Articles | Volume 14, issue 1
https://doi.org/10.5194/essd-14-307-2022
© 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/essd-14-307-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Jan 2022
Data description paper |
| 28 Jan 2022
| 28 Jan 2022
Improved BEC SMOS Arctic Sea Surface Salinity product v3.1
Justino Martínez
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Antonio Turiel
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Verónica González-Gambau
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Marta Umbert
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Nina Hoareau
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Cristina González-Haro
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Estrella Olmedo
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
Manuel Arias
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37–49, 08003 Barcelona, Spain
ARGANS, Derriford, PL6 8BX Plymouth, UK
Rafael Catany
ARGANS, Derriford, PL6 8BX Plymouth, UK
Laurent Bertino
Nansen Environmental and Remote Sensing Center – NERSC, Jahnebakken 3, 5007 Bergen, Norway
Roshin P. Raj
Nansen Environmental and Remote Sensing Center – NERSC, Jahnebakken 3, 5007 Bergen, Norway
Jiping Xie
Nansen Environmental and Remote Sensing Center – NERSC, Jahnebakken 3, 5007 Bergen, Norway
Roberto Sabia
Telespazio-Vega UK Ltd., for ESA-ESRIN, Largo
Galileo Galilei 1, 00044 Frascati, Italy
Diego Fernández
ESA-ESRIN, Largo
Galileo Galilei 1, 00044 Frascati, Italy
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Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
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Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, https://doi.org/10.5194/os-19-269-2023, 2023
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Short summary
Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Vidar S. Lien and Roshin P. Raj
State Planet Discuss., https://doi.org/10.5194/sp-2022-13, https://doi.org/10.5194/sp-2022-13, 2022
Preprint withdrawn
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Dense overflow water entering the North Atlantic from the Nordic Seas forms the northern limb of the Atlantic Meridional Overturning Circulation. The formation of dense water in the Nordic Seas is sensitive to the properties of the northward flowing Atlantic Water entering the Nordic Seas to the south. We find that the unprecedented freshwater anomaly in the North Atlantic recent years caused the dense water formed in the Barents Sea to have the lowest density in recorded history.
Verónica González-Gambau, Estrella Olmedo, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Justino Martínez, Pekka Alenius, Laura Tuomi, Rafael Catany, Manuel Arias, Carolina Gabarró, Nina Hoareau, Marta Umbert, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 2343–2368, https://doi.org/10.5194/essd-14-2343-2022, https://doi.org/10.5194/essd-14-2343-2022, 2022
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We present the first Soil Moisture and Ocean Salinity Sea Surface Salinity (SSS) dedicated products over the Baltic Sea (ESA Baltic+ Salinity Dynamics). The Baltic+ L3 product covers 9 days in a 0.25° grid. The Baltic+ L4 is derived by merging L3 SSS with sea surface temperature information, giving a daily product in a 0.05° grid. The accuracy of L3 is 0.7–0.8 and 0.4 psu for the L4. Baltic+ products have shown to be useful, covering spatiotemporal data gaps and for validating numerical models.
Fabio Mangini, Léon Chafik, Antonio Bonaduce, Laurent Bertino, and Jan Even Ø. Nilsen
Ocean Sci., 18, 331–359, https://doi.org/10.5194/os-18-331-2022, https://doi.org/10.5194/os-18-331-2022, 2022
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Martin Horwath, Benjamin D. Gutknecht, Anny Cazenave, Hindumathi Kulaiappan Palanisamy, Florence Marti, Ben Marzeion, Frank Paul, Raymond Le Bris, Anna E. Hogg, Inès Otosaka, Andrew Shepherd, Petra Döll, Denise Cáceres, Hannes Müller Schmied, Johnny A. Johannessen, Jan Even Øie Nilsen, Roshin P. Raj, René Forsberg, Louise Sandberg Sørensen, Valentina R. Barletta, Sebastian B. Simonsen, Per Knudsen, Ole Baltazar Andersen, Heidi Ranndal, Stine K. Rose, Christopher J. Merchant, Claire R. Macintosh, Karina von Schuckmann, Kristin Novotny, Andreas Groh, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, https://doi.org/10.5194/essd-14-411-2022, 2022
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Global mean sea-level change observed from 1993 to 2016 (mean rate of 3.05 mm yr−1) matches the combined effect of changes in water density (thermal expansion) and ocean mass. Ocean-mass change has been assessed through the contributions from glaciers, ice sheets, and land water storage or directly from satellite data since 2003. Our budget assessments of linear trends and monthly anomalies utilise new datasets and uncertainty characterisations developed within ESA's Climate Change Initiative.
Estrella Olmedo, Verónica González-Gambau, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Marilaure Gregoire, Aida Álvera-Azcárate, Luminita Buga, and Marie-Hélène Rio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-364, https://doi.org/10.5194/essd-2021-364, 2021
Revised manuscript not accepted
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We present the first dedicated satellite salinity product in the Black Sea. We use the measurements provided by the European Soil Moisture and Ocean Salinity mission. We introduce enhanced algorithms for dealing with the contamination produced by the Radio Frequency Interferences that strongly affect this basin. We also provide a complete quality assessment of the new product and give an estimated accuracy of it.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
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The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
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Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
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Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Estrella Olmedo, Cristina González-Haro, Nina Hoareau, Marta Umbert, Verónica González-Gambau, Justino Martínez, Carolina Gabarró, and Antonio Turiel
Earth Syst. Sci. Data, 13, 857–888, https://doi.org/10.5194/essd-13-857-2021, https://doi.org/10.5194/essd-13-857-2021, 2021
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After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission is still a unique, high-quality instrument for providing soil moisture over land and sea surface salinity (SSS) over the oceans. At the Barcelona
Expert Center (BEC), a new reprocessing of 9 years (2011–2019) of global SMOS SSS maps has been generated. This work presents the algorithms used in the generation of the BEC global SMOS SSS product v2.0, as well as an extensive quality assessment.
Anna V. Vesman, Igor L. Bashmachnikov, Pavel A. Golubkin, and Roshin P. Raj
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-109, https://doi.org/10.5194/os-2020-109, 2020
Revised manuscript not accepted
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Atlantic Waters carry heat and salt towards Arctic. The goal of this study was to study how the heat flux changes with its journey to the north. It was shown that despite the fact that there is some connection between variability of the heat flux near the shores of Norway and heat fluxes in the northern part of the Fram Strait. There are different processes governing this variability, which results in a different tendencies in the southern and northern regions of the study.
Cited articles
Anterrieu, E., Waldteufel, P., and Lannes, A.: Apodization functions for 2-D
hexagonally sampled synthetic aperture imaging radiometers, IEEE T.
Geosci. Remote, 40, 2531–2542, https://doi.org/10.1109/TGRS.2002.1176146, 2002. a
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo
GDAC), SEANOE,
https://doi.org/10.17882/42182, 2018. a
Cummings, J. A.: Operational multivariate ocean data assimilation, Q.
J. Roy. Meteor. Soc., 131, 3583–3604,
https://doi.org/10.1256/qj.05.105, 2005. a
Cummings, J. A. and Smedstad, O. M.: Variational Data Assimilation for the
Global Ocean, Springer Berlin Heidelberg, Berlin, Heidelberg, 303–343,
https://doi.org/10.1007/978-3-642-35088-7_13, 2013. a
Debye, P.: Polar molecules, The Chemical Catalog Company
Inc., New York, NY, USA, New York, 1929. a
Dinnat, E. P., Le Vine, D. M., Boutin, J., Meissner, T., and Lagerloef, G.:
Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ
Observations and Impact of Retrieval Parameters, Remote Sens., 11, 750–785,
https://doi.org/10.3390/rs11070750, 2019. a, b
ESA: Earth Observation CFI v3.X branch, ESA, available at:
http://eop-cfi.esa.int/index.php/mission-cfi-software/eocfi-software/branch-3-x
(last access: 4 October 2019), 2014. a
Font, J., Camps, A., Borges, A., Martin-Neira, M., Boutin, J., Reul, N., Kerr,
Y., Hahne, A., and Mechlenburg, S.: SMOS: the challenging sea surface
salinity measurement from space, Proc. IEEE, 98, 640–665, 2010. a
Fournier, S., Lee, T., Wang, X., Armitage, T. W. K., Wang, O., Fukumori, I.,
and Kwok, R.: Sea Surface Salinity as a Proxy for Arctic Ocean Freshwater
Changes, J. Geophys. Res.-Oceans, 125, e2020JC016110,
https://doi.org/10.1029/2020JC016110, 2020. a
González-Gambau, V., Turiel, A., González-Haro, C., Martínez, J., Olmedo,
E., Oliva, R., and Martín-Neira, M.: Triple Collocation Analysis for Two
Error-Correlated Datasets: Application to L-Band Brightness Temperatures over
Land, Remote Sens., 12, 3381–3422, https://doi.org/10.3390/rs12203381, 2020. a, b
Guimbard, S., Gourrion, J., Portabella, P., Turiel, A., Gabarró, C., and Font,
J.: SMOS Semi-Empirical Ocean Forward Model Adjustement, IEEE
Trans. Geosci. Remote Sens., 50, 1676–1687, 2012. a
Hoareau, N., Portabella, M., Lin, W., Ballabrera-Poy, J., and Turiel, A.: Error
Characterization of Sea Surface Salinity Products Using Triple Collocation
Analysis, IEEE T. Geosci. Remote, 56, 5160–5168,
https://doi.org/10.1109/TGRS.2018.2810442, 2018. a, b
ICM-CSIC, LOCEAN/SA/CETP, and IFREMER: SMOS SSS L2 Algorithm Theoretical
Baseline Document SO-L2-SSS-ACR-007,
available at: https://smos.argans.co.uk/docs/deliverables/delivered/ATBD/SO-TN-ARG-GS-0007_L2OS-ATBD_v3.13_160429.pdf (last access: 25 January 2022),
2016. a
Kerr, Y., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J.,
Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S., Drinkwater,
M., Hahne, A., Martin-Neira, M., and Mecklenburg, S.: The SMOS mission: new
tool for monitoring key elements of the global water cycle, Proc. IEEE, 98, 666–687, 2010. a
Klein, L. and Swift, C.: An improved model for the dielectric constant of sea
water at microwave frequencies, IEEE J. Ocean. Eng., 2,
104–111, https://doi.org/10.1109/JOE.1977.1145319, 1977. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a, b
Manabe, S., S. R.: Simulation of abrupt climate change induced by freshwater
input to the North Atlantic Ocean., Nature, 378, 165–167, 1995. a
Martínez, J., Gabarró, C., and Turiel, A.: Arctic Sea Surface Salinity L2 orbits and L3 maps (V.3.1), DIGITAL.CSIC [data set], https://doi.org/10.20350/digitalCSIC/12620, 2019. a, b, c, d
Martínez, J., Gabarró, C., and Turiel, A.: Algorithm Theoretical Basis
Document, Arctic+Salinity ITT, Tech. rep., BEC, Institut de Ciencies del
Mar-CSIC, https://doi.org/10.13140/RG.2.2.12195.58401, 2020. a, b, c, d
Matos, P., Gutiérrez, A., and Moreira, F.: SMOS L1 Processor Discrete Global
Grids Document SMOS-DMS-TN-5200, DEIMOS, version 1.4, 2004. a
Mecklenburg, S., Wright, N., Bouzina, C., and Delwart, S.: Getting down to
business – SMOS operations and products, ESA Bulletin, 137, 25–30, 2009. a
Meissner, T. and Wentz, F. J.: The complex dielectric constant of pure and sea
water from microwave satellite observations, IEEE T. Geosci. Remote, 42, 1836–1849, https://doi.org/10.1109/TGRS.2004.831888, 2004. a, b
Meissner, T. and Wentz, F. J.: The Emissivity of the Ocean Surface Between 6
and 90 GHz Over a Large Range of Wind Speeds and Earth Incidence Angles, IEEE
T. Geosci. Remote, 50, 3004–3026,
https://doi.org/10.1109/TGRS.2011.2179662, 2012. a, b
Oliva, R., Daganzo, E., Richaume, P., Kerr, Y., Cabot, F., Soldo, Y.,
Anterrieu, E., Reul, N., Gutierrez, A., Barbosa, J., and Lopes, G.: Status of
Radio Frequency Interference (RFI) in the 1400–1427 MHz passive band based
on six years of SMOS mission, Remote Sens. Environ., 180, 64–75,
https://doi.org/10.1016/j.rse.2016.01.013, 2016. a
Olmedo, E., Martínez, J., Umbert, M., Hoareau, N., Portabella, M.,
Ballabrera-Poy, J., and Turiel, A.: Improving time and space resolution of
SMOS salinity maps using multifractal fusion, Remote Sens.
Environ., 180, 246–263, https://doi.org/10.1016/j.rse.2016.02.038, 2016. a
Olmedo, E., Martínez, J., Turiel, A., Ballabrera-Poy, J., and Portabella,
M.: Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface
Salinity, Remote Sens. Environ., 193, 103–126,
https://doi.org/10.1016/j.rse.2017.02.023, 2017. a
Olmedo, E., Gabarró, C., González-Gambau, V., Martínez, J.,
Ballabrera-Poy, J., Turiel, A., Portabella, M., Fournier, S., and Lee, T.:
Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in
Arctic and Sub-Arctic Regions, Remote Sens., 10, 1772–1796, https://doi.org/10.3390/rs10111772,
2018. a, b
Olmedo, E., González-Haro, C., Hoareau, N., Umbert, M., González-Gambau, V., Martínez, J., Gabarró, C., and Turiel, A.: Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center, Earth Syst. Sci. Data, 13, 857–888, https://doi.org/10.5194/essd-13-857-2021, 2021. a, b, c
Press, W. P., Teukolsky, S., Vetterling, W., and Flannery, B.:
Numerical recipes in C (2nd ed.): the art of scientific computing, December 1992, Cambridge University Press, 40 W. 20 St., New York, NY, United States
994 pp.,
ISBN 978-0-521-43108-8, 1992. a
Reul, N., Tenerelli, J., Chapron, B., and Waldteufel, P.: Modeling Sun
glitter at L-band for sea surface salinity remote sensing with SMOS, IEEE
T. Geosci. Remote., 45, 2073–2087, 2007. a
Reul, N., Grodsky, S., Arias, M., Boutin, J., Catany, R., Chapron, B., D'Amico,
F., Dinnat, E., Donlon, C., Fore, A., Fournier, S., Guimbard, S., Hasson, A.,
Kolodziejczyk, N., Lagerloef, G., Lee, T., Le Vine, D., Lindstromn, E., Maes,
C., Mecklenburg, S., Meissner, T., Olmedo, E., Sabia, R., Tenerelli, J.,
Thouvenin-Masson, C., Turiel, A., Vergely, J., Vinogradova, N., Wentz, F.,
and Yueh, S.: Sea surface salinity estimates from spaceborne L-band
radiometers: An overview of the first decade of observation (2010–2019),
Remote Sens. Environ., 242, 111769, https://doi.org/10.1016/j.rse.2020.111769, 2020. a
Reverdin, G., Le Goff, H., Tara Oceans Consortium, C., and Tara Oceans
Expedition, P.: Properties of seawater from a Sea-Bird TSG temperature and
conductivity sensor mounted on the continuous surface water sampling system
during campaign TARA_20090913Z of the Tara Oceans expedition 2009–2013, PANGAEA,
https://doi.org/10.1594/PANGAEA.836924, 2014. a
Sabater, J. and De Rosnay, P.: Milestone 2 Tech Note – Parts 1/2/3: Operational
Pre-processing chain, Collocation software development and Offline monitoring
suite, Tech. rep., ECMWF, 2010. a
Snyder, J.: Map Projections, A Working Manual, U.S. Geological Survey
Professional Paper 1395, U.S. Government Printing Office, Washington D.C.,
1987. a
Stoffelen: Toward the true near-surface wind speed: Error modeling and
calibration using triple collocation, J. Geophys. Res.-Oceans, 103, 7755–7766, https://doi.org/10.1029/97JC03180, 1998. a, b
Tang, W., Fore, A., Yueh, S., Lee, T., Hayashi, A., Sanchez-Franks, A.,
Martinez, J., King, B., and Baranowski, D.: Validating SMAP SSS with in situ
measurements, Remote Sens. Environ., 200, 326–340,
https://doi.org/10.1016/j.rse.2017.08.021, 2017. a, b
Tang, W., Yueh, S. H., Fore, A. G., and Hayashi, A. K.: An Empirical Sea Ice
Correction Algorithm for SMAP SSS Retrieval in the Arctic Ocean, in: IGARSS
2020–2020 IEEE International Geoscience and Remote Sensing Symposium,
5639–5642, https://doi.org/10.1109/IGARSS39084.2020.9323740, 2020. a
Tenerelli, J. E., Reul, N., Mouche, A. A., and Chapron, B.: Earth Viewing
L Band Radiometer Sensing of Sea Surface Scattered Celestial
Sky Radiation-Part I: General Characteristics, Geoscience and
Remote Sensing, IEEE Transactions on, 46, 659–674, 2008. a
West, S. G., Finch, J. F., and Curran, P. J.: Structural equation models with nonnormal
variables: Problems and remedies, in: Hoyle, R. H., Structural equation modeling:
Concepts, issues, and applications, Sage Publications, Inc., 56–75, 1995. a
Yueh, S., West, R., Wilson, W., Li, F., Nghiem, S., and Rahmat-Samii, Y.:
Error Sources and Feasibility for Microwave Remote Sensing of Ocean Surface
Salinity, IEEE T. Geosci. Remote, 39,
1049–1059, 2001. a
Zhou, Y., Lang, R. H., Dinnat, E. P., and Vine, D. M. L.: L-Band Model Function
of the Dielectric Constant of Seawater, IEEE T. Geosci.
Remote, 55, 6964–6974, https://doi.org/10.1109/TGRS.2017.2737419, 2017. a
Zine, S., Boutin, J., Font, J., Reul, N., Waldteufel, P., Gabarro,
C., Tenerelli, J., Petitcolin, F., Vergely, J., Talone, M., and
Delwart, S.: Overview of the SMOS Sea Surface Salinity Prototype Processor,
IEEE T. Geosci. Remote, 46, 621–645,
https://doi.org/10.1109/TGRS.2008.915543, 2008. a, b
Zweng, M. M., Reagan, J. R., Antonov, J. I., Locarnini, R. A., Mishonov, A. V.,
Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D., and
Biddle, M. M.: World Ocean Atlas 2013, volume 2, Salinity, Levitus, A.
Mishonov Technical Ed., NOAA Atlas NESDIS 74, 39 pp., 2013. a
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Short summary
Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
Measuring salinity from space is challenging since the sensitivity of the brightness temperature...
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