Article Type:
Research Article

The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

Sabine Philipps CLS Space Oceanography Division, Ramonville St Agne, France

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Christine Boone CLS Space Oceanography Division, Ramonville St Agne, France

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Estelle Obligis CLS Space Oceanography Division, Ramonville St Agne, France

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Print Publication:
01 Feb 2007
DOI:
https://doi.org/10.1175/JTECH1954.1
Page(s):
255–269
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Abstract

Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

Corresponding author address: Sabine Philipps, CLS Space Oceanography Division, 8-10 rue Hermes, 31520 Ramonville St Agne, France. Email: philipps@cls.fr

Abstract

Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

Corresponding author address: Sabine Philipps, CLS Space Oceanography Division, 8-10 rue Hermes, 31520 Ramonville St Agne, France. Email: philipps@cls.fr

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Journal of Atmospheric and Oceanic Technology

  • Antonov, J. I., Levitus S. , and Boyer T. P. , 2002: Steric sea level variations during 1957–1994: Importance of salinity. J. Geophys. Res., 107 .8013, doi:10.1029/2001JC000964.

    • Search Google Scholar
    • Export Citation

  • Barnier, B., and Coauthors, 2002: Scientific requirements and impact of space observation of ocean salinity for modeling and climate studies—Final report. ESA/ESTEC Contract 14273/00/NL/DC, 272 pp.

  • Bingham, F. M., Howden S. D. , and Koblinsky C. J. , 2002: Sea surface salinity measurements in the historical database. J. Geophys. Res., 107 .8019, doi:10.1029/2000JC000767.

    • Search Google Scholar
    • Export Citation

  • Boutin, J., Waldteufel P. , Martin N. , Caudal G. , and Dinnat E. P. , 2004: Surface salinity retrieved from SMOS measurements over the Global Ocean: Imprecisions due to sea surface roughness and temperature uncertainties. J. Atmos. Oceanic Technol., 21 , 1432–1447.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Camps, A., and Coauthors, 2002: Sea surface emissivity observations at L-band: First results of the Wind and Salinity Experiment WISE-2000. IEEE Trans. Geosci. Remote Sens., 40 , 2117–2130.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Camps, A., and Coauthors, 2004: The WISE 2000 and 2001 field experiments in support of the SMOS mission: Sea surface L-band brightness temperature observations and their application to sea surface salinity retrieval. IEEE Trans. Geosci. Remote Sens., 42 , 804–823.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Camps, A., and Coauthors, 2005: The emissivity of foam-covered water surface at L-band: Theoretical modeling and experimental results from the Frog 2003 Field Experiment. IEEE Trans. Geosci. Remote Sens., 43 , 925–937.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • CERSAT-IFREMER, 2002: QuikSCAT scatterometer mean wind field product. User Manual C2-MUT-W-03-IF, 48 pp.

  • Delcroix, T., Henin C. , Porte V. , and Arkin P. , 1996: Precipitation and sea-surface salinity in the tropical Pacific Ocean. Deep-Sea Res., 43 , 1123–1141.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dinnat, E. P., Boutin J. , Caudal G. , Etcheto J. , and Waldteufel P. , 2002: Influence of sea surface emissivity model parameters at L-band for the estimation of salinity. Int. J. Remote Sens., 23 , 5117–5122.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Durand, F., Gourdeau L. , Delcroix T. , and Verron J. , 2002: Assimilation of sea surface salinity in a tropical Oceanic General Circulation Model (OGCM): A twin experiment approach. J. Geophys. Res., 107 .8004, doi:10.1029/2001JC000849.

    • Search Google Scholar
    • Export Citation

  • Durden, S. L., and Vesecky J. F. , 1985: A physical radar cross-section model for a wind-driven sea with swell. IEEE J. Oceanic Eng., OE-10 , 445–451.

    • Search Google Scholar
    • Export Citation

  • Gabarró, C., Font J. , Camps A. , Vall-llossera M. , and Julià A. , 2004: A new empirical model of the sea surface microwave emissivity for salinity remote sensing. Geophys. Res. Lett., 31 .L01309, doi:10.1029/2003GL018964.

    • Search Google Scholar
    • Export Citation

  • Global Ocean Data Assimilation Experiment, 2001: Strategic plan. GODAE Rep. 6., 23 pp.

  • Greiner, E., 2001: Biais de la réanalyse PSY1-v1 AOO1 de 1993–98 (Bias of reanalysis PSY1-v1 AOO1 from 1993 to 98). MERCATOR Newsletter, No. 2, 2–20. [Available online at http://www.mercator-ocean.fr/documents/lettre/lettre_2.pdf.].

  • Hansen, V. D., and Thacker W. C. , 1999: Estimation of salinity profiles in the upper ocean. J. Geophys. Res., 104 , 7921–7933.

  • Johnson, G., McPhadden M. , Rowe G. D. , and McTaggart K. E. , 2000: Upper equatorial Pacific Ocean current and salinity variability during the 1996-1998 El Niño-La Niña cycle. J. Geophys. Res., 105 , 1037–1053.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kerr, Y., 1998: The SMOS Mission: MIRAS on RAMSES: A proposal to the call for Earth Explorer Opportunity Mission. CESBIO, Toulouse, France, 76 pp.

  • Klein, L. A., and Swift C. T. , 1977: An improved model for the dielectric constant of sea water at microwave frequencies. IEEE Trans. Antennas Propag., AP-25 , 104–111.

    • Search Google Scholar
    • Export Citation

  • Lukas, R., and Lindstrom E. , 1991: The mixed layer of the western equatorial Pacific Ocean. J. Geophys. Res., 96 , 3343–3357.

  • Reul, N., and Chapron B. , 2002: Effects of foam on the emissivity of the sea surface at L band. Rep. WP1300, Final Report of ESA Contract 14273/00/NL/DC “Scientific Requirements and Impact of Space Observation of Ocean Salinity for Modeling and Climate Studies,” 42 pp.

  • Silvestrin, P., Berger M. , Kerr Y. H. , and Font J. , 2001: ESA’s Second Earth Explorer Opportunity Mission: The Soil Moisture and Ocean Salinity Mission—SMOS. IEEE Geoscience and Remote Sensing Society Newsletter, No. 118, 11–14.

  • Skou, N., 2003: Faraday rotation and L-band oceanographic measurements. Radio Sci., 38 .8059, doi:10.1029/2002RS002671.

  • Waldteufel, P., and Caudal G. , 2002: About off-axis radiometric polarimetric measurements. IEEE Trans. Geosci. Remote Sens., 40 , 1435–1439.

  • Waldteufel, P., Boutin J. , and Kerr Y. , 2003: Selecting an optimal configuration for the Soil Moisture and Ocean Salinity mission. Radio Sci., 38 .8051, doi:10.1029/2002RS002744.

    • Search Google Scholar
    • Export Citation

  • Yueh, S. H., 1997: Modeling of wind direction signals in polarimetric sea surface brightness temperatures. IEEE Trans. Geosci. Remote Sens., 35 , 1400–1418.

    • Crossref
    • Search Google Scholar
    • Export Citation
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