
Burrows, S. M., McCluskey, C. S., Cornwell, G., Steinke, I., Zhang, K., Zhao, B., Zawadowicz, M., Raman, A., Kulkarni, G., China, S., Zelenyuk, A., and DeMott, P. J.: Ice-Nucleating Particles That Impact Clouds and Climate: Observational and Modeling Research Needs, Rev. Geophys., 60, e2021RG000745, https://doi.org/10.1029/2021rg000745, 2022. a
Callewaert, S., Vandenbussche, S., Kumps, N., Kylling, A., Shang, X., Komppula, M., Goloub, P., and De Mazière, M.: The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation, Atmos. Meas. Tech., 12, 3673–3698, https://doi.org/10.5194/amt-12-3673-2019, 2019. a
Callies, J., Corpaccioli, E., Eisinger, M., Hahne, A., and Lefebvre, A.: GOME-2-Metop's second-generation sensor for operational ozone monitoring, ESA Bull., 102, 28–36, 2000. a
Capelle, V., Chédin, A., Siméon, M., Tsamalis, C., Pierangelo, C., Pondrom, M., Crevoisier, C., Crepeau, L., and Scott, N. A.: Evaluation of IASI-derived dust aerosol characteristics over the tropical belt, Atmos. Chem. Phys., 14, 9343–9362, https://doi.org/10.5194/acp-14-9343-2014, 2014. a
Charlson, R. J., Schwartz, S. E., Hales, J. M., Cess, R. D., Coakley, J. A., Hansen, J. E., and Hofmann, D. J.: Climate Forcing by Anthropogenic Aerosols, Science, 255, 423–430, https://doi.org/10.1126/science.255.5043.423, 1992. a
Clarisse, L., Clerbaux, C., Franco, B., Hadji‐Lazaro, J., Whitburn, S., Kopp, A. K., Hurtmans, D., and Coheur, P.: A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements, J. Geophys. Res.-Atmos., 124, 1618–1647, https://doi.org/10.1029/2018jd029701, 2019. a
Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmos. Chem. Phys., 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009. a, b
Coppo, P., Ricciarelli, B., Brandani, F., Delderfield, J., Ferlet, M., Mutlow, C., Munro, G., Nightingale, T., Smith, D., Bianchi, S., Nicol, P., Kirschstein, S., Hennig, T., Engel, W., Frerick, J., and Nieke, J.: SLSTR: a high accuracy dual scan temperature radiometer for sea and land surface monitoring from space, J. Mod. Optic., 57, 1815–1830, https://doi.org/10.1080/09500340.2010.503010, 2010. a, b, c
Drummond, A. and Thekaekara, M.: The Extraterrestrial Solar Spectrum, Institute of Environmental Sciences, ISBN 9780915414437, 1973. a
Dubovik, O., Fuertes, D., Litvinov, P., Lopatin, A., Lapyonok, T., Doubovik, I., Xu, F., Ducos, F., Chen, C., Torres, B., Derimian, Y., Li, L., Herreras-Giralda, M., Herrera, M., Karol, Y., Matar, C., Schuster, G. L., Espinosa, R., Puthukkudy, A., Li, Z., Fischer, J., Preusker, R., Cuesta, J., Kreuter, A., Cede, A., Aspetsberger, M., Marth, D., Bindreiter, L., Hangler, A., Lanzinger, V., Holter, C., and Federspiel, C.: A Comprehensive Description of Multi-Term LSM for Applying Multiple a Priori Constraints in Problems of Atmospheric Remote Sensing: GRASP Algorithm, Concept, and Applications, Frontiers in Remote Sensing, 2, https://doi.org/10.3389/frsen.2021.706851, 2021a. a
Dubovik, O., Schuster, G. L., Xu, F., Hu, Y., Bösch, H., Landgraf, J., and Li, Z.: Grand Challenges in Satellite Remote Sensing, Frontiers in Remote Sensing, 2, https://doi.org/10.3389/frsen.2021.619818, 2021b. a
EUMETSAT: GOME-2 Products Guide; Ref.: EUM/OPS-EPS/MAN/05/0005, Tech. rep., https://www-cdn.eumetsat.int/files/2020-04/pdf_man_050005_gome2-pg.pdf (last access: 5 September 2024), 2005. a
EUMETSAT: Validation Report – IASI-A Level 1c FCDR release 1, techreport, EUMETSAT, doc.-No.: EUM/RSP/REP/18/1024768; Issue: v3A e-signed, https://user.eumetsat.int/s3/eup-strapi-media/Product_User_Guide_IASI_Level_1c_FCDR_release_2_v1_A_d32efe2626.pdf (last access: 24 April 2025), 2019. a
EUMETSAT: GOME-2 Metop-A and -B FDR Product Validation Report Reprocessing R3, techreport, EUMETSAT, doc.-No.: EUM/OPS/DOC/21/1237264; Issue: v4 e-signed, https://user.eumetsat.int/s3/eup-strapi-media/GOME_2_Metop_A_and_B_FDR_Product_Validation_Report_Reprocessing_R3_8a6a49db6c.pdf (last access: 24 April 2025), 2022. a
EUMETSAT AC SAF: GOME-2 Instrument, https://acsaf.org/gome-2.php, last access: 8 July 2024. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity, Cambridge University Press, 923–1054, ISBN 9781009157896, https://doi.org/10.1017/9781009157896.002, 2021. a, b
Gliß, J., Mortier, A., Schulz, M., Andrews, E., Balkanski, Y., Bauer, S. E., Benedictow, A. M. K., Bian, H., Checa-Garcia, R., Chin, M., Ginoux, P., Griesfeller, J. J., Heckel, A., Kipling, Z., Kirkevåg, A., Kokkola, H., Laj, P., Le Sager, P., Lund, M. T., Lund Myhre, C., Matsui, H., Myhre, G., Neubauer, D., van Noije, T., North, P., Olivié, D. J. L., Rémy, S., Sogacheva, L., Takemura, T., Tsigaridis, K., and Tsyro, S. G.: AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations, Atmos. Chem. Phys., 21, 87–128, https://doi.org/10.5194/acp-21-87-2021, 2021. a
Global Modeling And Assimilation Office and Pawson, S.: MERRA-2 inst3_2d_gas_Nx: 2d, 3-Hourly, Instantaneous, Single-Level, Assimilation, Aerosol Optical Depth Analysis V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/HNGA0EWW0R09, 2015a. a, b, c
Global Modeling And Assimilation Office and Pawson, S.: MERRA-2 inst3_3d_aer_Nv: 3d, 3-Hourly, Instantaneous, Model-Level, Assimilation, Aerosol Mixing Ratio V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set],https://doi.org/10.5067/LTVB4GPCOTK2, 2015b. a, b, c
Grzegorski, M., Poli, G., Cacciari, A., Jafariserajehlou, S., Holdak, A., Lang, R., Vazquez-Navarro, M., Munro, R., and Fougnie, B.: Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation, Remote Sens., 14, 85, https://doi.org/10.3390/rs14010085, 2021. a, b
Hasekamp, O. P. and Landgraf, J.: Retrieval of aerosol properties over the ocean from multispectral single-viewing-angle measurements of intensity and polarization: Retrieval approach, information content, and sensitivity study, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2005JD006212, 2005. a, b
Hasekamp, O. P., Landgraf, J., and van Oss, R.: Aerosol retrieval studies for GOME-2, Ozone saf visiting scientist report, https://www-cdn.eumetsat.int/files/2020-04/pdf_saf_vsr_02.pdf (last access: 24 April 2025), 2004. a
Hébert, P., Blumstein, D., Buil, C., Carlier, T., Chalon, G., Astruc, P., Clauss, A., Siméoni, D., and Tournier, B.: IASI instrument: technical description and measured performances, in: International Conference on Space Optics – ICSO 2004, edited by: Costeraste, J. and Armandillo, E., International Society for Optics and Photonics, SPIE, Toulouse, France, 10568, 1056806, https://doi.org/10.1117/12.2308007, 2017. a
Holmlund, K., Bojkov, B., Klaes, D., and Schlussel, P.: The joint polar system: Towards the second generation eumetsat polar system, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 2017, 2779–2782, https://doi.org/10.1109/IGARSS.2017.8127574, 2017. a
Holzer-Popp, T. and Schroedter, M.: Retrieval of aerosol properties over land and ocean by exploiting the synergy of GOME and ATSR-2 data, in: IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg, Germany, IGARSS'99 (Cat. No.99CH36293), 1, 359–361, https://doi.org/10.1109/IGARSS.1999.773497, 1999. a
Holzer-Popp, T., Schroedter-Homscheidt, M., Breitkreuz, H., Martynenko, D., and Klüser, L.: Improvements of synergetic aerosol retrieval for ENVISAT, Atmos. Chem. Phys., 8, 7651–7672, https://doi.org/10.5194/acp-8-7651-2008, 2008. a, b, c
Holzer-Popp, T., de Leeuw, G., Griesfeller, J., Martynenko, D., Klüser, L., Bevan, S., Davies, W., Ducos, F., Deuzé, J. L., Graigner, R. G., Heckel, A., von Hoyningen-Hüne, W., Kolmonen, P., Litvinov, P., North, P., Poulsen, C. A., Ramon, D., Siddans, R., Sogacheva, L., Tanre, D., Thomas, G. E., Vountas, M., Descloitres, J., Griesfeller, J., Kinne, S., Schulz, M., and Pinnock, S.: Aerosol retrieval experiments in the ESA Aerosol_cci project, Atmos. Meas. Tech., 6, 1919–1957, https://doi.org/10.5194/amt-6-1919-2013, 2013. a
Hou, W., Wang, J., Xu, X., and Reid, J. S.: An algorithm for hyperspectral remote sensing of aerosols: 2. Information content analysis for aerosol parameters and principal components of surface spectra, J. Quant. Spectrosc. Ra., 192, 14–29, https://doi.org/10.1016/j.jqsrt.2017.01.041, 2017. a
Hou, W., Li, Z., Wang, J., Xu, X., Goloub, P., and Qie, L.: Improving Remote Sensing of Aerosol Microphysical Properties by Near-Infrared Polarimetric Measurements Over Vegetated Land: Information Content Analysis, J. Geophys. Res.-Atmos., 123, 2215–2243, https://doi.org/10.1002/2017JD027388, 2018. a
Inamdar, A. K., Shi, L., Lee, H.-T., Jackson, D. L., and Matthews, J. L.: Extending the HIRS Data Record with IASI Measurements, Remote Sens., 15, https://doi.org/10.3390/rs15030717, 2023. a
Kacenelenbogen, M. S. F., Tan, Q., Burton, S. P., Hasekamp, O. P., Froyd, K. D., Shinozuka, Y., Beyersdorf, A. J., Ziemba, L., Thornhill, K. L., Dibb, J. E., Shingler, T., Sorooshian, A., Espinosa, R. W., Martins, V., Jimenez, J. L., Campuzano-Jost, P., Schwarz, J. P., Johnson, M. S., Redemann, J., and Schuster, G. L.: Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition?, Atmos. Chem. Phys., 22, 3713–3742, https://doi.org/10.5194/acp-22-3713-2022, 2022. a
Kaufman, Y. J., Tanré, D., and Boucher, O.: A satellite view of aerosols in the climate system, Nature, 419, 215–223, https://doi.org/10.1038/nature01091, 2002. a, b, c, d
Kinne, S., Schulz, M., Textor, C., Guibert, S., Balkanski, Y., Bauer, S. E., Berntsen, T., Berglen, T. F., Boucher, O., Chin, M., Collins, W., Dentener, F., Diehl, T., Easter, R., Feichter, J., Fillmore, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Herzog, M., Horowitz, L., Isaksen, I., Iversen, T., Kirkevåg, A., Kloster, S., Koch, D., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque, J. F., Lesins, G., Liu, X., Lohmann, U., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, O., Stier, P., Takemura, T., and Tie, X.: An AeroCom initial assessment – optical properties in aerosol component modules of global models, Atmos. Chem. Phys., 6, 1815–1834, https://doi.org/10.5194/acp-6-1815-2006, 2006. a
Klüser, L., Kleiber, P., Holzer-Popp, T., and Grassian, V.: Desert dust observation from space – Application of measured mineral component infrared extinction spectra, Atmos. Environ., 54, 419–427, https://doi.org/10.1016/j.atmosenv.2012.02.011, 2012. a
Klüser, L., Banks, J., Martynenko, D., Bergemann, C., Brindley, H., and Holzer-Popp, T.: Information content of space-borne hyperspectral infrared observations with respect to mineral dust properties, Remote Sens. Environ., 156, 294–309, https://doi.org/10.1016/j.rse.2014.09.036, 2015. a
Kok, J. F., Storelvmo, T., Karydis, V. A., Adebiyi, A. A., Mahowald, N. M., Evan, A. T., He, C., and Leung, D. M.: Mineral dust aerosol impacts on global climate and climate change, Nat. Rev. Earth Environ., 4, 71–86, https://doi.org/10.1038/s43017-022-00379-5, 2023. a, b
Köpke, P.: Satellitenmeteorologie, Ulmer, Stuttgart, ISBN 978-3-8252-3525-3, 2012. a
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., Dubovik, O., Ding, A., Lacis, A. A., Zhang, L., and Dong, Y.: Scattering and absorbing aerosols in the climate system, Nat. Revi. Earth Environ., 3, 363–379, https://doi.org/10.1038/s43017-022-00296-7, 2022. a, b, c
Li, L., Dubovik, O., Derimian, Y., Schuster, G. L., Lapyonok, T., Litvinov, P., Ducos, F., Fuertes, D., Chen, C., Li, Z., Lopatin, A., Torres, B., and Che, H.: Retrieval of aerosol components directly from satellite and ground-based measurements, Atmos. Chem. Phys., 19, 13409–13443, https://doi.org/10.5194/acp-19-13409-2019, 2019. a
Li, L., Che, H., Derimian, Y., Dubovik, O., Schuster, G. L., Chen, C., Li, Q., Wang, Y., Guo, B., and Zhang, X.: Retrievals of fine mode light-absorbing carbonaceous aerosols from POLDER/PARASOL observations over East and South Asia, Remote Sens. Environ., 247, 111913, https://doi.org/10.1016/j.rse.2020.111913, 2020. a
Loyola, D., Erbertseder, T., Balis, D., Lambert, J.-C., Spurr, R., Van Roozendael, M., Valks, P., Zimmer, W., Meyer-Arnek, J., and Lerot, C.: Operational Monitoring of the Antarctic Ozone Hole: Transition from GOME and SCIAMACHY to GOME-2, Springer Netherlands, ISBN 9789048124695, 213–236, https://doi.org/10.1007/978-90-481-2469-5_16, 2009. a
Maahn, M., Turner, D. D., Löhnert, U., Posselt, D. J., Ebell, K., Mace, G. G., and Comstock, J. M.: Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know, B. Am. Meteor. Soc., 101, E1512–E1523, https://doi.org/10.1175/BAMS-D-19-0027.1, 2020. a, b
Martynenko, D., Holzer-Popp, T., Elbern, H., and Schroedter-Homscheidt, M.: Understanding the aerosol information content in multi-spectral reflectance measurements using a synergetic retrieval algorithm, Atmos. Meas. Tech., 3, 1589–1598, https://doi.org/10.5194/amt-3-1589-2010, 2010. a, b
Matsui, H., Hamilton, D. S., and Mahowald, N. M.: Black carbon radiative effects highly sensitive to emitted particle size when resolving mixing-state diversity, Nat. Commun., 9, 446, https://doi.org/10.1038/s41467-018-05635-1, 2018. a
Mei, L., Rozanov, V., Rozanov, A., and Burrows, J. P.: SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models, Geosci. Model Dev., 16, 1511–1536, https://doi.org/10.5194/gmd-16-1511-2023, 2023. a, b
Munro, R., Eisinger, M., Anderson, C., Callies, J., Corpaccioli, E., Lang, R., Lefebvre, A., and Livschitz, Y.: GOME-2 on MetOp, Proceedings of The 2006 EUMETSAT Meteorological Satellite Conference, Helsinki, Finland, 1216, 48, https://www-cdn.eumetsat.int/files/2020-04/pdf_conf_p48_s4_01_munro_v.pdf, (last access: 24 April 2025), 2006. a
Munro, R., Lang, R., Klaes, D., Poli, G., Retscher, C., Lindstrot, R., Huckle, R., Lacan, A., Grzegorski, M., Holdak, A., Kokhanovsky, A., Livschitz, J., and Eisinger, M.: The GOME-2 instrument on the Metop series of satellites: instrument design, calibration, and level 1 data processing – an overview, Atmos. Meas. Tech., 9, 1279–1301, https://doi.org/10.5194/amt-9-1279-2016, 2016. a
Myhre, G., Aas, W., Cherian, R., Collins, W., Faluvegi, G., Flanner, M., Forster, P., Hodnebrog, Ø., Klimont, Z., Lund, M. T., Mülmenstädt, J., Lund Myhre, C., Olivié, D., Prather, M., Quaas, J., Samset, B. H., Schnell, J. L., Schulz, M., Shindell, D., Skeie, R. B., Takemura, T., and Tsyro, S.: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015, Atmos. Chem. Phys., 17, 2709–2720, https://doi.org/10.5194/acp-17-2709-2017, 2017. a
NREL: Solar Spectra – The Thekaekara Spectrum, https://www.nrel.gov/grid/solar-resource/spectra.html, last access: 8 July 2024. a
Popp, T.: Correcting atmospheric masking to retrieve the spectral albedo of land surfaces from satellite measurements, Int. J. Remote Sens., 16, 3483–3508, https://doi.org/10.1080/01431169508954642, 1995. a
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation, J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017. a, b, c, d
Rodgers, C. D.: Information content and optimization of high-spectral-resolution measurements, Proc. SPIE 2830, Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research II, https://doi.org/10.1117/12.256110, 1996. a
Rozanov, A.: User's Guide for the Software Package SCIATRAN (Radiative Transfer Model and Retrieval Algorithms), Institute of Remote Sensing University of Bremen, Germany, https://www.iup.uni-bremen.de/sciatran/free_downloads/users_guide_sciatran.pdf (last access: 24 April 2025), 2022. a
Rozanov, V., Rozanov, A., Kokhanovsky, A., and Burrows, J.: Radiative transfer through terrestrial atmosphere and ocean: Software package SCIATRAN, J. Quant. Spectrosc. Ra., 133, 13–71, https://doi.org/10.1016/j.jqsrt.2013.07.004, 2014. a, b
Samset, B. H., Stjern, C. W., Andrews, E., Kahn, R. A., Myhre, G., Schulz, M., and Schuster, G. L.: Aerosol Absorption: Progress Towards Global and Regional Constraints, Current Climate Change Reports, 4, 65–83, https://doi.org/10.1007/s40641-018-0091-4, 2018. a
Sayer, A. M., Thomas, G. E., and Grainger, R. G.: A sea surface reflectance model for (A)ATSR, and application to aerosol retrievals, Atmos. Meas. Tech., 3, 813–838, https://doi.org/10.5194/amt-3-813-2010, 2010. a
Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J., Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas, I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather, K. A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R., Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system, P. Natl. Acad. Sci. USA, 113, 5781–5790, https://doi.org/10.1073/pnas.1514043113, 2016. a
Sherwood, S. C., Bony, S., Boucher, O., Bretherton, C., Forster, P. M., Gregory, J. M., and Stevens, B.: Adjustments in the Forcing-Feedback Framework for Understanding Climate Change, B. Am. Meteor. Soc., 96, 217–228, https://doi.org/10.1175/bams-d-13-00167.1, 2015. a
Simeoni, D., Astruc, P., Miras, D., Alis, C., Andreis, O., Scheidel, D., Degrelle, C., Nicol, P., Bailly, B., Guiard, P., Clauss, A., Blumstein, D., Maciaszek, T., Chalon, G., Carlier, T., and Kayal, G.: Design and development of IASI instrument, in: Infrared Spaceborne Remote Sensing XII, edited by: Strojnik, M., International Society for Optics and Photonics, SPIE, 5543, 208–219, https://doi.org/10.1117/12.561090, 2004. a
Sogacheva, L., Kolmonen, P., Virtanen, T. H., Rodriguez, E., Saponaro, G., and de Leeuw, G.: Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer, Atmos. Meas. Tech., 10, 491–505, https://doi.org/10.5194/amt-10-491-2017, 2017. a
Storelvmo, T.: Aerosol Effects on Climate via Mixed-Phase and Ice Clouds, Annu. Rev. Earth Planet. Sc., 45, 199–222, https://doi.org/10.1146/annurev-earth-060115-012240, 2017. a
Tilstra, L. G., Tuinder, O. N. E., Wang, P., and Stammes, P.: Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME-2 and SCIAMACHY, J. Geophys. Res.-Atmos., 122, 4084–4111, https://doi.org/10.1002/2016jd025940, 2017. a, b
Tilstra, L. G., Tuinder, O. N. E., Wang, P., and Stammes, P.: Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments, Atmos. Meas. Tech., 14, 4219–4238, https://doi.org/10.5194/amt-14-4219-2021, 2021. a, b
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974. a
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977. a
Vandenbussche, S.: Algorithm Theoretical Basis Document Mineral Aerosol Profiling from Infrared Radiances (MAPIR) Version 4.1b, Tech. rep., Copernicus Climate Change Service, https://wdc.dlr.de/C3S_312b_Lot2/Documentation/AER/C3S2_312a_Lot2_ATBD_AER_E_latest.pdf (last access: 24 April 2025), 2021. a, b
Vandenbussche, S., Kochenova, S., Vandaele, A. C., Kumps, N., and De Mazière, M.: Retrieval of desert dust aerosol vertical profiles from IASI measurements in the TIR atmospheric window, Atmos. Meas. Tech., 6, 2577–2591, https://doi.org/10.5194/amt-6-2577-2013, 2013. a
Wiacek, A., Peter, T., and Lohmann, U.: The potential influence of Asian and African mineral dust on ice, mixed-phase and liquid water clouds, Atmos. Chem. Phys., 10, 8649–8667, https://doi.org/10.5194/acp-10-8649-2010, 2010. a
World Meteorological Organization (WMO): Sentinel-3C, https://space.oscar.wmo.int/satellites/view/sentinel_3c, last access: 21 January 2025a. a
World Meteorological Organization (WMO): Sentinel-3D, https://space.oscar.wmo.int/satellites/view/sentinel_3d, last access: 21 January 2025b. a
Yin, Y., Wurzler, S., Levin, Z., and Reisin, T. G.: Interactions of mineral dust particles and clouds: Effects on precipitation and cloud optical properties, J. Geophys. Res.-Atmos., 107, AAC 19-1–AAC 19-14 https://doi.org/10.1029/2001JD001544, 2002. a
Zhang, X., Li, L., Chen, C., Chen, X., Dubovik, O., Derimian, Y., Gui, K., Zheng, Y., Zhao, H., Zhang, L., Guo, B., Wang, Y., Holben, B., Che, H., and Zhang, X.: Validation of the aerosol optical property products derived by the GRASP/Component approach from multi-angular polarimetric observations, Atmos. Res., 263, 105802, https://doi.org/10.1016/j.atmosres.2021.105802, 2021. a
Zhou, D. K., Larar, A. M., Liu, X., Smith, W. L., Strow, L. L., Yang, P., Schlussel, P., and Calbet, X.: Global Land Surface Emissivity Retrieved From Satellite Ultraspectral IR Measurements, IEEE T. Geosci. Remote, 49, 1277–1290, https://doi.org/10.1109/TGRS.2010.2051036, 2011. a
Zhou, D. K., Larar, A. M., and Liu, X.: MetOp-A/IASI Observed Continental Thermal IR Emissivity Variations, IEEE J. Sel. Top. Appl., 6, 1156–1162, https://doi.org/10.1109/JSTARS.2013.2238892, 2013. a
Zhou, D. K., Larar, A. M., and Liu, X.: On the relationship between land surface infrared emissivity and soil moisture, J. Appl. Remote Sens., 12, 1, https://doi.org/10.1117/1.JRS.12.016030, 2018. a
Zhou, D. K., Larar, A. M., and Liu, X.: Surface Skin Temperature and Its Trend Observations From IASI on Board MetOp Satellites, IEEE J. Sel. Top. Appl. Earth, 14, 1665–1675, https://doi.org/10.1109/JSTARS.2020.3046421, 2021. a
Zhou, Y., Brunner, D., Spurr, R. J. D., Boersma, K. F., Sneep, M., Popp, C., and Buchmann, B.: Accounting for surface reflectance anisotropy in satellite retrievals of tropospheric NO2, Atmos. Meas. Tech., 3, 1185–1203, https://doi.org/10.5194/amt-3-1185-2010, 2010. a
Short summary
Aerosol composition has a large influence on the climate system. This study uses realistic simulated scenarios to look at the information content of a combination of three satellite-based instruments (SLSTR, IASI and GOME-2). It shows that it is possible to retrieve 6 to 15 different aerosol components in addition to the aerosol optical depth (AOD) and different surface parameters. The results are used for the development of a synergistic multi-sensor retrieval algorithm.
Aerosol composition has a large influence on the climate system. This study uses realistic...
