Articles | Volume 17, issue 17
https://doi.org/10.5194/amt-17-5279-2024
© Author(s) 2024. 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-17-5279-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment of the contribution of the Meteosat Third Generation Infrared Sounder (MTG-IRS) for the characterisation of ozone over Europe
Francesca Vittorioso
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Vincent Guidard
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Nadia Fourrié
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Related authors
No articles found.
Augustin Colette, Gaëlle Collin, François Besson, Etienne Blot, Vincent Guidard, Frédérik Meleux, Adrien Royer, Valentin Petiot, Claire Miller, Oihana Fermond, Alizé Jeant, Mario Adani, Joaquim Arteta, Anna Benedictow, Robert Bergström, Dene Bowdalo, Jorgen Brandt, Gino Briganti, Ana C. Carvalho, Jesper Heile Christensen, Florian Couvidat, Ilaria D'Elia, Massimo D'Isidoro, Hugo Denier van der Gon, Gaël Descombes, Enza Di Tomaso, John Douros, Jeronimo Escribano, Henk Eskes, Hilde Fagerli, Yalda Fatahi, Johannes Flemming, Elmar Friese, Lise Frohn, Michael Gauss, Camilla Geels, Guido Guarnieri, Marc Guevara, Antoine Guion, Jonathan Guth, Risto Hänninen, Kaj Hansen, Ulas Im, Ruud Janssen, Marine Jeoffrion, Mathieu Joly, Luke Jones, Oriol Jorba, Evgeni Kadantsev, Michael Kahnert, Jacek W. Kaminski, Rostislav Kouznetsov, Richard Kranenburg, Jeroen Kuenen, Anne Caroline Lange, Joachim Langner, Victor Lannuque, Francesca Macchia, Astrid Manders, Mihaela Mircea, Agnes Nyiri, Miriam Olid, Carlos Pérez García-Pando, Yuliia Palamarchuk, Antonio Piersanti, Blandine Raux, Miha Razinger, Lennard Robertson, Arjo Segers, Martijn Schaap, Pilvi Siljamo, David Simpson, Mikhail Sofiev, Anders Stangel, Joanna Struzewska, Carles Tena, Renske Timmermans, Thanos Tsikerdekis, Svetlana Tsyro, Svyatoslav Tyuryakov, Anthony Ung, Andreas Uppstu, Alvaro Valdebenito, Peter van Velthoven, Lina Vitali, Zhuyun Ye, Vincent-Henri Peuch, and Laurence Rouïl
Geosci. Model Dev., 18, 6835–6883, https://doi.org/10.5194/gmd-18-6835-2025, https://doi.org/10.5194/gmd-18-6835-2025, 2025
Short summary
Short summary
The Copernicus Atmosphere Monitoring Service – Regional Production delivers daily forecasts, analyses, and reanalyses of air quality in Europe. The service relies on a distributed modelling production by 11 leading European modelling teams following stringent requirements with an operational design that has no equivalent in the world. All the products are free, open, and quality-assured and disseminated with a high level of reliability.
Mickaël Bacles, Jonathan Améric, and Vincent Guidard
Atmos. Meas. Tech., 18, 2659–2680, https://doi.org/10.5194/amt-18-2659-2025, https://doi.org/10.5194/amt-18-2659-2025, 2025
Short summary
Short summary
Sulfur dioxide emitted during volcanic eruptions can be hazardous for aviation safety. A recent development aims at improving the forecasts of volcanic sulfur dioxide quantities made by the chemistry transport model developed at Météo-France by assimilated infrared and ultraviolet satellite instruments. We focus on the eruption event of the La Soufrière Saint Vincent volcano in April 2021. The combined assimilation of these observations always leads to better analyses and forecasts.
Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié
Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024, https://doi.org/10.5194/amt-17-3567-2024, 2024
Short summary
Short summary
In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
Short summary
Short summary
In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard
Nonlin. Processes Geophys., 30, 139–166, https://doi.org/10.5194/npg-30-139-2023, https://doi.org/10.5194/npg-30-139-2023, 2023
Short summary
Short summary
This work is a theoretical contribution that provides equations for understanding uncertainty prediction applied in air quality where multiple chemical species can interact. A simplified minimal test bed is introduced that shows the ability of our equations to reproduce the statistics estimated from an ensemble of forecasts. While the latter estimation is the state of the art, solving equations is numerically less costly, depending on the number of chemical species, and motivates this research.
Samira Khodayar, Silvio Davolio, Paolo Di Girolamo, Cindy Lebeaupin Brossier, Emmanouil Flaounas, Nadia Fourrie, Keun-Ok Lee, Didier Ricard, Benoit Vie, Francois Bouttier, Alberto Caldas-Alvarez, and Veronique Ducrocq
Atmos. Chem. Phys., 21, 17051–17078, https://doi.org/10.5194/acp-21-17051-2021, https://doi.org/10.5194/acp-21-17051-2021, 2021
Short summary
Short summary
Heavy precipitation (HP) constitutes a major meteorological threat in the western Mediterranean. Every year, recurrent events affect the area with fatal consequences. Despite this being a well-known issue, open questions still remain. The understanding of the underlying mechanisms and the modeling representation of the events must be improved. In this article we present the most recent lessons learned from the Hydrological Cycle in the Mediterranean Experiment (HyMeX).
Mohammad El Aabaribaoune, Emanuele Emili, and Vincent Guidard
Atmos. Meas. Tech., 14, 2841–2856, https://doi.org/10.5194/amt-14-2841-2021, https://doi.org/10.5194/amt-14-2841-2021, 2021
Short summary
Short summary
This work aims to use correlated IASI errors in the ozone band within a chemical transport model assimilation. The validation of the results against ozone observations from ozonesondes, MLS, and OMI instruments has shown an improvement of the ozone distribution. The computational time was also highly reduced. The surface sea temperature was also improved. The work aims to improve the quality of the ozone prediction, which is important for air quality, climate, and meteorological applications.
Nadia Fourrié, Mathieu Nuret, Pierre Brousseau, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 21, 463–480, https://doi.org/10.5194/nhess-21-463-2021, https://doi.org/10.5194/nhess-21-463-2021, 2021
Short summary
Short summary
The assimilation impact of four observation data sets on forecasts is studied in a mesoscale weather model. The ground-based Global Navigation Satellite System (GNSS) zenithal total delay data set with information on humidity has the largest impact on analyses and forecasts, representing an evenly spread and frequent data set for each analysis time over the model domain. Moreover, the reprocessing of these data also improves the forecast quality, but this impact is not statistically significant.
Cited articles
Abdon, S., Gardette, H., Degrelle, C., Gaucel, J.-M., Astruc, P., Guiard, P., Accettura, A., Lamarre, D., Aminou, D. M., and Miras, D.: Meteosat third generation infrared sounder (MTG-IRS), interferometer and spectrometer test outcomes, demonstration of the new 3D metrology system efficiency, in: International Conference on Space Optics–ICSO 2020, Vol. 11852, 583–594, SPIE, https://doi.org/10.1117/12.2599240, 2021. a
Arnold Jr., C. P. and Dey, C. H.: Observing-systems simulation experiments: Past, present, and future, B. Am. Meteorol. Soc., 67, 687–695, 1986. a
Barré, J., Peuch, V.-H., Lahoz, W., Attié, J.-L., Josse, B., Piacentini, A., Eremenko, M., Dufour, G., Nedelec, P., von Clarmann, T., and El Amraoui, L.: Combined data assimilation of ozone tropospheric columns and stratospheric profiles in a high-resolution CTM, Q. J. Roy. Meteorol. Soc., 140, 966–981, 2014. a
Barret, B., Emili, E., and Le Flochmoen, E.: A tropopause-related climatological a priori profile for IASI-SOFRID ozone retrievals: improvements and validation, Atmos. Meas. Tech., 13, 5237–5257, https://doi.org/10.5194/amt-13-5237-2020, 2020. 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, Infrared Spaceborne Remote Sensing XII, 5543, 196–207, https://doi.org/10.1117/12.560907, 2004. a
Boukabara, S.-A., Moradi, I., Atlas, R., Casey, S. P., Cucurull, L., Hoffman, R. N., Ide, K., Krishna Kumar, V., Li, R., Li, Z., Masutani, M., Shahroudi, N., Woollen, J., and Zhou, Y.: Community global observing system simulation experiment (OSSE) package (CGOP): description and usage, J. Atmos. Ocean. Technol., 33, 1759–1777, 2016. a
Claeyman, M., Attié, J.-L., Peuch, V.-H., El Amraoui, L., Lahoz, W. A., Josse, B., Joly, M., Barré, J., Ricaud, P., Massart, S., Piacentini, A., von Clarmann, T., Höpfner, M., Orphal, J., Flaud, J.-M., and Edwards, D. P.: A thermal infrared instrument onboard a geostationary platform for CO and O3 measurements in the lowermost troposphere: Observing System Simulation Experiments (OSSE), Atmos. Meas. Tech., 4, 1637–1661, https://doi.org/10.5194/amt-4-1637-2011, 2011. a
Clarisse, L., Van Damme, M., Hurtmans, D., Franco, B., Clerbaux, C., and Coheur, P.-F.: The Diel Cycle of NH3 Observed From the FY-4A Geostationary Interferometric Infrared Sounder (GIIRS), Geophys. Res. Lett., 48, e2021GL093010, https://doi.org/10.1029/2021GL093010, 2021. 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
Coopmann, O., Fourrié, N., and Guidard, V.: Analysis of MTG-IRS observations and general channel selection for numerical weather prediction models, Q. Roy. Meteorol. Soc., 148, 1864–1885, https://doi.org/10.1002/qj.4282, 2022. a, b, c
Coopmann, O., Fourrié, N., Chambon, P., Vidot, J., Brousseau, P., Martet, M., and Birman, C.: Preparing the assimilation of the future MTG-IRS sounder into the mesoscale NWP AROME model [Manuscript submitted for publication], Q. J. Roy. Meteorol. Soc., 149, 3110–3134, https://doi.org/10.1002/qj.4548, 2023. a
Bouyssel, F. , Berre, L., Bénichou, H., Chambon, P., Girardot, N., Guidard, V., Loo, C., Mahfouf, J.-F., Moll, P., Payan, C. and Raspaud, D. The 2020 Global Operational NWP Data Assimilation System at Météo-France, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), edited by: Park, S. K. and Xu, L., Springer, Cham, https://doi.org/10.1007/978-3-030-77722-7_25, 2022. a
Descheemaecker, M., Plu, M., Marécal, V., Claeyman, M., Olivier, F., Aoun, Y., Blanc, P., Wald, L., Guth, J., Sič, B., Vidot, J., Piacentini, A., and Josse, B.: Monitoring aerosols over Europe: an assessment of the potential benefit of assimilating the VIS04 measurements from the future MTG/FCI geostationary imager, Atmos. Meas. Tech., 12, 1251–1275, https://doi.org/10.5194/amt-12-1251-2019, 2019. a, b, c
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Appl. Meteorol. Phys. Oceanogr., 131, 3385–3396, 2005. a
Diehl, T., Heil, A., Chin, M., Pan, X., Streets, D., Schultz, M., and Kinne, S.: Anthropogenic, biomass burning, and volcanic emissions of black carbon, organic carbon, and SO2 from 1980 to 2010 for hindcast model experiments, Atmos. Chem. Phys. Discuss., 12, 24895–24954, https://doi.org/10.5194/acpd-12-24895-2012, 2012. a, b
Duruisseau, F., Chambon, P., Guedj, S., Guidard, V., Fourrié, N., Taillefer, F., Brousseau, P., Mahfouf, J.-F., and Roca, R.: Investigating the potential benefit to a mesoscale NWP model of a microwave sounder on board a geostationary satellite, Q. J. Roy. Meteorol. Soc., 143, 2104–2115, 2017. a
Eckermann, S.: Hybrid σ–p coordinate choices for a global model, Mon. Weather Rev., 137, 224–245, 2009. a
El Aabaribaoune, M.: Assimilation des luminances IASI dans un modèle de chimie transport pour la surveillance de l'ozone et des poussières désertiques, Ph.D. thesis, Université Toulouse III-Paul Sabatier, https://theses.fr/2022TOU30271 (last access: 3 September 2024), 2022. a
El Aabaribaoune, M., Emili, E., and Guidard, V.: Estimation of the error covariance matrix for IASI radiances and its impact on the assimilation of ozone in a chemistry transport model, Atmos. Meas. Tech., 14, 2841–2856, https://doi.org/10.5194/amt-14-2841-2021, 2021. a, b, c, d
El Amraoui, L., Attié, J.-L., Semane, N., Claeyman, M., Peuch, V.-H., Warner, J., Ricaud, P., Cammas, J.-P., Piacentini, A., Josse, B., Cariolle, D., Massart, S., and Bencherif, H.: Midlatitude stratosphere – troposphere exchange as diagnosed by MLS O3 and MOPITT CO assimilated fields, Atmos. Chem. Phys., 10, 2175–2194, https://doi.org/10.5194/acp-10-2175-2010, 2010. a, b
Emili, E., Barret, B., Massart, S., Le Flochmoen, E., Piacentini, A., El Amraoui, L., Pannekoucke, O., and Cariolle, D.: Combined assimilation of IASI and MLS observations to constrain tropospheric and stratospheric ozone in a global chemical transport model, Atmos. Chem. Phys., 14, 177–198, https://doi.org/10.5194/acp-14-177-2014, 2014. a, b
Emili, E., Barret, B., Le Flochmoën, E., and Cariolle, D.: Comparison between the assimilation of IASI Level 2 ozone retrievals and Level 1 radiances in a chemical transport model, Atmos. Meas. Tech., 12, 3963–3984, https://doi.org/10.5194/amt-12-3963-2019, 2019. a, b, c
Errico, R. M., Yang, R., Masutani, M., and Woollen, J.: The use of an OSSE to estimate characteristics of analysis error, Meteorologische Z., 16, 695–708, https://doi.org/10.1127/0941-2948/2007/0242, 2007. a
Fujino, J., Nair, R., Kainuma, M., Masui, T., and Matsuoka, Y.: Multi-gas mitigation analysis on stabilization scenarios using AIM global model, The Energy J., 27, https://doi.org/10.5547/ISSN0195-6574-EJ-VolSI2006-NoS, 2006. a, b
Gambacorta, A. and Barnet, C. D.: Methodology and information content of the NOAA NESDIS operational channel selection for the Cross-Track Infrared Sounder (CrIS), IEEE T. Geosci. Remote Sens., 51, 3207–3216, 2012. a
Granier, C., Bessagnet, B., Bond, T., D’Angiola, A., Denier van der Gon, H., Frost, G. J., Heil, A., Kaiser, J. W., Kinne, S., Klimont, Z., Kloster, S., Lamarque, J.-F., Liousse, C., Masui, T., Meleux, F., Mieville, A., Ohara, T., Raut, J.-C., Riahi, K., Schultz, M. G., Smith, S. J., Thompson, A., van Aardenne, J., van der Werf, G. R., and van Vuuren, D. P. : Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period, Clim. Change, 109, 163–190, 2011. a, b
Granier, C., Darras, S., van der Gon, H. D., Jana, D., Elguindi, N., Bo, G., Michael, G., Marc, G., Jalkanen, J.-P., Kuenen, J., Liousse, C., Quack, B., Simpson, D., and Sindelarova, K.: The Copernicus atmosphere monitoring service global and regional emissions (April 2019 version), Ph.D. thesis, Copernicus Atmosphere Monitoring Service, https://doi.org/10.24380/d0bn-kx16, 2019. a, b, c
Guevara, M., Petetin, H., Jorba, O., Denier van der Gon, H., Kuenen, J., Super, I., Jalkanen, J.-P., Majamäki, E., Johansson, L., Peuch, V.-H., and Pérez García-Pando, C.: European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions, Earth Syst. Sci. Data, 14, 2521–2552, https://doi.org/10.5194/essd-14-2521-2022, 2022. a, b
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS OSSE Nature run – June 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12634489, 2024a. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS OSSE Nature run – July 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12635956, 2024b. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS OSSE Nature run – August 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12643523, 2024c. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS OSSE Control run – June 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12570356, 2024d. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS OSSE Control run – July 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12570536, 2024e. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS OSSE Control run – August 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12570745, 2024f. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS assimilation run – June 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12547819, 2024g. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS assimilation run – July 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12565862, 2024h. a
Guidard, V., Vittorioso, F., and Fourrié, N.: Ozone 3D field from IRS assimilation run – August 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12567703. 2024i. a
Guth, J., Josse, B., Marécal, V., Joly, M., and Hamer, P.: First implementation of secondary inorganic aerosols in the MOCAGE version R2.15.0 chemistry transport model, Geosci. Model Dev., 9, 137–160, https://doi.org/10.5194/gmd-9-137-2016, 2016. a
Hilton, F., Armante, R., August, T., Barnet, C., Bouchard, A., Camy-Peyret, C., Capelle, V., Clarisse, L., Clerbaux, C., Coheur, P.-F., Collard, A., Crevoisier, C., Dufour, G., Edwards, D., Faijan, F., Fourrié, N., Gambacorta, A., Goldberg, M., Guidard, V., Hurtmans, D., Illingworth, S., Jacquinet-Husson, N., Kerzenmacher, T., Klaes, D., Lavanant, L., Masiello, G., Matricardi, M., McNally, A., Newman, S., Pavelin, E., Payan, S., Péquignot, E., Peyridieu, S., Phulpin, T., Remedios, J., Schlüssel, P., Serio, C., Strow, L., Stubenrauch, C., Taylor, J., Tobin, D., Wolf, W., and Zhou, D.: Hyperspectral Earth observation from IASI: Five years of accomplishments, B. Am. Meteorol. Soc., 93, 347–370, 2012. a
Holmlund, K., Grandell, J., Schmetz, J., Stuhlmann, R., Bojkov, B., Munro, R., Lekouara, M., Coppens, D., Viticchie, B., August, T., Theodore, B., Watts, P., Dobber, M., Fowler, G., Bojinski, S., Schmid, A., Salonen, K., Tjemkes, S., Aminou, D., and Blythe, P.: Meteosat Third Generation (MTG): Continuation and innovation of observations from geostationary orbit, B. Am. Meteorol. Soc., 102, E990–E1015, https://doi.org/10.1175/BAMS-D-19-0304.1, 1–71, 2021. a
Huijnen, V., Pozzer, A., Arteta, J., Brasseur, G., Bouarar, I., Chabrillat, S., Christophe, Y., Doumbia, T., Flemming, J., Guth, J., Josse, B., Karydis, V. A., Marécal, V., and Pelletier, S.: Quantifying uncertainties due to chemistry modelling – evaluation of tropospheric composition simulations in the CAMS model (cycle 43R1), Geosci. Model Dev., 12, 1725–1752, https://doi.org/10.5194/gmd-12-1725-2019, 2019. a
Josse, B., Simon, P., and Peuch, V.-H.: Radon global simulations with the multiscale chemistry and transport model MOCAGE, Tellus B, 56, 339–356, 2004. a
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012. a, b
Kopacz, M., Breeze, V., Kondragunta, S., Frost, G., Anenberg, S., Bruhwiler, L., Davis, S., da Silva, A., de Gouw, J., Duren, R., Flynn, L., Gaudel, A., Geigert, M., Goldman, G., Joiner, J., McDonald, B., Ott, L., Peuch, V.-H., Pusede, S. E., Stajner, I., Seftor, C., Sweeney, C., Valin, L. C., Wang, J., Whetstone, J., and Kalluri, S.: Global Atmospheric Composition Needs from Future Ultraviolet–Visible–Near-Infrared (UV–Vis–NIR) NOAA Satellite Instruments, B. Am. Meteorol. Soc., 104, E623–E630, 2023. a
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and Denier van der Gon, H.: CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling, Earth Syst. Sci. Data, 14, 491–515, https://doi.org/10.5194/essd-14-491-2022, 2022. a, b
Lacressonnière, G., Peuch, V.-H., Arteta, J., Josse, B., Joly, M., Marécal, V., Saint Martin, D., Déqué, M., and Watson, L.: How realistic are air quality hindcasts driven by forcings from climate model simulations?, Geosci. Model Dev., 5, 1565–1587, https://doi.org/10.5194/gmd-5-1565-2012, 2012. a
Lahoz, W. A., Geer, A. J., Bekki, S., Bormann, N., Ceccherini, S., Elbern, H., Errera, Q., Eskes, H. J., Fonteyn, D., Jackson, D. R., Khattatov, B., Marchand, M., Massart, S., Peuch, V.-H., Rharmili, S., Ridolfi, M., Segers, A., Talagrand, O., Thornton, H. E., Vik, A. F., and von Clarmann, T.: The Assimilation of Envisat data (ASSET) project, Atmos. Chem. Phys., 7, 1773–1796, https://doi.org/10.5194/acp-7-1773-2007, 2007. a
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017–7039, https://doi.org/10.5194/acp-10-7017-2010, 2010. a, b
Lamarque, J.-F., Shindell, D. T., Josse, B., Young, P. J., Cionni, I., Eyring, V., Bergmann, D., Cameron-Smith, P., Collins, W. J., Doherty, R., Dalsoren, S., Faluvegi, G., Folberth, G., Ghan, S. J., Horowitz, L. W., Lee, Y. H., MacKenzie, I. A., Nagashima, T., Naik, V., Plummer, D., Righi, M., Rumbold, S. T., Schulz, M., Skeie, R. B., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S., Voulgarakis, A., and Zeng, G.: The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): overview and description of models, simulations and climate diagnostics, Geosci. Model Dev., 6, 179–206, https://doi.org/10.5194/gmd-6-179-2013, 2013. a
Lefevre, F., Brasseur, G., Folkins, I., Smith, A., and Simon, P.: Chemistry of the 1991–1992 stratospheric winter: Three-dimensional model simulations, J. Geophys. Res.-Atmos., 99, 8183–8195, 1994. a
Lelieveld, J. and Dentener, F. J.: What controls tropospheric ozone?, J. Geophys. Res.-Atmos., 105, 3531–3551, 2000. a
Lelieveld, J., Berresheim, H., Borrmann, S., Crutzen, P. J., Dentener, F., Fischer, H., Feichter, J., Flatau, P., Heland, J., Holzinger, R., Korrmann, R., Lawrence, M. G., Levin, Z., Markowicz, K. M., Mihalopoulos, N., Minikin, A., Ramanathan, V., de Reus, M., Roelofs, G. J., Scheeren, H. A., Sciare, J., Schlager, H., Schultz, M., Siegmund, P., Steil, B., Stephanou, E. G., Stier, P., Traub, M., Warneke, C., Williams, J., and Ziereis, H.: Global air pollution crossroads over the Mediterranean, Science, 298, 794–799, 2002. a
Marécal, V., Peuch, V.-H., Andersson, C., Andersson, S., Arteta, J., Beekmann, M., Benedictow, A., Bergström, R., Bessagnet, B., Cansado, A., Chéroux, F., Colette, A., Coman, A., Curier, R. L., Denier van der Gon, H. A. C., Drouin, A., Elbern, H., Emili, E., Engelen, R. J., Eskes, H. J., Foret, G., Friese, E., Gauss, M., Giannaros, C., Guth, J., Joly, M., Jaumouillé, E., Josse, B., Kadygrov, N., Kaiser, J. W., Krajsek, K., Kuenen, J., Kumar, U., Liora, N., Lopez, E., Malherbe, L., Martinez, I., Melas, D., Meleux, F., Menut, L., Moinat, P., Morales, T., Parmentier, J., Piacentini, A., Plu, M., Poupkou, A., Queguiner, S., Robertson, L., Rouïl, L., Schaap, M., Segers, A., Sofiev, M., Tarasson, L., Thomas, M., Timmermans, R., Valdebenito, Á., van Velthoven, P., van Versendaal, R., Vira, J., and Ung, A.: A regional air quality forecasting system over Europe: the MACC-II daily ensemble production, Geosci. Model Dev., 8, 2777–2813, https://doi.org/10.5194/gmd-8-2777-2015, 2015. a
Massart, S., Clerbaux, C., Cariolle, D., Piacentini, A., Turquety, S., and Hadji-Lazaro, J.: First steps towards the assimilation of IASI ozone data into the MOCAGE-PALM system, Atmos. Chem. Phys., 9, 5073–5091, https://doi.org/10.5194/acp-9-5073-2009, 2009. a
Masutani, M., Schlatter, T. W., Errico, R. M., Stoffelen, A., Andersson, E., Lahoz, W., Woollen, J. S., Emmitt, G. D., Riishøjgaard, L.-P., and Lord, S. J.: Observing system simulation experiments, in: Data Assimilation, 647–679 pp., Springer, https://doi.org/10.1007/978-3-540-74703-1_24, 2010. a, b
McCarty, W., Errico, R. M., and Gelaro, R.: Cloud coverage in the joint OSSE nature run, Mon. Weather Rev., 140, 1863–1871, 2012. a
Morgenstern, O., Hegglin, M. I., Rozanov, E., O'Connor, F. M., Abraham, N. L., Akiyoshi, H., Archibald, A. T., Bekki, S., Butchart, N., Chipperfield, M. P., Deushi, M., Dhomse, S. S., Garcia, R. R., Hardiman, S. C., Horowitz, L. W., Jöckel, P., Josse, B., Kinnison, D., Lin, M., Mancini, E., Manyin, M. E., Marchand, M., Marécal, V., Michou, M., Oman, L. D., Pitari, G., Plummer, D. A., Revell, L. E., Saint-Martin, D., Schofield, R., Stenke, A., Stone, K., Sudo, K., Tanaka, T. Y., Tilmes, S., Yamashita, Y., Yoshida, K., and Zeng, G.: Review of the global models used within phase 1 of the Chemistry–Climate Model Initiative (CCMI), Geosci. Model Dev., 10, 639–671, https://doi.org/10.5194/gmd-10-639-2017, 2017. a
Orbe, C., Yang, H., Waugh, D. W., Zeng, G., Morgenstern , O., Kinnison, D. E., Lamarque, J.-F., Tilmes, S., Plummer, D. A., Scinocca, J. F., Josse, B., Marecal, V., Jöckel, P., Oman, L. D., Strahan, S. E., Deushi, M., Tanaka, T. Y., Yoshida, K., Akiyoshi, H., Yamashita, Y., Stenke, A., Revell, L., Sukhodolov, T., Rozanov, E., Pitari, G., Visioni, D., Stone, K. A., Schofield, R., and Banerjee, A.: Large-scale tropospheric transport in the Chemistry–Climate Model Initiative (CCMI) simulations, Atmos. Chem. Phys., 18, 7217–7235, https://doi.org/10.5194/acp-18-7217-2018, 2018. a
Peiro, H., Emili, E., Cariolle, D., Barret, B., and Le Flochmoën, E.: Multi-year assimilation of IASI and MLS ozone retrievals: variability of tropospheric ozone over the tropics in response to ENSO, Atmos. Chem. Phys., 18, 6939–6958, https://doi.org/10.5194/acp-18-6939-2018, 2018. a
Phulpin, T., Cayla, F., Chalon, G., Diebel, D., and Schlüssel, P.: IASI on board Metop: Project status and scientific preparation, in: 12th International TOVS Study Conference, Lorne, Victoria, Australia, Vol. 26, https://itwg.ssec.wisc.edu/wordpress/wp-content/uploads/2023/05/5b3_T.Phulpin.pdf (last access: 5 September 2024), 2002. a
Privé, N., Errico, R., and Tai, K.-S.: Validation of the forecast skill of the Global Modeling and Assimilation Office observing system simulation experiment, Q. J. Roy. Meteorol. Soc., 139, 1354–1363, 2013a. a
Privé, N. C., Xie, Y., Woollen, J. S., Koch, S. E., Atlas, R., and Hood, R. E.: Evaluation of the earth systems research laboratory's global observing system simulation experiment system, Tellus A, 65, 19011, 2013b. a
Rouil, L., Honore, C., Vautard, R., Beekmann, M., Bessagnet, B., Malherbe, L., Meleux, F., Dufour, A., Elichegaray, C., Flaud, J.-M., Menut, L., Martin, D., Peuch, A., Peuch, V.-H., and Poisson, N.: PREV'AIR: an operational forecasting and mapping system for air quality in Europe, B. Am. Meteorol. Soc., 90, 73–84, https://doi.org/10.1175/2008BAMS2390.1, 2009. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Sič, B., El Amraoui, L., Marécal, V., Josse, B., Arteta, J., Guth, J., Joly, M., and Hamer, P. D.: Modelling of primary aerosols in the chemical transport model MOCAGE: development and evaluation of aerosol physical parameterizations, Geosci. Model Dev., 8, 381–408, https://doi.org/10.5194/gmd-8-381-2015, 2015. a, b
Sič, B., El Amraoui, L., Piacentini, A., Marécal, V., Emili, E., Cariolle, D., Prather, M., and Attié, J.-L.: Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth, Atmos. Meas. Tech., 9, 5535–5554, https://doi.org/10.5194/amt-9-5535-2016, 2016. a
Siméoni, D., Singer, C., and Chalon, G.: Infrared atmospheric sounding interferometer, Acta Astronaut., 40, 113–118, 1997. a
Simpson, D. and Darras, S.: Global soil NO emissions for Atmospheric Chemical Transport Modelling: CAMS-GLOB-SOIL v2.2, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-221, 2021. a
Sindelarova, K., Granier, C., Bouarar, I., Guenther, A., Tilmes, S., Stavrakou, T., Müller, J.-F., Kuhn, U., Stefani, P., and Knorr, W.: Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years, Atmos. Chem. Phys., 14, 9317–9341, https://doi.org/10.5194/acp-14-9317-2014, 2014. a, b, c
Stockwell, W. R., Kirchner, F., Kuhn, M., and Seefeld, S.: A new mechanism for regional atmospheric chemistry modeling, J. Geophys. Res.-Atmos., 102, 25847–25879, 1997. a
Teyssèdre, H., Michou, M., Clark, H. L., Josse, B., Karcher, F., Olivié, D., Peuch, V.-H., Saint-Martin, D., Cariolle, D., Attié, J.-L., Nédélec, P., Ricaud, P., Thouret, V., van der A, R. J., Volz-Thomas, A., and Chéroux, F.: A new tropospheric and stratospheric Chemistry and Transport Model MOCAGE-Climat for multi-year studies: evaluation of the present-day climatology and sensitivity to surface processes, Atmos. Chem. Phys., 7, 5815–5860, https://doi.org/10.5194/acp-7-5815-2007, 2007. a
Timmermans, R. M., Lahoz, W., Attié, J.-L., Peuch, V.-H., Curier, R., Edwards, D., Eskes, H., and Builtjes, P.: Observing system simulation experiments for air quality, Atmos. Environ., 115, 199–213, 2015. a
Watson, L., Lacressonnière, G., Gauss, M., Engardt, M., Andersson, C., Josse, B., Marécal, V., Nyiri, A., Sobolowski, S., Siour, G., Szopa, S., and Vautard, R.: Impact of emissions and 2 C climate change upon future ozone and nitrogen dioxide over Europe, Atmos. Environ., 142, 271–285, https://doi.org/10.1016/j.atmosenv.2016.07.051, 2016. a
Weston, P., Bell, W., and Eyre, J.: Accounting for correlated error in the assimilation of high-resolution sounder data, Q. J. Roy. Meteorol. Soc., 140, 2420–2429, 2014. a
Williams, J. E. ., Huijnen, V., Bouarar, I., Meziane, M., Schreurs, T., Pelletier, S., Marécal, V., Josse, B., and Flemming, J.: Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1), Geosci. Model Dev., 15, 4657–4687, https://doi.org/10.5194/gmd-15-4657-2022, 2022. a
Yang, J., Zhang, Z., Wei, C., Lu, F., and Guo, Q.: Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4, B. Am. Meteorol. Soc., 98, 1637–1658, 2017. a
Zeng, X., Atlas, R., Birk, R. J., Carr, F. H., Carrier, M. J., Cucurull, L., Hooke, W. H., Kalnay, E., Murtugudde, R., Posselt, D. J., et al.: Use of observing system simulation experiments in the United States, B. Am. Meteorol. Soc., 101, E1427–E1438, 2020. a
Zeng, Z.-C., Lee, L., and Qi, C.: Diurnal carbon monoxide observed from a geostationary infrared hyperspectral sounder: first result from GIIRS on board FengYun-4B, Atmos. Meas. Tech., 16, 3059–3083, https://doi.org/10.5194/amt-16-3059-2023, 2023. a
Short summary
The future Meteosat Third Generation Infrared Sounder (MTG-IRS) will represent a major innovation for the monitoring of the chemical state of the atmosphere. MTG-IRS will have the advantage of being based on a geostationary platform and acquiring data with a high temporal frequency. This work aims to evaluate its potential impact over Europe within a chemical transport model (MOCAGE). The results indicate that the assimilation of these data always has a positive impact on ozone analysis.
The future Meteosat Third Generation Infrared Sounder (MTG-IRS) will represent a major...