Articles | Volume 18, issue 7
https://doi.org/10.5194/amt-18-1675-2025
© 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-18-1675-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Apr 2025
Research article |
| 11 Apr 2025
| 11 Apr 2025
Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction
Alexander C. Bradley
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
Chemistry Department, University of Colorado, Boulder, CO 80309, USA
Barbara Dix
Chemistry Department, University of Colorado, Boulder, CO 80309, USA
Fergus Mackenzie
BlueSky Resources, Boulder CO 80302, USA
J. Pepijn Veefkind
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Department of Geosciences and Remote Sensing, Delft University of Technology, Delft, the Netherlands
Joost A. de Gouw
CORRESPONDING AUTHOR
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
Chemistry Department, University of Colorado, Boulder, CO 80309, USA
Related authors
No articles found.
Andre F. Schaum, Christopher M. Kenseth, Madison Rutherford, Harald Stark, Manjula R. Canagaratna, Joost A. de Gouw, Jose L. Jimenez, and Kelvin H. Bates
Aerosol Research Discuss., https://doi.org/10.5194/ar-2026-13, https://doi.org/10.5194/ar-2026-13, 2026
Preprint under review for AR
Short summary
Short summary
Organic aerosols influence global climate though uncertainties in the magnitude of this influence remain large. A comprehensive understanding of the molecular composition of aerosols is key to minimizing these uncertainties. We present a new method for analyzing organic aerosols in real time using ion mobility mass spectrometry, enabling the separation of aerosol components with identical mass. Analysis of standards and laboratory-generated aerosols demonstrate the method’s potential.
Melissa A. Ehrenfels, Benjamin C. Schulze, Andrew R. Jensen, Afsara Tasnia, Douglas A. Day, Pedro Campuzano-Jost, Anne V. Handschy, Melinda K. Schueneman, Seonsik Yun, Dongwook Kim, Donna Sueper, Havala O. T. Pye, Benjamin N. Murphy, T. Nash Skipper, Kelley C. Barsanti, Joost A. de Gouw, and Jose L. Jimenez
Aerosol Research Discuss., https://doi.org/10.5194/ar-2026-12, https://doi.org/10.5194/ar-2026-12, 2026
Preprint under review for AR
Short summary
Short summary
Los Angeles, CA suffers from poor air quality, partially due to secondary aerosol formation. The identity and sources of the most important gas phase precursors is a complex and unresolved issue. For the first time, we separated ambient gas-phase species by volatility, then measured their potential to form aerosols. We found that higher-volatility emissions are responsible for 2/3 of the aerosol formation, and lower volatility ones for 1/3. We evaluate two box models against our measurements.
Serena Di Pede, Erwin Loots, Antje Ludewig, Emiel van der Plas, Edward van Amelrooy, Mirna van Hoek, Maarten Sneep, Mark ter Linden, Arno Keppens, and J. Pepijn Veefkind
Atmos. Meas. Tech., 19, 1875–1899, https://doi.org/10.5194/amt-19-1875-2026, https://doi.org/10.5194/amt-19-1875-2026, 2026
Short summary
Short summary
TROPOMI (TROPOspheric Monitoring Instrument) provides daily ozone profile data from ultraviolet bands 1–2. Accurate radiometric calibration is critical below 300 nm where signals are low. A Level-2 soft calibration complements L1 calibration and reveals spectral, temporal, and across-track biases. Re-analysis of calibration data enabled improved L1 corrections to address remaining straylight and residual signal effects, reducing soft calibration magnitude by ~15–20 % and associated biases in L1b v3.0 and ozone processor v2.9.0.
Tobias Borsdorff, Maarten Krol, Pepijn Veefkind, and Jochen Landgraf
EGUsphere, https://doi.org/10.5194/egusphere-2026-1134, https://doi.org/10.5194/egusphere-2026-1134, 2026
Short summary
Short summary
Industrial facilities release carbon dioxide and nitrogen dioxide. The TANGO mission, launching in 2028, will monitor both gases from ten thousand facilities per year using two satellites. We studied whether combining both improves carbon dioxide emission estimates and reveals plume chemistry. While this produces cleaner carbon dioxide images, precision does not improve as noise is redistributed not eliminated. Their ratio captures how nitrogen oxide converts to nitrogen dioxide within plumes.
Jos van Geffen, Henk Eskes, Maarten Sneep, Mark ter Linden, and Pepijn Veefkind
EGUsphere, https://doi.org/10.5194/egusphere-2025-5836, https://doi.org/10.5194/egusphere-2025-5836, 2026
Short summary
Short summary
The Fraunhofer absorption feature at 430 nm, which varies in strength with the solar activity cycle influences the retrieval of nitrogen dioxide (NO2) from from Tropospheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) measurements. This study describes the benefits of removing the wavelength range 428–433 nm from the retrieval, which is implemented for TROPOMI retrievals as of v2.9.1.
Robert J. D. Spurr, Matt Christi, Nickolay A. Krotkov, Won-Ei Choi, Simon Carn, Can Li, Natalya Kramarova, David Haffner, Eun-Su Yang, Nick Gorkavyi, Alexander Vasilkov, Krzysztof Wargan, Omar Torres, Diego Loyola, Serena Di Pede, J. Pepijn Veefkind, Parker Case, Thomas Schroeder, and Pawan K. Bhartia
Atmos. Meas. Tech., 19, 993–1021, https://doi.org/10.5194/amt-19-993-2026, https://doi.org/10.5194/amt-19-993-2026, 2026
Short summary
Short summary
The submarine eruption of the Hunga volcano released water vapor and sulfur dioxide directly into the stratosphere. The sulfur dioxide formed sulfuric acid aerosols leading to increased scattering of solar ultraviolet radiation (UV), interfering with satellite ozone retrievals. We present a new satellite technique for deriving the amount and height of Hunga aerosols from UV measurements, revealing an unusually low sulfuric acid concentration due to the water-rich conditions in the fresh plume.
Arno Keppens, Daan Hubert, José Granville, Oindrila Nath, Jean-Christopher Lambert, Catherine Wespes, Pierre-François Coheur, Cathy Clerbaux, Anne Boynard, Richard Siddans, Barry Latter, Brian Kerridge, Serena Di Pede, Pepijn Veefkind, Juan Cuesta, Gaelle Dufour, Klaus-Peter Heue, Melanie Coldewey-Egbers, Diego Loyola, Andrea Orfanoz-Cheuquelaf, Swathi Maratt Satheesan, Kai-Uwe Eichmann, Alexei Rozanov, Viktoria F. Sofieva, Jerald R. Ziemke, Antje Inness, Roeland Van Malderen, and Lars Hoffmann
Atmos. Meas. Tech., 18, 6893–6916, https://doi.org/10.5194/amt-18-6893-2025, https://doi.org/10.5194/amt-18-6893-2025, 2025
Short summary
Short summary
The first Tropospheric Ozone Assessment Report (TOAR) encountered discrepancies between several satellite sensors’ estimates of the distribution and change of ozone in the free troposphere. Therefore, contributing to the second TOAR, we harmonise as much as possible the observational perspective of sixteen tropospheric ozone products from satellites. This only partially accounts for the observed discrepancies, with a reduction of 10–40 % of the inter-product dispersion upon harmonisation.
Harikrishnan Charuvil Asokan, Jochen Landgraf, Pepijn Veefkind, Stijn Dellaert, and André Butz
Atmos. Meas. Tech., 18, 5247–5264, https://doi.org/10.5194/amt-18-5247-2025, https://doi.org/10.5194/amt-18-5247-2025, 2025
Short summary
Short summary
Greenhouse gases like CO2 and CH4 drive climate change. Satellites enable monitoring of these emissions from space. Our simulations show that the upcoming Twin ANthropogenic Greenhouse gas Observers (TANGO) mission can detect about 500 targets per 4 d cycle under clear skies, but cloud cover reduces detection. Integrating cloud forecasts into TANGO’s manoeuvering boosts detection, highlighting its potential for improving global emission monitoring.
Huan Yu, Isabelle De Smedt, Nicolas Theys, Maarten Sneep, Pepijn Veefkind, and Michel Van Roozendael
Atmos. Meas. Tech., 18, 4131–4163, https://doi.org/10.5194/amt-18-4131-2025, https://doi.org/10.5194/amt-18-4131-2025, 2025
Short summary
Short summary
We introduce a new cloud retrieval algorithm using the O2–O2 absorption band at 477 nm to generate harmonized cloud datasets from OMI and TROPOMI. The algorithm improves upon the OMI O2–O2 operational cloud algorithm in several aspects. The new approach improves consistency in cloud parameters and NO2 retrievals between two sensors.
Martin de Graaf, Maarten Sneep, Mark ter Linden, L. Gijsbert Tilstra, David P. Donovan, Gerd-Jan van Zadelhoff, and J. Pepijn Veefkind
Atmos. Meas. Tech., 18, 2553–2571, https://doi.org/10.5194/amt-18-2553-2025, https://doi.org/10.5194/amt-18-2553-2025, 2025
Short summary
Short summary
The aerosol layer height (ALH) from the TROPOspheric Monitoring Instrument (TROPOMI) has been constantly improved since its release in 2019. Over bright surfaces, fitting the albedo improved the retrieval, as shown for a set of situations, ranging from multiple layers of smoke to thick desert dust plumes and low-altitude industrial pollution. The latest results of the operational ALH are compared to profiles from the ATmospheric LIDar (ATLID) on board the recently launched EarthCARE mission.
Michael F. Link, Megan S. Claflin, Christina E. Cecelski, Ayomide A. Akande, Delaney Kilgour, Paul A. Heine, Matthew Coggon, Chelsea E. Stockwell, Andrew Jensen, Jie Yu, Han N. Huynh, Jenna C. Ditto, Carsten Warneke, William Dresser, Keighan Gemmell, Spiro Jorga, Rileigh L. Robertson, Joost de Gouw, Timothy Bertram, Jonathan P. D. Abbatt, Nadine Borduas-Dedekind, and Dustin Poppendieck
Atmos. Meas. Tech., 18, 1013–1038, https://doi.org/10.5194/amt-18-1013-2025, https://doi.org/10.5194/amt-18-1013-2025, 2025
Short summary
Short summary
Proton-transfer-reaction mass spectrometry (PTR-MS) is widely used for the measurement of volatile organic compounds (VOCs) both indoors and outdoors. An analytical challenge for PTR-MS measurements is the formation of unintended measurement interferences, product ion distributions (PIDs), that may appear in the data as VOCs of interest. We developed a method for quantifying PID formation and use interlaboratory comparison data to put quantitative constraints on PID formation.
Audrey Gaudel, Ilann Bourgeois, Meng Li, Kai-Lan Chang, Jerald Ziemke, Bastien Sauvage, Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, Nadia Smith, Daan Hubert, Arno Keppens, Juan Cuesta, Klaus-Peter Heue, Pepijn Veefkind, Kenneth Aikin, Jeff Peischl, Chelsea R. Thompson, Thomas B. Ryerson, Gregory J. Frost, Brian C. McDonald, and Owen R. Cooper
Atmos. Chem. Phys., 24, 9975–10000, https://doi.org/10.5194/acp-24-9975-2024, https://doi.org/10.5194/acp-24-9975-2024, 2024
Short summary
Short summary
The study examines tropical tropospheric ozone changes. In situ data from 1994–2019 display increased ozone, notably over India, Southeast Asia, and Malaysia and Indonesia. Sparse in situ data limit trend detection for the 15-year period. In situ and satellite data, with limited sampling, struggle to consistently detect trends. Continuous observations are vital over the tropical Pacific Ocean, Indian Ocean, western Africa, and South Asia for accurate ozone trend estimation in these regions.
Mengyao Liu, Ronald van der A, Michiel van Weele, Lotte Bryan, Henk Eskes, Pepijn Veefkind, Yongxue Liu, Xiaojuan Lin, Jos de Laat, and Jieying Ding
Atmos. Meas. Tech., 17, 5261–5277, https://doi.org/10.5194/amt-17-5261-2024, https://doi.org/10.5194/amt-17-5261-2024, 2024
Short summary
Short summary
A new divergence method was developed and applied to estimate methane emissions from TROPOMI observations over the Middle East, where it is typically challenging for a satellite to measure methane due to its complicated orography and surface albedo. Our results show the potential of TROPOMI to quantify methane emissions from various sources rather than big emitters from space after objectively excluding the artifacts in the retrieval.
Arno Keppens, Serena Di Pede, Daan Hubert, Jean-Christopher Lambert, Pepijn Veefkind, Maarten Sneep, Johan De Haan, Mark ter Linden, Thierry Leblanc, Steven Compernolle, Tijl Verhoelst, José Granville, Oindrila Nath, Ann Mari Fjæraa, Ian Boyd, Sander Niemeijer, Roeland Van Malderen, Herman G. J. Smit, Valentin Duflot, Sophie Godin-Beekmann, Bryan J. Johnson, Wolfgang Steinbrecht, David W. Tarasick, Debra E. Kollonige, Ryan M. Stauffer, Anne M. Thompson, Angelika Dehn, and Claus Zehner
Atmos. Meas. Tech., 17, 3969–3993, https://doi.org/10.5194/amt-17-3969-2024, https://doi.org/10.5194/amt-17-3969-2024, 2024
Short summary
Short summary
The Sentinel-5P satellite operated by the European Space Agency has carried the TROPOspheric Monitoring Instrument (TROPOMI) around the Earth since October 2017. This mission also produces atmospheric ozone profile data which are described in detail for May 2018 to April 2023. Independent validation using ground-based reference measurements demonstrates that the operational ozone profile product mostly fully and at least partially complies with all mission requirements.
Adrianus de Laat, Jos van Geffen, Piet Stammes, Ronald van der A, Henk Eskes, and J. Pepijn Veefkind
Atmos. Chem. Phys., 24, 4511–4535, https://doi.org/10.5194/acp-24-4511-2024, https://doi.org/10.5194/acp-24-4511-2024, 2024
Short summary
Short summary
Removal of stratospheric nitrogen oxides is crucial for the formation of the ozone hole. TROPOMI satellite measurements of nitrogen dioxide reveal the presence of a not dissimilar "nitrogen hole" that largely coincides with the ozone hole. Three very distinct regimes were identified: inside and outside the ozone hole and the transition zone in between. Our results introduce a valuable and innovative application highly relevant for Antarctic ozone hole and ozone layer recovery.
Melissa A. Morris, Demetrios Pagonis, Douglas A. Day, Joost A. de Gouw, Paul J. Ziemann, and Jose L. Jimenez
Atmos. Meas. Tech., 17, 1545–1559, https://doi.org/10.5194/amt-17-1545-2024, https://doi.org/10.5194/amt-17-1545-2024, 2024
Short summary
Short summary
Polymer absorption of volatile organic compounds (VOCs) is important to characterize for atmospheric sampling setups (as interactions cause sampling delays) and indoor air quality. Here we test different polymer materials and quantify their absorptive capacities through modeling. We found the main polymers in carpets to be highly absorptive, acting as large reservoirs for indoor pollution. We also demonstrated how polymer tubes can be used as a low-cost gas separation technique.
Andrew R. Jensen, Abigail R. Koss, Ryder B. Hales, and Joost A. de Gouw
Atmos. Meas. Tech., 16, 5261–5285, https://doi.org/10.5194/amt-16-5261-2023, https://doi.org/10.5194/amt-16-5261-2023, 2023
Short summary
Short summary
Quantification of a wide range of volatile organic compounds by proton-transfer-reaction mass spectrometry (PTR-MS) can be achieved with direct calibration of only a subset of compounds, characterization of instrument response, and simple reaction kinetics. We characterized our Vocus PTR-MS and developed a toolkit as a guide through this process. A catalytic zero air generator provided the lowest detection limits, and short, frequent calibrations informed variability in instrument response.
Kevin J. Nihill, Matthew M. Coggon, Christopher Y. Lim, Abigail R. Koss, Bin Yuan, Jordan E. Krechmer, Kanako Sekimoto, Jose L. Jimenez, Joost de Gouw, Christopher D. Cappa, Colette L. Heald, Carsten Warneke, and Jesse H. Kroll
Atmos. Chem. Phys., 23, 7887–7899, https://doi.org/10.5194/acp-23-7887-2023, https://doi.org/10.5194/acp-23-7887-2023, 2023
Short summary
Short summary
In this work, we collect emissions from controlled burns of biomass fuels that can be found in the western United States into an environmental chamber in order to simulate their oxidation as they pass through the atmosphere. These findings provide a detailed characterization of the composition of the atmosphere downwind of wildfires. In turn, this will help to explore the effects of these changing emissions on downwind populations and will also directly inform atmospheric and climate models.
Konstantinos Michailidis, Maria-Elissavet Koukouli, Dimitris Balis, J. Pepijn Veefkind, Martin de Graaf, Lucia Mona, Nikolaos Papagianopoulos, Gesolmina Pappalardo, Ioanna Tsikoudi, Vassilis Amiridis, Eleni Marinou, Anna Gialitaki, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Daniele Bortoli, Maria João Costa, Vanda Salgueiro, Alexandros Papayannis, Maria Mylonaki, Lucas Alados-Arboledas, Salvatore Romano, Maria Rita Perrone, and Holger Baars
Atmos. Chem. Phys., 23, 1919–1940, https://doi.org/10.5194/acp-23-1919-2023, https://doi.org/10.5194/acp-23-1919-2023, 2023
Short summary
Short summary
Comparisons with ground-based correlative lidar measurements constitute a key component in the validation of satellite aerosol products. This paper presents the validation of the TROPOMI aerosol layer height (ALH) product, using archived quality assured ground-based data from lidar stations that belong to the EARLINET network. Comparisons between the TROPOMI ALH and co-located EARLINET measurements show good agreement over the ocean.
John Douros, Henk Eskes, Jos van Geffen, K. Folkert Boersma, Steven Compernolle, Gaia Pinardi, Anne-Marlene Blechschmidt, Vincent-Henri Peuch, Augustin Colette, and Pepijn Veefkind
Geosci. Model Dev., 16, 509–534, https://doi.org/10.5194/gmd-16-509-2023, https://doi.org/10.5194/gmd-16-509-2023, 2023
Short summary
Short summary
We focus on the challenges associated with comparing atmospheric composition models with satellite products such as tropospheric NO2 columns. The aim is to highlight the methodological difficulties and propose sound ways of doing such comparisons. Building on the comparisons, a new satellite product is proposed and made available, which takes advantage of higher-resolution, regional atmospheric modelling to improve estimates of troposheric NO2 columns over Europe.
Miriam Latsch, Andreas Richter, Henk Eskes, Maarten Sneep, Ping Wang, Pepijn Veefkind, Ronny Lutz, Diego Loyola, Athina Argyrouli, Pieter Valks, Thomas Wagner, Holger Sihler, Michel van Roozendael, Nicolas Theys, Huan Yu, Richard Siddans, and John P. Burrows
Atmos. Meas. Tech., 15, 6257–6283, https://doi.org/10.5194/amt-15-6257-2022, https://doi.org/10.5194/amt-15-6257-2022, 2022
Short summary
Short summary
The article investigates different S5P TROPOMI cloud retrieval algorithms for tropospheric trace gas retrievals. The cloud products show differences primarily over snow and ice and for scenes under sun glint. Some issues regarding across-track dependence are found for the cloud fractions as well as for the cloud heights.
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050, https://doi.org/10.5194/gmd-15-7031-2022, https://doi.org/10.5194/gmd-15-7031-2022, 2022
Short summary
Short summary
We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.
John T. Sullivan, Arnoud Apituley, Nora Mettig, Karin Kreher, K. Emma Knowland, Marc Allaart, Ankie Piters, Michel Van Roozendael, Pepijn Veefkind, Jerry R. Ziemke, Natalya Kramarova, Mark Weber, Alexei Rozanov, Laurence Twigg, Grant Sumnicht, and Thomas J. McGee
Atmos. Chem. Phys., 22, 11137–11153, https://doi.org/10.5194/acp-22-11137-2022, https://doi.org/10.5194/acp-22-11137-2022, 2022
Short summary
Short summary
A TROPOspheric Monitoring Instrument (TROPOMI) validation campaign (TROLIX-19) was held in the Netherlands in September 2019. The research presented here focuses on using ozone lidars from NASA’s Goddard Space Flight Center to better evaluate the characterization of ozone throughout TROLIX-19 as compared to balloon-borne, space-borne and ground-based passive measurements, as well as a global coupled chemistry meteorology model.
Pieternel F. Levelt, Deborah C. Stein Zweers, Ilse Aben, Maite Bauwens, Tobias Borsdorff, Isabelle De Smedt, Henk J. Eskes, Christophe Lerot, Diego G. Loyola, Fabian Romahn, Trissevgeni Stavrakou, Nicolas Theys, Michel Van Roozendael, J. Pepijn Veefkind, and Tijl Verhoelst
Atmos. Chem. Phys., 22, 10319–10351, https://doi.org/10.5194/acp-22-10319-2022, https://doi.org/10.5194/acp-22-10319-2022, 2022
Short summary
Short summary
Using the COVID-19 lockdown periods as an example, we show how Sentinel-5P/TROPOMI trace gas data (NO2, SO2, CO, HCHO and CHOCHO) can be used to understand impacts on air quality for regions and cities around the globe. We also provide information for both experienced and inexperienced users about how we created the data using state-of-the-art algorithms, where to get the data, methods taking meteorological and seasonal variability into consideration, and insights for future studies.
Quintus Kleipool, Nico Rozemeijer, Mirna van Hoek, Jonatan Leloux, Erwin Loots, Antje Ludewig, Emiel van der Plas, Daley Adrichem, Raoul Harel, Simon Spronk, Mark ter Linden, Glen Jaross, David Haffner, Pepijn Veefkind, and Pieternel F. Levelt
Atmos. Meas. Tech., 15, 3527–3553, https://doi.org/10.5194/amt-15-3527-2022, https://doi.org/10.5194/amt-15-3527-2022, 2022
Short summary
Short summary
A new collection-4 dataset for the Ozone Monitoring Instrument (OMI) mission has been established to supersede the current collection-3 level-1b (L1b) data, produced with a newly developed L01b data processor based on the TROPOspheric Monitoring Instrument (TROPOMI) L01b processor. The collection-4 L1b data have a similar output format to the TROPOMI L1b data for easy connection of the data series. Many insights from the TROPOMI algorithms, as well as from OMI collection-3 usage, were included.
Nora Mettig, Mark Weber, Alexei Rozanov, John P. Burrows, Pepijn Veefkind, Anne M. Thompson, Ryan M. Stauffer, Thierry Leblanc, Gerard Ancellet, Michael J. Newchurch, Shi Kuang, Rigel Kivi, Matthew B. Tully, Roeland Van Malderen, Ankie Piters, Bogumil Kois, René Stübi, and Pavla Skrivankova
Atmos. Meas. Tech., 15, 2955–2978, https://doi.org/10.5194/amt-15-2955-2022, https://doi.org/10.5194/amt-15-2955-2022, 2022
Short summary
Short summary
Vertical ozone profiles from combined spectral measurements in the UV and IR spectral ranges were retrieved by using data from TROPOMI/S5P and CrIS/Suomi-NPP. The vertical resolution and accuracy of the ozone profiles are improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The advancement of our TOPAS algorithm for combined measurements is required because in the UV-only retrieval the vertical resolution in the troposphere is very limited.
Kun Zhang, Zhiqiang Liu, Xiaojuan Zhang, Qing Li, Andrew Jensen, Wen Tan, Ling Huang, Yangjun Wang, Joost de Gouw, and Li Li
Atmos. Chem. Phys., 22, 4853–4866, https://doi.org/10.5194/acp-22-4853-2022, https://doi.org/10.5194/acp-22-4853-2022, 2022
Short summary
Short summary
A significant increase in O3 concentrations was found during the lockdown period of COVID-19 in most areas of China. By field measurements coupled with machine learning, an observation-based model (OBM) and sensitivity analysis, we found the changes in the NOx / VOC ratio were a key reason for the significant rise in O3. To restrain O3 pollution, more efforts should be devoted to the control of anthropogenic OVOCs, alkenes and aromatics.
Jos van Geffen, Henk Eskes, Steven Compernolle, Gaia Pinardi, Tijl Verhoelst, Jean-Christopher Lambert, Maarten Sneep, Mark ter Linden, Antje Ludewig, K. Folkert Boersma, and J. Pepijn Veefkind
Atmos. Meas. Tech., 15, 2037–2060, https://doi.org/10.5194/amt-15-2037-2022, https://doi.org/10.5194/amt-15-2037-2022, 2022
Short summary
Short summary
Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. It compares results with previous versions v1.2–v1.4 and with Ozone Monitoring Instrument (OMI) and ground-based measurements.
Daan Hubert, Klaus-Peter Heue, Jean-Christopher Lambert, Tijl Verhoelst, Marc Allaart, Steven Compernolle, Patrick D. Cullis, Angelika Dehn, Christian Félix, Bryan J. Johnson, Arno Keppens, Debra E. Kollonige, Christophe Lerot, Diego Loyola, Matakite Maata, Sukarni Mitro, Maznorizan Mohamad, Ankie Piters, Fabian Romahn, Henry B. Selkirk, Francisco R. da Silva, Ryan M. Stauffer, Anne M. Thompson, J. Pepijn Veefkind, Holger Vömel, Jacquelyn C. Witte, and Claus Zehner
Atmos. Meas. Tech., 14, 7405–7433, https://doi.org/10.5194/amt-14-7405-2021, https://doi.org/10.5194/amt-14-7405-2021, 2021
Short summary
Short summary
We assess the first 2 years of TROPOMI tropical tropospheric ozone column data. Comparisons to reference measurements by ozonesonde and satellite sensors show that TROPOMI bias (−0.1 to +2.3 DU) and precision (1.5 to 2.5 DU) meet mission requirements. Potential causes of bias and its spatio-temporal structure are discussed, as well as ways to identify sampling errors. Our analysis of known geophysical patterns demonstrates the improved performance of TROPOMI with respect to its predecessors.
Nora Mettig, Mark Weber, Alexei Rozanov, Carlo Arosio, John P. Burrows, Pepijn Veefkind, Anne M. Thompson, Richard Querel, Thierry Leblanc, Sophie Godin-Beekmann, Rigel Kivi, and Matthew B. Tully
Atmos. Meas. Tech., 14, 6057–6082, https://doi.org/10.5194/amt-14-6057-2021, https://doi.org/10.5194/amt-14-6057-2021, 2021
Short summary
Short summary
TROPOMI is a nadir-viewing satellite that has observed global atmospheric trace gases at unprecedented spatial resolution since 2017. The retrieval of ozone profiles with high accuracy has been demonstrated using the TOPAS (Tikhonov regularised Ozone Profile retrievAl with SCIATRAN) algorithm and applying appropriate spectral corrections to TROPOMI UV data. Ozone profiles from TROPOMI were compared to ozonesonde and lidar profiles, showing an agreement to within 5 % in the stratosphere.
Benjamin A. Nault, Duseong S. Jo, Brian C. McDonald, Pedro Campuzano-Jost, Douglas A. Day, Weiwei Hu, Jason C. Schroder, James Allan, Donald R. Blake, Manjula R. Canagaratna, Hugh Coe, Matthew M. Coggon, Peter F. DeCarlo, Glenn S. Diskin, Rachel Dunmore, Frank Flocke, Alan Fried, Jessica B. Gilman, Georgios Gkatzelis, Jacqui F. Hamilton, Thomas F. Hanisco, Patrick L. Hayes, Daven K. Henze, Alma Hodzic, James Hopkins, Min Hu, L. Greggory Huey, B. Thomas Jobson, William C. Kuster, Alastair Lewis, Meng Li, Jin Liao, M. Omar Nawaz, Ilana B. Pollack, Jeffrey Peischl, Bernhard Rappenglück, Claire E. Reeves, Dirk Richter, James M. Roberts, Thomas B. Ryerson, Min Shao, Jacob M. Sommers, James Walega, Carsten Warneke, Petter Weibring, Glenn M. Wolfe, Dominique E. Young, Bin Yuan, Qiang Zhang, Joost A. de Gouw, and Jose L. Jimenez
Atmos. Chem. Phys., 21, 11201–11224, https://doi.org/10.5194/acp-21-11201-2021, https://doi.org/10.5194/acp-21-11201-2021, 2021
Short summary
Short summary
Secondary organic aerosol (SOA) is an important aspect of poor air quality for urban regions around the world, where a large fraction of the population lives. However, there is still large uncertainty in predicting SOA in urban regions. Here, we used data from 11 urban campaigns and show that the variability in SOA production in these regions is predictable and is explained by key emissions. These results are used to estimate the premature mortality associated with SOA in urban regions.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Zenodo [code], https://doi.org/10.5281/zenodo.4724125, 2015.
Akoglu, H.: User's guide to correlation coefficients, Turk. J. Emerg. Med., 18, 91–93, https://doi.org/10.1016/j.tjem.2018.08.001, 2018.
Allen, D.: Attributing Atmospheric Methane to Anthropogenic Emission Sources, Acc. Chem. Res., 49, 1344–1350, https://doi.org/10.1021/acs.accounts.6b00081, 2016.
Allen, D. T.: Methane emissions from natural gas production and use: reconciling bottom-up and top-down measurements, Curr. Opin. Chem. Eng., 5, 78–83, https://doi.org/10.1016/j.coche.2014.05.004, 2014.
Annual Energy Outlook 2023: https://www.eia.gov/outlooks/aeo/index.php, last access: 16 January 2025.
Apituley, A., Pedergnana, M., Sneep, M., Veefkind, P., Loyola, D., Hasekamp, O., Lorente, A., and Borsdorff, T.: Sentinel-5 precursor/TROPOMI Level 2 Product User Manual Methane, SRON-S5P-LEV2-MA-001, SRON, https://sentinels.copernicus.eu/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Methane.pdf (last access: 14 September 2024), 2022.
Balasus, N., Jacob, D. J., Lorente, A., Maasakkers, J. D., Parker, R. J., Boesch, H., Chen, Z., Kelp, M. M., Nesser, H., and Varon, D. J.: A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases, Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, 2023.
Beirle, S., Borger, C., Dörner, S., Eskes, H., Kumar, V., de Laat, A., and Wagner, T.: Catalog of NOx emissions from point sources as derived from the divergence of the NO2 flux for TROPOMI, Earth Syst. Sci. Data, 13, 2995–3012, https://doi.org/10.5194/essd-13-2995-2021, 2021.
Boucher, O., Friedlingstein, P., Collins, B., and Shine, K. P.: The indirect global warming potential and global temperature change potential due to methane oxidation, Environ. Res. Lett., 4, 044007, https://doi.org/10.1088/1748-9326/4/4/044007, 2009.
Bradley, A. C.: Tropomi_seasonal_albedo_correction v1.0, Version v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.12809441, 2024.
Chen, D., Huang, J., and Jackson, T. J.: Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands, Remote Sens. Environ., 98, 225–236, https://doi.org/10.1016/j.rse.2005.07.008, 2005.
Chollet, F., et al.: Keras, https://keras.io (last access: 28 March 2025), 2015.
Collins, W. J., Webber, C. P., Cox, P. M., Huntingford, C., Lowe, J., Sitch, S., Chadburn, S. E., Comyn-Platt, E., Harper, A. B., Hayman, G., and Powell, T.: Increased importance of methane reduction for a 1.5° target, Environ. Res. Lett., 13, 054003, https://doi.org/10.1088/1748-9326/aab89c, 2018.
Cook, T., Perrin, J., and Van Wagener, D.: Hydraulically fractured horizontal wells account for most of new oil and gas well, Hydraulically Fractured Horizontal Wells Account for Most of New Oil and Gas Well, https://www.eia.gov/todayinenergy/detail.php?id=34732 (last access: 8 June 2024), 2018.
Crameri, F., Shephard, G. E., and Heron, P. J.: The misuse of colour in science communication, Nat. Commun., 11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020.
da Costa-Luis, C. O.: tqdm: A Fast, Extensible Progress Meter for Python and CLI, Journal of Open Source Software, 4, 1277, https://doi.org/10.21105/joss.01277, 2019.
de Gouw, J. A., Veefkind, J. P., Roosenbrand, E., Dix, B., Lin, J. C., Landgraf, J., and Levelt, P. F.: Daily Satellite Observations of Methane from Oil and Gas Production Regions in the United States, Scientific Reports, 10, 1379, https://doi.org/10.1038/s41598-020-57678-4, 2020.
ESA: TROPOMI Methane Product, ESA [data set], https://browser.dataspace.copernicus.eu/ (last access: 12 August 2024), 2024.
Etiope, G. and Schwietzke, S.: Global geological methane emissions: An update of top-down and bottom-up estimates, Elementa: Science of the Anthropocene, 7, 47, https://doi.org/10.1525/elementa.383, 2019.
Gillies, S., van der Wel, C., Van den Bossche, J., et al.: Shapely, Version 2.1.0rc1, Zenodo [code], https://doi.org/10.5281/zenodo.15032054, 2025.
Gillies, S., et al.: Rasterio: Geospatial Raster I/O for Python Programmers, GitHub [code], https://github.com/rasterio/rasterio (last access: 28 March 2025), 2013.
Han, W., Yang, Z., Di, L., and Yue, P.: A geospatial Web service approach for creating on-demand Cropland Data Layer thematic maps, T. ASABE, 57, 239–247, https://doi.org/10.13031/trans.57.10020, 2014.
Harris, C. R., Millman, K. J., Walt, S. J. van der, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., Kerkwijk, M. H. van, Brett, M., Haldane, A., Río, J. F. del, Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020.
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, 2007.
IEA: Global Methane Tracker, IEA, Paris, https://www.iea.org/reports/global-methane-tracker-2023 (last access: 28 March 2025), 2023.
Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X., Chance, K., Aben, I., McKeever, J., and Frankenberg, C.: Satellite observations of atmospheric methane and their value for quantifying methane emissions, Atmos. Chem. Phys., 16, 14371–14396, https://doi.org/10.5194/acp-16-14371-2016, 2016.
Jordahl, K., Bossche, J. V. D., Fleischmann, M., Wasserman, J., McBride, J., Gerard, J., Tratner, J., Perry, M., Badaracco, A. G., Farmer, C., Hjelle, G. A., Snow, A. D., Cochran, M., Gillies, S., Culbertson, L., Bartos, M., Eubank, N., Maxalbert, Bilogur, A., Rey, S., Ren, C., Arribas-Bel, D., Wasser, L., Wolf, L. J., Journois, M., Wilson, J., Greenhall, A., Holdgraf, C., Filipe, and Leblanc, F.: Geopandas/Geopandas: v0.8.1, Version v0.8.1, Zenodo [code], https://doi.org/10.5281/ZENODO.3946761, 2020.
Kuckartz, U., Rädiker, S., Ebert, T., and Schehl, J.: Statistik: Eine verständliche Einführung, 2., überarb. Aufl., 2013th edn., VS Verlag für Sozialwissenschaften, Wiesbaden, 316 pp., ISBN: 978-3-531-19889-7, 2013.
Landgraf, J., Lorente, A., Langerock, B., and Kumar Sha, M.: S5P Mission Performance Centre Methane [L2__CH4___] Readme; S5P MPC Product Readme Methane, SRON, p. 4, https://doi.org/10.5270/S5P-3lcdqiv, 2023.
Levelt, P. F., Stein Zweers, D. C., Aben, I., Bauwens, M., Borsdorff, T., De Smedt, I., Eskes, H. J., Lerot, C., Loyola, D. G., Romahn, F., Stavrakou, T., Theys, N., Van Roozendael, M., Veefkind, J. P., and Verhoelst, T.: Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI, Atmos. Chem. Phys., 22, 10319–10351, https://doi.org/10.5194/acp-22-10319-2022, 2022.
Liu, M., van der A, R., van Weele, M., Eskes, H., Lu, X., Veefkind, P., de Laat, J., Kong, H., Wang, J., Sun, J., Ding, J., Zhao, Y., and Weng, H.: A New Divergence Method to Quantify Methane Emissions Using Observations of Sentinel-5P TROPOMI, Geophys. Res. Lett., 48, e2021GL094151, https://doi.org/10.1029/2021GL094151, 2021.
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., aan de Brugh, J., Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi, K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke, T., and Landgraf, J.: Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021.
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, Adv. Neur. In., 30, 4765–4775, 2017.
National Weather Service (NWS): Rivers of the U.S., NWS [data set], https://www.weather.gov/gis/Rivers, last access: 16 February 2024.
Omara, M., Zimmerman, N., Sullivan, M. R., Li, X., Ellis, A., Cesa, R., Subramanian, R., Presto, A. A., and Robinson, A. L.: Methane Emissions from Natural Gas Production Sites in the United States: Data Synthesis and National Estimate, Environ. Sci. Technol., 52, 12915–12925, https://doi.org/10.1021/acs.est.8b03535, 2018.
Parker, R. and Boesch, H.: University of Leicester GOSAT Proxy XCH4 v9.0, Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb, 2020.
Pétron, G., Karion, A., Sweeney, C., Miller, B. R., Montzka, S. A., Frost, G. J., Trainer, M., Tans, P., Andrews, A., Kofler, J., Helmig, D., Guenther, D., Dlugokencky, E., Lang, P., Newberger, T., Wolter, S., Hall, B., Novelli, P., Brewer, A., Conley, S., Hardesty, M., Banta, R., White, A., Noone, D., Wolfe, D., and Schnell, R.: A new look at methane and nonmethane hydrocarbon emissions from oil and natural gas operations in the Colorado Denver-Julesburg Basin, J. Geophys. Res.-Atmos., 119, 6836–6852, https://doi.org/10.1002/2013JD021272, 2014.
Riddick, S. N., Mbua, M., Santos, A., Hartzell, W., and Zimmerle, D. J.: Potential Underestimate in Reported Bottom-up Methane Emissions from Oil and Gas Operations in the Delaware Basin, Atmosphere, 15, 202, https://doi.org/10.3390/atmos15020202, 2024.
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence, 1, 206–215, https://doi.org/10.1038/s42256-019-0048-x, 2019.
Schepers, D., Guerlet, S., Butz, A., Landgraf, J., Frankenberg, C., Hasekamp, O., Blavier, J.-F., Deutscher, N. M., Griffith, D. W. T., Hase, F., Kyro, E., Morino, I., Sherlock, V., Sussmann, R., and Aben, I.: Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: Performance comparison of proxy and physics retrieval algorithms, J. Geophys. Res.-Atmos., 117, D10307, https://doi.org/10.1029/2012JD017549, 2012.
Schober, P., Boer, C., and Schwarte, L. A.: Correlation Coefficients: Appropriate Use and Interpretation, Anesth. Analg., 126, 1763, https://doi.org/10.1213/ANE.0000000000002864, 2018.
Takuya, A., Shotaro, S., Toshihiko, Y., Takeru, O., and Masanori, K.: Optuna: A Next-Generation Hyperparameter Optimization Framework, in: KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA, 4–8 August 2019, Association for Computing Machinery [code], 2623–2631, https://doi.org/10.1145/3292500.3330701, 2623–2631, 2023.
The pandas development team: pandas-dev/pandas: Pandas, Version v2.2.3, Zenodo [code], https://doi.org/10.5281/zenodo.13819579, 2024.
Twain, M.: Roughing It, American Pub. Co., Hartford, Conn., ISBN: 978-0451531100, 1891.
United States Department of Agriculture (USDA): County Profile: Weld County, 2017 Census of Agriculture, USDA, 1–2, https://www.nass.usda.gov/Publications/AgCensus/2017/Online_Resources/County_Profiles/Colorado/cp08123.pdf (last access: 28 March 2025), 2017.
U.S. Census Bureau: Cartographic Boundary Files - Shapefile, U.S. Census Bureau [data set], https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html, last access: 16 February 2024.
USDA NASS (National Agricultural Statistics Service): Cropland Data Layer (CDL), CropScape [data set], https://nassgeodata.gmu.edu/CropScape/, last access: 28 March 2025.
U.S. EIA: Primary Energy Production Estimates, State Energy Data System (SEDS), United States Energy Information Administration, United States, https://www.eia.gov/state/seds/archive/ (last access: 5 June 2024), 2020.
U.S. EIA: Drilling Productivity Report, https://www.eia.gov/petroleum/drilling/ (last access: 5 June 2024), 2023.
U.S. Energy Information Administration (EIA): https://www.eia.gov/outlooks/aeo/data/browser/#/?id=1-AEO2023®ion=0-0&cases=ref2023&start=2021&end=2050&f=A&linechart=ref2023-d020623a.6-1-AEO2023&ctype=linechart&sourcekey=0, last access: 4 January 2024.
Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.: TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications, Remote Sens. Environ., 120, 70–83, https://doi.org/10.1016/j.rse.2011.09.027, 2012.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
Whitaker, J.: Netcdf4-Python: Python/Numpy Interface to the netCDF C Library [code], http://unidata.github.io/netcdf4-python (last access: 28 March 2025), 2008.
Zavala-Araiza, D., Lyon, D. R., Alvarez, R. A., Davis, K. J., Harriss, R., Herndon, S. C., Karion, A., Kort, E. A., Lamb, B. K., Lan, X., Marchese, A. J., Pacala, S. W., Robinson, A. L., Shepson, P. B., Sweeney, C., Talbot, R., Townsend-Small, A., Yacovitch, T. I., Zimmerle, D. J., and Hamburg, S. P.: Reconciling divergent estimates of oil and gas methane emissions, P. Natl. Acad. Sci. USA, 112, 15597–15602, https://doi.org/10.1073/pnas.1522126112, 2015.
Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., Lyon, D., Nesser, H., Sulprizio, M. P., Varon, D. J., Zhang, R., Houweling, S., Zavala-Araiza, D., Alvarez, R. A., Lorente, A., Hamburg, S. P., Aben, I., and Jacob, D. J.: Quantifying methane emissions from the largest oil-producing basin in the United States from space, Science Advances, 6, eaaz5120, https://doi.org/10.1126/sciadv.aaz5120, 2020.
Short summary
Currently, measurement of methane from the TROPOMI satellite is biased with respect to surface reflectance. This study demonstrates a new method of correcting for this bias on a seasonal timescale to allow for differences in surface reflectance in areas of intense agriculture where growing seasons may introduce a reflectance bias. We have successfully implemented this technique in the Denver–Julesburg basin, where agriculture and methane extraction infrastructure is often co-located.
Currently, measurement of methane from the TROPOMI satellite is biased with respect to surface...
