Articles | Volume 15, issue 11
https://doi.org/10.5194/amt-15-3527-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-3527-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jun 2022
Research article |
| 14 Jun 2022
| 14 Jun 2022
Ozone Monitoring Instrument (OMI) collection 4: establishing a 17-year-long series of detrended level-1b data
Quintus Kleipool
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Nico Rozemeijer
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
TriOpSys B.V., Utrecht, the Netherlands
Mirna van Hoek
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Jonatan Leloux
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
TriOpSys B.V., Utrecht, the Netherlands
Erwin Loots
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Antje Ludewig
CORRESPONDING AUTHOR
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Emiel van der Plas
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Daley Adrichem
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
TriOpSys B.V., Utrecht, the Netherlands
Raoul Harel
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
TriOpSys B.V., Utrecht, the Netherlands
Simon Spronk
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
TriOpSys B.V., Utrecht, the Netherlands
Mark ter Linden
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Science and Technology (S[&]T), Delft, the Netherlands
Glen Jaross
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
David Haffner
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
Pepijn Veefkind
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Pieternel F. Levelt
Atmospheric Chemistry Observations & Modeling Laboratory (ACOM), National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
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Yutao Chen, Ronald J. van der A, Jieying Ding, Henk Eskes, Felipe Cifuentes, and Pieternel F. Levelt
Atmos. Meas. Tech., 19, 2737–2750, https://doi.org/10.5194/amt-19-2737-2026, https://doi.org/10.5194/amt-19-2737-2026, 2026
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The SO2 gridded point emissions calculated from satellite measurements appear spread out around the source. This is inherent to the inversion method and the resolution of the satellite measurements. We made a new sharpening algorithm to reverse the spreading and make the emission signal clearer. With this method, the effective resolution is improved. Known sources can be located more precisely, and new ones can be found from space. The same approach also works for other gases like NOx.
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
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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
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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
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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
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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
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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
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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.
Michael D. Himes, Natalya A. Kramarova, Krzysztof Wargan, Sean M. Davis, and Glen Jaross
EGUsphere, https://doi.org/10.5194/egusphere-2025-4845, https://doi.org/10.5194/egusphere-2025-4845, 2025
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Stratospheric water vapor (SWV) influences various atmospheric processes. While the Ozone Mapping and Profiler Suite Limb Profiler (OMPS LP) was not designed to measure SWV, we utilized near-coincident measurements by the Aura Microwave Limb Sounder (MLS) and OMPS LP to develop a machine learning method to measure SWV between 11.5–40.5 km. The LP-derived SWV closely agrees with MLS. Our results suggest OMPS LP can continue the global water vapor record after MLS measurements cease in 2026.
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
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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
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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.
Alexander C. Bradley, Barbara Dix, Fergus Mackenzie, J. Pepijn Veefkind, and Joost A. de Gouw
Atmos. Meas. Tech., 18, 1675–1687, https://doi.org/10.5194/amt-18-1675-2025, https://doi.org/10.5194/amt-18-1675-2025, 2025
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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.
Yutao Chen, Ronald J. van der A, Jieying Ding, Henk Eskes, Jason E. Williams, Nicolas Theys, Athanasios Tsikerdekis, and Pieternel F. Levelt
Atmos. Chem. Phys., 25, 1851–1868, https://doi.org/10.5194/acp-25-1851-2025, https://doi.org/10.5194/acp-25-1851-2025, 2025
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There is a lack of local SO2 top-down emission inventories in India. With the improvement in the divergence method and the derivation of SO2 local lifetime, gridded SO2 emissions over a large area can be estimated efficiently. This method can be applied to any region in the world to derive SO2 emissions. Especially for regions with high latitudes, our methodology has the potential to significantly improve the top-down derivation of SO2 emissions.
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
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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
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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
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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.
Heesung Chong, Gonzalo González Abad, Caroline R. Nowlan, Christopher Chan Miller, Alfonso Saiz-Lopez, Rafael P. Fernandez, Hyeong-Ahn Kwon, Zolal Ayazpour, Huiqun Wang, Amir H. Souri, Xiong Liu, Kelly Chance, Ewan O'Sullivan, Jhoon Kim, Ja-Ho Koo, William R. Simpson, François Hendrick, Richard Querel, Glen Jaross, Colin Seftor, and Raid M. Suleiman
Atmos. Meas. Tech., 17, 2873–2916, https://doi.org/10.5194/amt-17-2873-2024, https://doi.org/10.5194/amt-17-2873-2024, 2024
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We present a new bromine monoxide (BrO) product derived using radiances measured from OMPS-NM on board the Suomi-NPP satellite. This product provides nearly a decade of global stratospheric and tropospheric column retrievals, a feature that is currently rare in publicly accessible datasets. Both stratospheric and tropospheric columns from OMPS-NM demonstrate robust performance, exhibiting good agreement with ground-based observations collected at three stations (Lauder, Utqiagvik, and Harestua).
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
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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.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
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
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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
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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
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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
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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
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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
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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.
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
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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.
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
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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
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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
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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.
Lily N. Zhang, Susan Solomon, Kane A. Stone, Jonathan D. Shanklin, Joshua D. Eveson, Steve Colwell, John P. Burrows, Mark Weber, Pieternel F. Levelt, Natalya A. Kramarova, and David P. Haffner
Atmos. Chem. Phys., 21, 9829–9838, https://doi.org/10.5194/acp-21-9829-2021, https://doi.org/10.5194/acp-21-9829-2021, 2021
Short summary
Short summary
In the 1980s, measurements at the British Antarctic Survey station in Halley, Antarctica, led to the discovery of the ozone hole. The Halley total ozone record continues to be uniquely valuable for studies of long-term changes in Antarctic ozone. Environmental conditions in 2017 forced a temporary cessation of operations, leading to a gap in the historic record. We develop and test a method for filling in the Halley record using satellite data and find evidence to further support ozone recovery.
Cited articles
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S., Rozanov,
V. V., Chance, K. V., and Goede, A. P. H.: SCIAMACHY: Mission Objectives and
Measurement Modes, J. Atmos. Sci., 56, 127–150,
https://doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2,
1999. a
Cebula, R. P., Park, H., and Heath, D. F.: Characterization of the Nimbus-7
SBUV Radiometer for the Long-Term Monitoring of Stratospheric Ozone, J. Atmos. Ocean. Tech., 5, 215–227,
https://doi.org/10.1175/1520-0426(1988)005<0215:cotnsr>2.0.co;2, 1988. a
CFConventions: NetCDF Climate and Forecast (CF) Metadata Conventions, Tech.
Rep. issue 1.6, CF Conventions, http://www.cfconventions.org (last access: 8 June 2022), 2011. a
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation
data 2010 (GMTED2010), Report 2011-1073, p. 26, U.S. Geological Survey,
https://doi.org/10.3133/ofr20111073, 2011. a
Dobber, M., Kleipool, Q., Dirksen, R., Levelt, P., Jaross, G., Taylor, S.,
Kelly, T., Flynn, L., Leppelmeier, G., and Rozemeijer, N.: Validation of
Ozone Monitoring Instrument level 1b data products, J. Geophys.
Res.-Atmos., 113, D15, https://doi.org/10.1029/2007JD008665, 2008a. a, b, c, d
Dobber, M., Voors, R., Dirksen, R., Kleipool, Q., and Levelt, P.: The
High-Resolution Solar Reference Spectrum between 250 and 550 nm and its
Application to Measurements with the Ozone Monitoring Instrument,
Sol. Phys., 249, 281–291, https://doi.org/10.1007/s11207-008-9187-7,
2008b. a, b
Dobber, M. R., Dirksen, R. J., Levelt, P. F., van den Oord, G. H. J., Voors, R.
H. M., Kleipool, Q., Jaross, G., Kowalewski, M., Hilsenrath, E., Leppelmeier,
G. W., de Vries, J., Dierssen, W., and Rozemeijer, N. C.: Ozone monitoring
instrument calibration, IEEE T. Geosci. Remote, 44, 1209–1238,
https://doi.org/10.1109/TGRS.2006.869987, 2006. a, b, c, d, e
ESIP: Attribute Conventions Dataset Discovery,
http://wiki.esipfed.org/index.php/Category:Attribute_Conventions_Dataset_Discovery (last access: 8 June 2022),
2022. a
HDFgroup: Hierarchical Data Format, https://www.hdfgroup.org/ (last access: 8 June 2022),
2021. a
Herman, J., Hudson, R., McPeters, R., Stolarski, R., Ahmad, Z., Gu, X.-Y.,
Taylor, S., and Wellemeyer, C.: A new self-calibration method applied to
TOMS/SBUV backscattered ultraviolet data to determine long-term global ozone
change, J. Geophys. Res., 96, 7531–7545, https://doi.org/10.1029/90JD02662, 1991. a
ISO: Geographic Information – Metadata, ISO 19115:2003(E) First Edition,
International Organization for Standardization (ISO), 2003. a
ISO: Geographic Information – Observations and Measurements, Iso
19156:2011(e), International Organization for Standardization (ISO), 2011. a
Jaross, G. and Krueger, A. J.: Ice radiance method for backscatter UV
instrument monitoring, in: Atmospheric Ozone, edited by: Henriksen, T.,
2047, 94–101, International Society for Optics and Photonics, SPIE,
https://doi.org/10.1117/12.163469, 1993. a
JRC: INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO
19115 and EN ISO 19119, MD-IR-and-ISO-v1-2-20100616 issue 1.2, European
Commission Joint Research Centre (EC JRC), https://inspire.ec.europa.eu/documents/Metadata/INSPIRE_MD_IR_and_ISO_v1_2_20100616.pdf (last access: 8 June 2022), 2010. a
Kroon, M., Veefkind, J. P., Sneep, M., McPeters, R. D., Bhartia, P. K., and
Levelt, P. F.: Comparing OMI-TOMS and OMI-DOAS total ozone column data,
J. Geophys. Res.-Atmos., 113, D16,
https://doi.org/10.1029/2007JD008798, 2008. a
Levelt, P. F., van den Oord, G. H., Dobber, M. R., Malkki, A., Visser, H.,
de Vries, J., Stammes, P., Lundell, J. O., and Saari, H.: The ozone
monitoring instrument, IEEE T. Geosci. Remote, 44, 1093–1101,
https://doi.org/10.1109/TGRS.2006.872333, 2006. a
Ludewig, A., Adrichem, D., Haffner, D., Harel, R., van Hoek, M., Jaross, G.,
Kleipool, Q., Leloux, J., Loots, E., van der Plas, E., and Rozemeijer, N.:
Algorithm Theoretical Basis Document for the collection 4 L01b data
processing of the Ozone Monitoring Instrument, AURA-OMI-KNMI-L01B-0002-SD
24170, Royal Netherlands Meteorological Institute (KNMI),
https://docserver.gesdisc.eosdis.nasa.gov/public/project/OMI/AURA-OMI-KNMI-L01B-0002-SD-atbd-v024170-20210716.pdf (last access: 8 June 2022),
2021. a, b, c, d, e, f, g
Marchenko, S., DeLand, M., and Lean, J.: Solar spectral irradiance variability
in cycle 24: observations and models, J. Space Weather Spac., 6, A40,
https://doi.org/10.1051/swsc/2016036, 2016. a
McPeters, R. D., Bhartia, P. K., Krueger, A. J., Herman, J. R., Wellemeyer,
C. G., Seftor, C. J., Jaross, G., Torres, O., Moy, L., Labow, G., Byerly, W.,
Taylor, S. L., Swissler, T., and Cebula, R. P.: Earth Probe Total Ozone
Mapping Spectrometer (TOMS) Data Product’s User’s Guide, NASA Technical
Publication 1998-206895, National Aeronautics and Space Administration
(NASA), https://ozoneaq.gsfc.nasa.gov/docs/epusrguide.pdf (last access: 8 June 2022), 1998. a
NASA: Solar eclipse website, https://eclipse.gsfc.nasa.gov/ (last access: 8 June 2022),
2021. a
OGC: Earth Observation Metadata profile of Observations Measurements, OGC
10-157r4 issue 1.0.3-DRAFT, Open Geospatial Consortium (OGC), http://www.opengis.net/doc/IS/eompom/1.1,
(last access 8 June 2022)
2014. a
OML1BIRR: OMI/Aura Level 1B Averaged Solar Irradiances V004,
Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/AURA/OMI/DATA1401, 2021. a, b
OML1BRUG: OMI/Aura Level 1B UV Global Geolocated Earthshine Radiances V004,
Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/AURA/OMI/DATA1402, 2021. a, b
OML1BRUZ: OMI/Aura Level 1B UV Zoom-in Geolocated Earthshine Radiances V004,
Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/AURA/OMI/DATA1403, 2021. a, b
OML1BRVG: OMI/Aura Level 1B VIS Global Geolocated Earthshine Radiances V004,
Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/AURA/OMI/DATA1404, 2021. a, b
OML1BRVZ: OMI/Aura Level 1B VIS Zoom-in Geolocated Earthshine Radiances V004,
Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/AURA/OMI/DATA1405, 2021. a, b
Rozemeijer, N.: Metadata specification for the collection 4 L01b data
processing of the Ozone Monitoring Instrument, AURA-OMI-KNMI-L01B-0007-SD
24148, Royal Netherlands Meteorological Institute (KNMI),
https://docserver.gesdisc.eosdis.nasa.gov/public/project/OMI/AURA-OMI-KNMI-L01B-0007-SD-metadata_specs-v024148-20210702.pdf (last access: 10 June 2022),
2021. a
Rozemeijer, N., Adrichem, D., Haffner, D., Harel, R., van Hoek, M., Jaross, G.,
Kleipool, Q., Leloux, J., Loots, E., Ludewig, A., and van der Plas, E.: Input
output data specification for the collection 4 L01b data processing of the
Ozone Monitoring Instrument, AURA-OMI-KNMI-L01B-0005-SD 24148, Royal
Netherlands Meteorological Institute (KNMI),
https://docserver.gesdisc.eosdis.nasa.gov/public/project/OMI/AURA-OMI-KNMI-L01B-0005-SD-iods-v024148-20210702.pdf (last access: 10 June 2022),
2021. a, b
Schenkeveld, V. M. E., Jaross, G., Marchenko, S., Haffner, D., Kleipool, Q. L., Rozemeijer, N. C., Veefkind, J. P., and Levelt, P. F.: In-flight performance of the Ozone Monitoring Instrument, Atmos. Meas. Tech., 10, 1957–1986, https://doi.org/10.5194/amt-10-1957-2017, 2017. a, b, c
Schoeberl, M., Douglass, A., Hilsenrath, E., Bhartia, P., Beer, R., Waters, J.,
Gunson, M., Froidevaux, L., Gille, J., Barnett, J., Levelt, P., and DeCola,
P.: Overview of the EOS Aura mission,
IEEE
T. Geosci. Remote, 44, 1066–1074, https://doi.org/10.1109/TGRS.2005.861950, 2006. a
Stammes, P., Levelt, P. F., de Vries, J., Visser, H., Kruizinga, B.,
Smorenburg, K., Leppelmeier, G. W., and Hilsenrath, E.: Scientific
requirements and optical design of the ozone monitoring instrument on
EOS-CHEM, in: Earth Observing Systems IV, edited by: Barnes, W. L.,
3750, 221–232, International Society for Optics and Photonics, SPIE,
https://doi.org/10.1117/12.363517, 1999. a
Unidata: Network Common Data Format,
http://www.unidata.ucar.edu/software/netcdf/docs/ (last access: 10 June 2022), 2021. a
van den Oord, G., Rozemeijer, N., Schenkelaars, V., Levelt, P., Dobber, M.,
Voors, R., Claas, J., de Vries, J., ter Linden, M., De Haan, C., and van de
Berg, T.: OMI level 0 to 1b processing and operational aspects, IEEE
T. Geosci. Remote, 44, 1380–1397,
https://doi.org/10.1109/TGRS.2006.872935, 2006. a
Veefkind, J., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G.,
Claas, J., Eskes, H., de Haan, J., 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.: 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. a
Wellemeyer, C., Taylor, S., Jaross, G., DeLand, M., Seftor, C., Labow, G.,
Swissler, T., and Cebula, R.: Final report on Nimbus-7 TOMS version 7
calibration, Nasa contractor rep. 4717, NASA, https://ntrs.nasa.gov/api/citations/19960021250/downloads/19960021250.pdf
(last access: 10 June 2022), 1996. a
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.
A new collection-4 dataset for the Ozone Monitoring Instrument (OMI) mission has been...
