Articles | Volume 15, issue 17
https://doi.org/10.5194/amt-15-5129-2022
© 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-15-5129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Sep 2022
Research article |
| 09 Sep 2022
| 09 Sep 2022
Validation of Copernicus Sentinel-3/OLCI Level 2 Land Integrated Water Vapour product
Space and Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
Viktoria F. Sofieva
Space and Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
René Preusker
Institute for Space Sciences, Freie Universität Berlin (FUB), Berlin, Germany
Claire Henocq
ACRI-ST, Sophia-Antipolis, France
Matthieu Denisselle
ACRI-ST, Sophia-Antipolis, France
Steffen Dransfeld
European Space Research Institute (ESRIN), Frascati, Italy
Silvia Scifoni
Serco Italia SpA for European Space Agency (ESA), European Space Research Institute (ESRIN), Frascati, Italy
Related authors
Pekka T. Verronen, Akira Mizuno, Yoshizumi Miyoshi, Sandeep Kumar, Taku Nakajima, Shin-Ichiro Oyama, Tomoo Nagahama, Satonori Nozawa, Monika E. Szeląg, Tuomas Häkkilä, Niilo Kalakoski, Antti Kero, Esa Turunen, Satoshi Kasahara, Shoichiro Yokota, Kunihiro Keika, Tomoaki Hori, Takefumi Mitani, Takeshi Takashima, and Iku Shinohara
Ann. Geophys., 43, 561–578, https://doi.org/10.5194/angeo-43-561-2025, https://doi.org/10.5194/angeo-43-561-2025, 2025
Short summary
Short summary
We use NO column density data from the Syowa station in Antarctica from 2012–2017. We compare these ground-based radiometer observations with results from a global atmosphere model to understand the year-to-year and day-to-day variability, shortcomings of current electron forcing, and how geomagnetic storms are driving the variability of NO. Our results demonstrate an underestimation in the magnitude of day-to-day variability in simulations, which calls for improved electron forcing in models.
Tuomas Häkkilä, Maxime Grandin, Markus Battarbee, Monika E. Szeląg, Markku Alho, Leo Kotipalo, Niilo Kalakoski, Pekka T. Verronen, and Minna Palmroth
Ann. Geophys., 43, 217–240, https://doi.org/10.5194/angeo-43-217-2025, https://doi.org/10.5194/angeo-43-217-2025, 2025
Short summary
Short summary
We study the atmospheric impact of auroral electron precipitation through the novel combination of both magnetospheric modelling and atmospheric modelling. We first simulate fluxes of auroral electrons and then use these fluxes to model their atmospheric impact. We find an increase of more than 200 % in thermospheric odd nitrogen and a corresponding decrease in stratospheric ozone of around 0.8 %. The produced auroral electron precipitation is realistic and shows potential for future studies.
Caroline Jonas, Corinne Vigouroux, Bavo Langerock, Robin Björklund, Anne Boynard, Thomas Carlund, Martine De Mazière, Peter Effertz, Quentin Errera, Matthias Frey, James W. Hannigan, Nis Jepsen, Rigel Kivi, Norrie Lyall, Mathias Palm, Maxime Prignon, Viktoria F. Sofieva, Kimberly Strong, Tove Svendby, David Tarasick, Laura Thölix, Roeland Van Malderen, Yana Virolainen, Sibylle von Löwis, and Xiaoyi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2025-6473, https://doi.org/10.5194/egusphere-2025-6473, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We study the evolution of ozone in the Arctic over the 2000–2024 period in the stratosphere (about 10 to 50 km) to assess the expected recovery of the ozone layer following the diminution of ozone-depleting substances. We merge ground-based data sets within spatially coherent regions to reduce uncertainties and we obtain positive trends for the total column everywhere in the Arctic and for the middle and upper stratosphere over Canada, but no significant trends in the lower stratosphere.
Viktoria F. Sofieva, Monika E. Szelag, Natalia Kramarova, Robert Damadeo, Wolfgang Steinbrecht, Irina Petropavlovskikh, Corinne Vigouroux, Eliane Maillard Barras, Daniel Zawada, Kleareti Tourpali, Stacey M. Frith, Jeannette D. Wild, Sean M. Davis, Carlo Arosio, Mark Weber, Alexei Rozanov, Brian Auffarth, Lucien Froidevaux, Ryan Fuller, Doug Degenstein, Kimberlee Dube, Peter Effertz, Thierry Leblanc, Gérard Ancellet, Sophie Godin-Beekmann, Glen McConville, Richard Querel, Dan Smale, Marie-Renee DeBacker, Emmanuel Mahieu, and Ralf Sussmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-5963, https://doi.org/10.5194/egusphere-2025-5963, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We present an updated evaluation of stratospheric ozone profile trends in the 60°S–60°N latitude range using long-term ground-based and satellite climate data records, as well as simulations by chemistry-climate models. Analyses confirm the statistically significant positive ozone trends in the upper stratosphere of ~1–3 % decade-1. The trends are close to zero in the middle stratosphere, and mostly negative in the lower stratosphere, but they are not statistically significant.
Monika E. Szelag, Viktoria F. Sofieva, Edward Malina, Pekka T. Verronen, Michelle L. Santee, Manuel López-Puertas, Bernd Funke, Gabriele Stiller, Alexandra Laeng, Kaley A. Walker, Patrick E. Sheese, Mark E. Hervig, and Benjamin T. Marshall
EGUsphere, https://doi.org/10.5194/egusphere-2025-6236, https://doi.org/10.5194/egusphere-2025-6236, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We present a new global dataset of ozone profiles in the mesosphere and lower thermosphere, created by combining several satellite measurements covering more than three decades. Our results show that ozone is recovering in the stratosphere but decreasing in the mesosphere, with the strongest declines near the mesopause. This dataset provides a valuable resource for investigating long-term changes, improving model performance, and addressing an observational gap in the upper atmosphere.
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.
Pekka T. Verronen, Akira Mizuno, Yoshizumi Miyoshi, Sandeep Kumar, Taku Nakajima, Shin-Ichiro Oyama, Tomoo Nagahama, Satonori Nozawa, Monika E. Szeląg, Tuomas Häkkilä, Niilo Kalakoski, Antti Kero, Esa Turunen, Satoshi Kasahara, Shoichiro Yokota, Kunihiro Keika, Tomoaki Hori, Takefumi Mitani, Takeshi Takashima, and Iku Shinohara
Ann. Geophys., 43, 561–578, https://doi.org/10.5194/angeo-43-561-2025, https://doi.org/10.5194/angeo-43-561-2025, 2025
Short summary
Short summary
We use NO column density data from the Syowa station in Antarctica from 2012–2017. We compare these ground-based radiometer observations with results from a global atmosphere model to understand the year-to-year and day-to-day variability, shortcomings of current electron forcing, and how geomagnetic storms are driving the variability of NO. Our results demonstrate an underestimation in the magnitude of day-to-day variability in simulations, which calls for improved electron forcing in models.
Viktoria F. Sofieva, Alexandra Laeng, Thomas von Clarmann, Gabriele Stiller, Michael Kiefer, Johanna Tamminen, Alexey Rozanov, Carlo Arosio, Nathaniel Livesey, Robert Damadeo, Patrick Sheese, Kaley A. Walker, Doug Degenstein, Daniel Zawada, Natalya A. Kramarova, and Arno Keppens
EGUsphere, https://doi.org/10.5194/egusphere-2025-2830, https://doi.org/10.5194/egusphere-2025-2830, 2025
Short summary
Short summary
For satellite measurements of atmospheric composition, the random uncertainty estimates provided by retrieval algorithms might be imperfect due to various approximations used in the retrievals or presence of unknown error sources. This paper presents an overview of the methods used for validation of random uncertainty estimates. All methods discussed in this study are categorized, and assumptions and limitations of each method are discussed.
Carlo Arosio, Viktoria Sofieva, Andrea Orfanoz-Cheuquelaf, Alexei Rozanov, Klaus-Peter Heue, Diego Loyola, Edward Malina, Ryan M. Stauffer, David Tarasick, Roeland Van Malderen, Jerry R. Ziemke, and Mark Weber
Atmos. Meas. Tech., 18, 3247–3265, https://doi.org/10.5194/amt-18-3247-2025, https://doi.org/10.5194/amt-18-3247-2025, 2025
Short summary
Short summary
Tropospheric ozone affects air quality and climate, being a pollutant and a greenhouse gas. We analyze satellite data of tropospheric ozone columns obtained by combining two types of observations: one providing stratospheric and the other total ozone. We compare common climatological features and study the influence of the tropopause (troposphere to stratosphere boundary) on the results. We also examine trends over the last 20 years and compare satellite data with ozonesondes to identify drifts.
Tuomas Häkkilä, Maxime Grandin, Markus Battarbee, Monika E. Szeląg, Markku Alho, Leo Kotipalo, Niilo Kalakoski, Pekka T. Verronen, and Minna Palmroth
Ann. Geophys., 43, 217–240, https://doi.org/10.5194/angeo-43-217-2025, https://doi.org/10.5194/angeo-43-217-2025, 2025
Short summary
Short summary
We study the atmospheric impact of auroral electron precipitation through the novel combination of both magnetospheric modelling and atmospheric modelling. We first simulate fluxes of auroral electrons and then use these fluxes to model their atmospheric impact. We find an increase of more than 200 % in thermospheric odd nitrogen and a corresponding decrease in stratospheric ozone of around 0.8 %. The produced auroral electron precipitation is realistic and shows potential for future studies.
Viktoria F. Sofieva, Alexei Rozanov, Monika Szelag, John P. Burrows, Christian Retscher, Robert Damadeo, Doug Degenstein, Landon A. Rieger, and Adam Bourassa
Earth Syst. Sci. Data, 16, 5227–5241, https://doi.org/10.5194/essd-16-5227-2024, https://doi.org/10.5194/essd-16-5227-2024, 2024
Short summary
Short summary
Climate-related studies need information about the distribution of stratospheric aerosols, which influence the energy balance of the Earth’s atmosphere. In this work, we present a merged dataset of vertically resolved stratospheric aerosol extinction coefficients, which is derived from data of six limb and occultation satellite instruments. The created aerosol climate record covers the period from October 1984 to December 2023. It can be used in various climate-related studies.
Robert P. Damadeo, Viktoria F. Sofieva, Alexei Rozanov, and Larry W. Thomason
Atmos. Meas. Tech., 17, 3669–3678, https://doi.org/10.5194/amt-17-3669-2024, https://doi.org/10.5194/amt-17-3669-2024, 2024
Short summary
Short summary
Comparing different aerosol data sets for scientific studies often requires converting aerosol extinction data between different wavelengths. A common approximation for the spectral behavior of aerosol is the Ångström formula; however, this introduces biases. Using measurements across many different wavelengths from a single instrument, we derive an empirical relationship to both characterize this bias and offer a correction for other studies that may employ this analysis approach.
Viktoria F. Sofieva, Monika Szelag, Johanna Tamminen, Didier Fussen, Christine Bingen, Filip Vanhellemont, Nina Mateshvili, Alexei Rozanov, and Christine Pohl
Atmos. Meas. Tech., 17, 3085–3101, https://doi.org/10.5194/amt-17-3085-2024, https://doi.org/10.5194/amt-17-3085-2024, 2024
Short summary
Short summary
We have developed the new multi-wavelength dataset of aerosol extinction profiles, which are retrieved from the averaged transmittance spectra by the Global Ozone Monitoring by Occultation of Stars instrument aboard Envisat. The retrieved aerosol extinction profiles are provided in the altitude range 10–40 km at 400, 440, 452, 470, 500, 525, 550, 672 and 750 nm for the period 2002–2012. FMI-GOMOSaero aerosol profiles have improved quality; they are in good agreement with other datasets.
Viktoria F. Sofieva, Monika Szelag, Johanna Tamminen, Carlo Arosio, Alexei Rozanov, Mark Weber, Doug Degenstein, Adam Bourassa, Daniel Zawada, Michael Kiefer, Alexandra Laeng, Kaley A. Walker, Patrick Sheese, Daan Hubert, Michel van Roozendael, Christian Retscher, Robert Damadeo, and Jerry D. Lumpe
Atmos. Meas. Tech., 16, 1881–1899, https://doi.org/10.5194/amt-16-1881-2023, https://doi.org/10.5194/amt-16-1881-2023, 2023
Short summary
Short summary
The paper presents the updated SAGE-CCI-OMPS+ climate data record of monthly zonal mean ozone profiles. This dataset covers the stratosphere and combines measurements by nine limb and occultation satellite instruments (SAGE II, OSIRIS, MIPAS, SCIAMACHY, GOMOS, ACE-FTS, OMPS-LP, POAM III, and SAGE III/ISS). The update includes new versions of MIPAS, ACE-FTS, and OSIRIS datasets and introduces data from additional sensors (POAM III and SAGE III/ISS) and retrieval processors (OMPS-LP).
Larisa Sogacheva, Matthieu Denisselle, Pekka Kolmonen, Timo H. Virtanen, Peter North, Claire Henocq, Silvia Scifoni, and Steffen Dransfeld
Atmos. Meas. Tech., 15, 5289–5322, https://doi.org/10.5194/amt-15-5289-2022, https://doi.org/10.5194/amt-15-5289-2022, 2022
Short summary
Short summary
The aim of this study was to provide global characterisation of a new SYNERGY aerosol product derived from the data from the OLCI and SLSTR sensors aboard the Sentinel-3A and Sentinel-3B satellites. Over ocean, the performance of SYNERGY-retrieved AOD is good. Reduced performance over land was expected since the surface reflectance and angular distribution of scattering are more difficult to treat. Validation statistics are often slightly better for S3B and in the Southern Hemisphere.
Sophie Godin-Beekmann, Niramson Azouz, Viktoria F. Sofieva, Daan Hubert, Irina Petropavlovskikh, Peter Effertz, Gérard Ancellet, Doug A. Degenstein, Daniel Zawada, Lucien Froidevaux, Stacey Frith, Jeannette Wild, Sean Davis, Wolfgang Steinbrecht, Thierry Leblanc, Richard Querel, Kleareti Tourpali, Robert Damadeo, Eliane Maillard Barras, René Stübi, Corinne Vigouroux, Carlo Arosio, Gerald Nedoluha, Ian Boyd, Roeland Van Malderen, Emmanuel Mahieu, Dan Smale, and Ralf Sussmann
Atmos. Chem. Phys., 22, 11657–11673, https://doi.org/10.5194/acp-22-11657-2022, https://doi.org/10.5194/acp-22-11657-2022, 2022
Short summary
Short summary
An updated evaluation up to 2020 of stratospheric ozone profile long-term trends at extrapolar latitudes based on satellite and ground-based records is presented. Ozone increase in the upper stratosphere is confirmed, with significant trends at most latitudes. In this altitude region, a very good agreement is found with trends derived from chemistry–climate model simulations. Observed and modelled trends diverge in the lower stratosphere, but the differences are non-significant.
Viktoria F. Sofieva, Risto Hänninen, Mikhail Sofiev, Monika Szeląg, Hei Shing Lee, Johanna Tamminen, and Christian Retscher
Atmos. Meas. Tech., 15, 3193–3212, https://doi.org/10.5194/amt-15-3193-2022, https://doi.org/10.5194/amt-15-3193-2022, 2022
Short summary
Short summary
We present tropospheric ozone column datasets that have been created using combinations of total ozone column from OMI and TROPOMI with stratospheric ozone column datasets from several available limb-viewing instruments (MLS, OSIRIS, MIPAS, SCIAMACHY, OMPS-LP, GOMOS). The main results are (i) several methodological developments, (ii) new tropospheric ozone column datasets from OMI and TROPOMI, and (iii) a new high-resolution dataset of ozone profiles from limb satellite instruments.
Viktoria F. Sofieva, Monika Szeląg, Johanna Tamminen, Erkki Kyrölä, Doug Degenstein, Chris Roth, Daniel Zawada, Alexei Rozanov, Carlo Arosio, John P. Burrows, Mark Weber, Alexandra Laeng, Gabriele P. Stiller, Thomas von Clarmann, Lucien Froidevaux, Nathaniel Livesey, Michel van Roozendael, and Christian Retscher
Atmos. Chem. Phys., 21, 6707–6720, https://doi.org/10.5194/acp-21-6707-2021, https://doi.org/10.5194/acp-21-6707-2021, 2021
Short summary
Short summary
The MErged GRIdded Dataset of Ozone Profiles is a long-term (2001–2018) stratospheric ozone profile climate data record with resolved longitudinal structure that combines the data from six limb satellite instruments. The dataset can be used for various analyses, some of which are discussed in the paper. In particular, regionally and vertically resolved ozone trends are evaluated, including trends in the polar regions.
Viktoria F. Sofieva, Hei Shing Lee, Johanna Tamminen, Christophe Lerot, Fabian Romahn, and Diego G. Loyola
Atmos. Meas. Tech., 14, 2993–3002, https://doi.org/10.5194/amt-14-2993-2021, https://doi.org/10.5194/amt-14-2993-2021, 2021
Short summary
Short summary
Our paper discusses the structure function method, which allows validation of random uncertainties in the data and, at the same time, probing of the small-scale natural variability. We applied this method to the clear-sky total ozone measurements by TROPOMI Sentinel-5P satellite instrument and found that the TROPOMI random error estimation is adequate. The discussed method is a powerful tool, which can be used in various applications.
Cited articles
Bengtsson, L.: The global atmospheric water cycle, Environ. Res. Lett., 5, 025202, https://doi.org/10.1088/1748-9326/5/2/025202, 2010. a
Bengtsson, L. and Hodges, K. I.: On the impact of humidity observations in
numerical weather prediction, Tellus A, 57, 701–708, 2005. a
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of essential climate variables in support of climate
research, applications, and policy, B. Am. Meteorol. Soc., 95, 1431–1443, 2014. a
Diedrich, H., Preusker, R., Lindstrot, R., and Fischer, J.: Retrieval of daytime total columnar water vapour from MODIS measurements over land surfaces, Atmos. Meas. Tech., 8, 823–836, https://doi.org/10.5194/amt-8-823-2015, 2015. a
Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M.-H., Féménias, P., Frerick, J., Goryl, P., Klein, U., Laur, H., Mavrocordatos, C., Nieke, J., Rebhan, H., Seitz, B., Stroede, J., and Sciarra, R.: The
global monitoring for environment and security (GMES) sentinel-3 mission,
Remote Sens. Environ., 120, 37–57, 2012. a
Doppler, L., Preusker, R., Bennartz, R., and Fischer, J.: K-bin and k-IR: K-distribution methods without correlation approximation for non-fixed instrument response function and extension to the thermal infrared-Applications to satellite remote sensing, J. Quant. Spectrosc. Ra., 133, 382–395, https://doi.org/10.1016/j.jqsrt.2013.09.001, 2013. a
Durre, I., Xungang, Y., Vose, R. S., Applequist, S., and Arnfield, J.: Integrated Global Radiosonde Archive (IGRA), Version 2, NOAA National Centers for Environmental Information, 10, V5X63X0Q, https://doi.org/10.7289/V5X63K0Q, 2016. a
Durre, I., Yin, X., Vose, R. S., Applequist, S., and Arnfield, J.: Enhancing
the data coverage in the Integrated Global Radiosonde Archive, J. Atmos. Ocean. Tech., 35, 1753–1770, 2018. a
Grossi, M., Valks, P., Loyola, D., Aberle, B., Slijkhuis, S., Wagner, T., Beirle, S., and Lang, R.: Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B, Atmos. Meas. Tech., 8, 1111–1133, https://doi.org/10.5194/amt-8-1111-2015, 2015. a
Kalakoski, N., Kujanpää, J., Sofieva, V., Tamminen, J., Grossi, M., and Valks, P.: Validation of GOME-2/Metop total column water vapour with ground-based and in situ measurements, Atmos. Meas. Tech., 9, 1533–1544, https://doi.org/10.5194/amt-9-1533-2016, 2016. a
Küchler, T., Noël, S., Bovensmann, H., Burrows, J. P., Wagner, T., Borger, C., Borsdorff, T., and Schneider, A.: Total water vapour columns derived from Sentinel 5P using the AMC-DOAS method, Atmos. Meas. Tech., 15, 297–320, https://doi.org/10.5194/amt-15-297-2022, 2022. a
Lindstrot, R., Preusker, R., Diedrich, H., Doppler, L., Bennartz, R., and Fischer, J.: 1D-Var retrieval of daytime total columnar water vapour from MERIS measurements, Atmos. Meas. Tech., 5, 631–646, https://doi.org/10.5194/amt-5-631-2012, 2012. a, b
Mertikas, S., Partsinevelos, P., Tripolitsiotis, A., Kokolakis, C., Petrakis,
G., and Frantzis, X.: Validation of Sentinel-3 OLCI integrated water vapor
products using regional GNSS measurements in crete, Greece, Remote Sens.,
12, 2606, https://doi.org/10.3390/rs12162606, 2020. a
Noël, S., Buchwitz, M., Bovensmann, H., Hoogen, R., and Burrows, J. P.:
Atmospheric water vapor amounts retrieved from GOME satellite data,
Geophys. Res. Lett., 26, 1841–1844, 1999. a
Noël, S., Buchwitz, M., and Burrows, J. P.: First retrieval of global water vapour column amounts from SCIAMACHY measurements, Atmos. Chem. Phys., 4, 111–125, https://doi.org/10.5194/acp-4-111-2004, 2004. a
Noël, S., Mieruch, S., Bovensmann, H., and Burrows, J. P.: Preliminary results of GOME-2 water vapour retrievals and first applications in polar regions, Atmos. Chem. Phys., 8, 1519–1529, https://doi.org/10.5194/acp-8-1519-2008, 2008. a
Preusker, R., Carbajal Henken, C., and Fischer, J.: Retrieval of Daytime Total Column Water Vapour from OLCI Measurements over Land Surfaces, Remote
Sens., 13, 932, https://doi.org/10.3390/rs13050932, 2021. a
Rast, M., Bezy, J. L., and Bruzzi, S.: The ESA Medium Resolution Imaging
Spectrometer MERIS a review of the instrument and its mission, Int. J. Remote Sens., 20, 1681–1702, https://doi.org/10.1080/014311699212416, 1999. a
Roman, J., Knuteson, R., August, T., Hultberg, T., Ackerman, S., and Revercomb, H.: A global assessment of NASA AIRS v6 and EUMETSAT IASI v6 precipitable water vapor using ground-based GPS SuomiNet stations, J. Geophys. Res.-Atmos., 121, 8925–8948, 2016. a
Schluessel, P. and Emery, W. J.: Atmospheric water vapour over oceans from SSM/I measurements, Int. J. Remote Sens., 11, 753–766, 1990. a
Schlüssel, P. and Goldberg, M.: Retrieval of atmospheric temperature and water vapour from IASI measurements in partly cloudy situations, Adv. Space Res., 29, 1703–1706, https://doi.org/10.1016/S0273-1177(02)00101-1, 2002. a
Schröder, M., Lockhoff, M., Fell, F., Forsythe, J., Trent, T., Bennartz, R., Borbas, E., Bosilovich, M. G., Castelli, E., Hersbach, H., Kachi, M., Kobayashi, S., Kursinski, E. R., Loyola, D., Mears, C., Preusker, R., Rossow, W. B., and Saha, S.: The GEWEX Water Vapor Assessment archive of water vapour products from satellite observations and reanalyses, Earth Syst. Sci. Data, 10, 1093–1117, https://doi.org/10.5194/essd-10-1093-2018, 2018. a
Seidel, D. J., Sun, B., Pettey, M., and Reale, A.: Global radiosonde balloon
drift statistics, J. Geophys. Res.-Atmos., 116, D07102, https://doi.org/10.1029/2010JD014891, 2011. a
Sherwood, S., Roca, R., Weckwerth, T., and Andronova, N.: Tropospheric water
vapor, convection, and climate, Rev. Geophys., 48, RG2001, https://doi.org/10.1029/2009RG000301, 2010. a
Shi, L. and Bates, J. J.: Three decades of intersatellite-calibrated
High-Resolution Infrared Radiation Sounder upper tropospheric water vapor,
J. Geophys. Res.-Atmos., 116, D04108, https://doi.org/10.1029/2010JD014847, 2011.
a
Sofieva, V. F., Lee, H. S., Tamminen, J., Lerot, C., Romahn, F., and Loyola, D. G.: A method for random uncertainties validation and probing the natural variability with application to TROPOMI on board Sentinel-5P total ozone measurements, Atmos. Meas. Tech., 14, 2993–3002, https://doi.org/10.5194/amt-14-2993-2021, 2021. a
Van Malderen, R., Brenot, H., Pottiaux, E., Beirle, S., Hermans, C., De Mazière, M., Wagner, T., De Backer, H., and Bruyninx, C.: A multi-site intercomparison of integrated water vapour observations for climate change analysis, Atmos. Meas. Tech., 7, 2487–2512, https://doi.org/10.5194/amt-7-2487-2014, 2014. a, b, c, d, e
Wagner, T., Beirle, S., Sihler, H., and Mies, K.: A feasibility study for the retrieval of the total column precipitable water vapour from satellite observations in the blue spectral range, Atmos. Meas. Tech., 6, 2593–2605, https://doi.org/10.5194/amt-6-2593-2013, 2013. a
Wang, H., Liu, X., Chance, K., González Abad, G., and Chan Miller, C.: Water vapor retrieval from OMI visible spectra, Atmos. Meas. Tech., 7, 1901–1913, https://doi.org/10.5194/amt-7-1901-2014, 2014. a
Wang, J. and Zhang, L.: Systematic errors in global radiosonde precipitable
water data from comparisons with ground-based GPS measurements, J. Climate, 21, 2218–2238, 2008. a
Wang, J., Zhang, L., Dai, A., Van Hove, T., and Van Baelen, J.: A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements, J. Geophys. Res.-Atmos., 112, D11107, https://doi.org/10.1029/2006JD007529, 2007. a, b, c, d
Ware, R. H., Fulker, D. W., Stein, S. A., Anderson, D. N., Avery, S. K., Clark, R. D., Droegemeier, K. K., Kuettner, J. P., Minster, J. B., and Sorooshian, S.: SuomiNet: A real-time national GPS network for atmospheric research and education, B. Am. Meteorol. Soc., 81, 677–694, 2000. a
Wentz, F. J.: A well-calibrated ocean algorithm for special sensor
microwave/imager, J. Geophys. Res.-Oceans, 102, 8703–8718, 1997. a
Short summary
Geophysical validation of the Integrated Water Vapour (IWV) product from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) was performed against reference observations from SUOMINET and IGRA databases. Results for cloud-free matchups over land show a wet bias of 7 %–10 % for OLCI, with a high correlation against the reference observations (0.98 against SUOMINET and 0.90 against IGRA). Special attention is given to validation of uncertainty estimates and cloud flagging.
Geophysical validation of the Integrated Water Vapour (IWV) product from the Sentinel-3 Ocean...
