Articles | Volume 13, issue 2
https://doi.org/10.5194/essd-13-857-2021
© Author(s) 2021. 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-13-857-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center
Estrella Olmedo
CORRESPONDING AUTHOR
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
Nina Hoareau
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
Verónica González-Gambau
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37-49, 08003 Barcelona, Spain
Justino Martínez
Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC,
P. Marítim de la Barceloneta, 37-49, 08003 Barcelona, Spain
Carolina Gabarró
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
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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.
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We used satellite observations to study how density changes modify the ocean surface in the North Atlantic, especially in areas important for deep ocean currents that affect climate. We found that freshwater plays a bigger role than expected in disrupting ocean circulation. By tracking these changes from space over time, our research helps scientists better understand climate risks and improve future climate predictions.
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Revised manuscript accepted for ESSD
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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.
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
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Short summary
Short summary
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.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
Short summary
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.
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
Short summary
Short summary
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.
Cited articles
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo
GDAC), SEANOE, https://doi.org/10.17882/42182, 2000. a
Barcelona Expert Center: FTP service, available at: http://bec.icm.csic.es/bec-ftp-service/ (last access: 1 March 2021), 2007. a
Blumen, W.: Uniform potential vorticity flow: Part I. Theory of wave
interactions and two-dimensional turbulence, J. Atmos. Sci.,
35, 774–783, https://doi.org/10.1175/1520-0469(1978)035<0774:UPVFPI>2.0.CO;2, 1978. a
Boutin, J., Martin, N., Kolodziejczyk, N., and Reverdin, G.: Interannual
anomalies of SMOS sea surface salinity, Remote Sens. Environ., 180, 128–136, https://doi.org/10.1016/j.rse.2016.02.053, 2016. a
Boutin, J., Vergely, J., Marchand, S., D'Amico, F., Hasson, A., Kolodziejczyk, N., Reul, N., Reverdin, G., and Vialard, J.: New SMOS Sea Surface Salinity with reduced systematic errors and improved variability, Remote Sens. Environ., 214, 115–134, https://doi.org/10.1016/j.rse.2018.05.022, 2018. a
Castellanos, P., Olmedo, E., Pelegrí, J., Turiel, A., and Campos, E. J. D.:
Seasonal Variability of Retroflection Structures and Transports in the
Atlantic Ocean as Inferred from Satellite-Derived Salinity Maps, Remote
Sens., 11, 802, https://doi.org/10.3390/rs11070802, 2019. a
Charney, J.: Geostrophic turbulence, J. Atmos. Sci., 28,
1087–1095, https://doi.org/10.1175/1520-0469(1971)028<1087:GT>2.0.CO;2, 1971. a
CMEMS Lambda project: available at: http://www.cmems-lambda.eu/mapviewer/, last access: 1 March 2021. a
Deimos: SMOS L1 Processor L1C Data Prorocessing Model, SO-DS-DME-L1PP-0009,
Deimos, version 2.14, 2014. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ, 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Droghei, R., Nardelli, B. B., and Santoleri, R.: Combining In Situ and
Satellite Observations to Retrieve Salinity and Density at the Ocean Surface,
J. Atmos. Ocean. Tech., 33, 1211–1223,
https://doi.org/10.1175/JTECH-D-15-0194.1, 2016. a
EMODnet: available at: https://www.emodnet-physics.eu/map/Products/Smos/, last access: 1 March 2021. a
Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP)
Mission, P. IEEE, 98, 704–716,
https://doi.org/10.1109/JPROC.2010.2043918, 2010. a
ESA: Earth Observation CFI v3.X branch, available at:
http://eop-cfi.esa.int/index.php/mission-cfi-software/eocfi-software/branch-3-x
(last access: 23 August 2016), 2014. a
Font, J., Camps, A., Borges, A., Martin-Neira, M., Boutin, J., Reul, N., Kerr,
Y., Hahne, A., and Mecklenburg, S.: SMOS: the challenging sea surface
salinity measurement from space, P. IEEE, 98, 649,
https://doi.org/10.1109/JPROC.2009.2033096, 2010. a
Fore, A., Yueh, S. H., Tang, W., Stiles, B. W., and Hayashi, A. K.: Combined
Active/Passive Retrievals of Ocean Vector Wind and Sea Surface Salinity With SMAP, IEEE T. Geosci. Remote, 54, 7396–7404,
https://doi.org/10.1109/TGRS.2016.2601486, 2016. 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 temperature over land, Remote Sens., 12, 3381, https://doi.org/10.3390/rs12203381, 2020. a, b
Gruber, A., Su, C., Crow, W. T., Zwieback, Z., Dorigo, W. A., and Wagner, W.:
Estimating error cross-correlations in soil moisture data sets using extended collocation analysis, J. Geophys. Res.-Atmos., 121,
1208–1219, https://doi.org/10.1002/2015JD024027, 2016. a
Guimbard, S., Gourrion, J., Portabella, P., Turiel, A., Gabarró, C., and
Font, J.: SMOS Semi-Empirical Ocean Forward Model Adjustement, IEEE T. Geosci. Remote, 50, 1676–1687,
https://doi.org/10.1109/TGRS.2012.2188410, 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, 2018a. a
Hoareau, N., Turiel, A., Portabella, M., Ballabrera, J., and Vogelzang, J.:
Singularity Power Spectra: A method to assess geophysical consistency of
gridded products – Aplication to sea surface salinity remote sensing maps,
IEEE T. Geosci. Remote, 56, 5525–5535,
https://doi.org/10.1109/TGRS.2018.2819240, 2018b. a, b, c, d, e, f, g, h, i, j
Isern-Fontanet, J., Turiel, A., García-Ladona, E., and Font, J.:
Microcanonical multifractal formalism: Application to the estimation of ocean surface velocities, J. Geophys. Res.-Oceans, 112, 2156–2202,
https://doi.org/10.1029/2006JC003878, 2007. 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, P.
IEEE, 98, 666–687, https://doi.org/10.1109/JPROC.2010.2043032, 2010. a
Kerr, Y., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B.,
Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart, S.,
Al Bitar, A., Pellarin, T., Bindlish, R., Jackson, T., Rüdiger, C.,
Waldteufel, P., Mecklenburg, S., and Wigneron, J.: Overview of SMOS
performance in terms of global soil moisture monitoring after six years in
operation, Remote Sens. Environ., 180, 40–63,
https://doi.org/10.1016/j.rse.2016.02.042, 2016. a
Klein, L. A. and Swift, C. T.: An improved model for the dialectric constant of sea water at microwave frequencies, IEEE T. Antenn. Propag.,
25, 104–111, 1977. a
Kolodziejczyk, N., Reverdin, G., Boutin, J., and Hernandez, O.: Observation of the surface horizontal thermohaline variability at mesoscale to submesoscale in the north-eastern subtropical Atlantic Ocean, J. Geophys.
Res., 120, 2588–2600, https://doi.org/10.1002/2014JC010455, 2015. a
Kolodziejczyk, N., Boutin, J., Vergely, J., Marchand, S., Martin, N., and
Reverdin, G.: Mitigation of systematic errors in SMOS sea surface
salinity, Remote Sens. Environ., 180, 164–177,
https://doi.org/10.1016/j.rse.2016.02.061, 2016. a
Martin, M., Hines, A., and Bell, M.: Data assimilation in the FOAM operational short-range ocean forecasting system: a description of the scheme and its impact, Q. J. Roy. Meteor. Soc., 133, 981–995, https://doi.org/10.1002/qj.74, 2007. a
Martín-Neira, M., Oliva, R., Corbella, I., Torres, F., Duffo, N., Duran, I.,
Kainulainen, J., Closa, A., Zurita, A., Cabot, F., Khazaal, A., Anterrieau,
E., Barbosa, J., Lopes, G., Tenerelli, J., Diez-Garcia, R., Fauste, J.,
Martin-Porqueras, F., González-Gambau, V., Turiel, A., Delwart, S.,
Crapolicchio, R., and Suess, M.: SMOS Instrument performance and calibration
after six years in orbit, Remote Sens. Environ., 180, 19–39,
https://doi.org/10.1016/j.rse.2016.02.036, 2016. a
McMullan, K. D., Brown, M., Martin-Neira, M., Rits, W., Ekholm, S., Marti, J., and Lemanczyk, J.: SMOS: The Payload,
IEEE T. Geosci. Remote, 46, 594–605, https://doi.org/10.1109/TGRS.2007.914809, 2008. a
Mecklenburg, S., Wright, N., Bouzina, C., and Delwart, S.: Getting down to
business – SMOS operations and products., ESA Bull.-Eur. Space, 137, 25–30, 2009. a
Meissner, T., Wentz, F., and Le Vine, D.: The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases, Remote Sens., 10, 1121, https://doi.org/10.3390/rs10071121, 2018. a
Nardelli, B. B.: A Novel Approach for the High-Resolution Interpolation of In
Situ Sea Surface Salinity, J. Atmos. Ocean. Tech.,
29, 867–879, https://doi.org/10.1175/JTECH-D-11-00099.1, 2012. a
Nardelli, B. B., Droghei, R., and R., S.: Multi-dimensional interpolation of
SMOS sea surface salinity with surface temperature and in situ salinity
data, Remote Sens. Environ., 180, 392–402,
https://doi.org/10.1016/j.rse.2015.12.052, 2016. a
Nieves, V., Llebot, C., Turiel, A., Solé, J., García-Ladona, E.,
Estrada, M., and Blasco, D.: Common turbulent signature in sea surface
temperature and chlorophyll maps, Geophys. Res. Lett., 34,
L23602, https://doi.org/10.1029/2007GL030823, 2007. a, b
Olmedo, E., González-Gambau, V., Turiel, A., Martínez, J., Gabarró,
C., Portabella, M., Ballabrera-Poy, J., Arias, M., Sabia, R., and Oliva, R.:
Empirical Characterization of the SMOS Brightness Temperature Bias and
Uncertainty for Improving the Sea Surface Salinity Retrieval, IEEE J.
Sel. Top. Appl., 12, 2486–2503, https://doi.org/10.1109/JSTARS.2019.2904947, 2019a. a
Olmedo, E., González-Gambau, V., Martínez, J., González-Haro, C., Turiel,
A., Portabella, M., Arias, M., Sabia, R., Oliva, R., and Corbella, I.:
Characterization and Correction of the Latitudinal and Seasonal Bias in BEC SMOS Sea Surface Salinity Maps, IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, 7932–7935,
https://doi.org/10.1109/IGARSS.2019.8900562, 2019b. a, b
Olmedo, E., González-Haro, C., González-Gambau, V., and Turiel, A.: BEC SMOS
Sea Surface Salinity global L3 product (V.2.0), Digital CSIC,
https://doi.org/10.20350/digitalCSIC/12601, 2020a. a, b
Olmedo, E., González-Haro, C., González-Gambau, V., and Turiel, A.: BEC SMOS
Sea Surface Salinity global L4 product (V.2.0), Digital CSIC,
https://doi.org/10.20350/digitalCSIC/12600, 2020b. a, b
Pierdicca, N., Fascetti, F., Pulvirenti, L., and Crapolicchio, R.: Error
characterization of soil moisture satellite products: Retrieving error
cross- correlation through extended quadruple collocation, IEEE J.
Sel. Top. Appl., 10, 4522–4530, https://doi.org/10.1109/JSTARS.2017.2714025, 2017. a
Pont, O., Turiel, A., and Yahia, H.: Singularity analysis of digital signals
through the evaluation of their unpredictable point manifold, Int.
J. Comput. Math., 90, 1693–1707,
https://doi.org/10.1080/00207160.2012.748895, 2013. 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, https://doi.org/10.1109/TGRS.2006.890421,
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
Reynolds, R. and Chelton, D.: Comparison of daily sea surface temperature
analyses for 2007-2008, J. Climate, 23, 3545–3562,
https://doi.org/10.1175/2010JCLI3294.1, 2010. 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,
available at: http://www.ecmwf.int/en/elibrary/11316-milestone-2-tech-note-parts-1/2/3-operational-pre-processing-chain-collocation (last access: 1 March 2021),
2010. a
Stammer, D.: On eddy characteristics, eddy transports, and mean flow
properties, J. Phys. Oceanogr., 28, 727–739,
https://doi.org/10.1175/1520-0485(1998)028<0727:OECETA>2.0.CO;2, 1998. a
Tenerelli, J. and Reul, N.: Analysis of L1PP calibration approach impacts in SMOS Tbs and 3-days SSS
retrievals over the Pacific using an alternative Ocean Target Transformation applied to L1OP data.
Technical note ESL IFREMER/CLS, 16 pp. 2010. 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, IEEE T. Geosci. Remote,, 46, 659–674, https://doi.org/10.1109/TGRS.2007.914803, 2008. a
Turiel, A., Isern-Fontanet, J., Garcia-Ladona, E., and Font, J.: Multifractal
Method for the Instantaneous Evaluation of the Stream Function in Geophysical Flows, Phys. Rev. Lett., 95, 104502, https://doi.org/10.1103/PhysRevLett.95.104502, 2005. a
Turiel, A., Pérez-Vicente, C. J., and Grazzini, J.: Numerical methods for
the estimation of multifractal singularity spectra on sampled data: a
comparative study, J. Comput. Phys., 216, 362–390,
https://doi.org/10.1016/j.jcp.2005.12.004, 2006. a
Turiel, A., Solé, J., Nieves, V., Ballabrera-Poy, J., and García-Ladona, E.:
Tracking oceanic currents by singularity analysis of Microwave Sea Surface
Temperature images, Remote Sens. Environ., 112, 2246–2260,
https://doi.org/10.1016/j.rse.2007.10.007, 2008a. a, b
Turiel, A., Yahia, H., and Pérez-Vicente, C. J.: Microcanonical multifractal
formalism-a geometrical approach to multifractal systems: Part I. Singularity analysis, J. Phys. A-Math. Theo., 41, 015501,
https://doi.org/10.1088/1751-8113/41/1/015501, 2008b. a, b
Umbert, M., Guimbard, S., Ballabrera-Poy, J., and Turiel, A.: Synergy between
Ocean Variables: Remotely Sensed Surface Temperature and Chlorophyll
Concentration Coherence, Remote Sens., 12, 1153, https://doi.org/10.3390/rs12071153,
2020. a
Zine, S., Boutin, J., Waldteufel, P., Vergely, J., Pellarin, T., and Lazure,
P.: Issues About Retrieving Sea Surface Salinity in Coastal
Areas From SMOS Data, IEEE T. Geosci. Remote, 45,
2061–2072, https://doi.org/10.1109/TGRS.2007.894934, 2007.
a
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, NOAA Atlas NESDIS 74, National Oceanic and Atmospheric Administration, Silver Spring, MD, 39 pp., 2013. a
Short summary
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.
After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission...
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