• Athié, G., , and Marin F. , 2008: Cross-equatorial structure and temporal modulation of intra-seasonal variability at the surface of the Tropical Atlantic Ocean. J. Geophys. Res., 113, C08020, doi:10.1029/2007JC004332.

    • Search Google Scholar
    • Export Citation
  • Athié, G., , Marin F. , , Treguierd A. , , Bourlès B. , , and Guiavarch C. , 2009: Sensitivity of near-surface tropical instability waves to submonthly wind forcing in the tropical Atlantic. Ocean Model., 30, 241255.

    • Search Google Scholar
    • Export Citation
  • Boyer, T. P., , and Levitus S. , 2002: Harmonic analysis of climatological sea surface salinity. J. Geophys. Res., 107, 8006, doi:10.1029/2001JC000829.

    • Search Google Scholar
    • Export Citation
  • Chassignet, E. P., and Coauthors, 2009: U.S. GODAE: Global Ocean Prediction with the Hybrid Coordinate Ocean Model (HYCOM). Oceanography, 22, 6475.

    • Search Google Scholar
    • Export Citation
  • Cronin, M. F., , and McPhaden M. J. , 1999: Diurnal cycle of rainfall and surface salinity in the western Pacific warm pool. Geophys. Res. Lett., 26, 34653468.

    • Search Google Scholar
    • Export Citation
  • Cummings, J. A., 2005: Operational multivariate ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131C, 35833604.

  • Delcroix, T., , and Henin C. , 1991: Seasonal and interannual variations of sea-surface salinity in the tropical Pacific Ocean. J. Geophys. Res., 96, 22 13522 150.

    • Search Google Scholar
    • Export Citation
  • Delcroix, T., , and McPhaden M. J. , 2002: Interannual sea surface salinity and temperature changes in the western Pacific warm pool during 1992–2000. J. Geophys. Res., 107, 8002, doi:10.1029/2001JC000862.

    • Search Google Scholar
    • Export Citation
  • Delcroix, T., , McPhaden M. J. , , Dessier A. , , and Gouriou Y. , 2005: Time and space scales for sea surface salinity in the tropical oceans. Deep-Sea Res. I, 52, 787813.

    • Search Google Scholar
    • Export Citation
  • Eldin, G., , Rodier M. , , and Radenac M. H. , 1997: Physical and nutrient variability in the upper equatorial Pacific associated with westerly wind forcing and wave activity in October 1994. Deep-Sea Res. I, 44, 17831800.

    • Search Google Scholar
    • Export Citation
  • Font, J., and Coauthors, 2010: SMOS: The challenging sea surface salinity measurement from space. Proc. IEEE, 98, 649665.

  • Fox, D. N., , Teague W. J. , , Barron C. N. , , Carnes M. R. , , and Lee C. M. , 2002: The Modular Ocean Data Assimilation System (MODAS). J. Atmos. Oceanic Technol., 19, 240252.

    • Search Google Scholar
    • Export Citation
  • Freitag, H. P., , McCarty M. E. , , Nosse C. , , Lukas R. , , McPhaden M. J. , , and Cronin M. F. , 1999: COARE Seacat data: Calibrations and quality control procedures. NOAA Tech. Memo. ERL PMEL-115, 89 pp.

  • Johnson, E.,, Lagerloef, G. , Gunn, and J. , Bonjean F. , 2002: Surface salinity advection in the tropical oceans compared with atmospheric forcing: a trial balance. J. Geophys. Res., 107, doi:10.1029/2001JC001122.

    • Search Google Scholar
    • Export Citation
  • Lagerloef, G.,, and Delcroix T. , 2001: Sea surface salinity: A regional case study for the tropical Pacific. Observing the Ocean in the 21st Century, Australian Bureau of Meteorology, 137–148.

    • Search Google Scholar
    • Export Citation
  • Lagerloef, G., , Swift C. T. , , and Le Vine D. M. , 1995: Sea surface salinity: The next remote sensing challenge. J. Oceanogr., 8, 4450.

  • Lagerloef, G., and Coauthors, 2008: The Aquarius/SAC-D mission: Designed to meet the salinity remove-sensing challenge. J. Oceanogr., 21, 6881.

    • Search Google Scholar
    • Export Citation
  • Le Vine, D. M., , and Abraham S. , 2004: Galactic noise and passive microwave remote sensing from space at L-band. IEEE Trans. Geosci. Remote Sens., 42, 119129.

    • Search Google Scholar
    • Export Citation
  • Le Vine, D. M., , Abraham S. , , Wentz F. , , and Lagerloef G. S. E. , 2005: Impact of the sun on remote sensing of sea surface salinity from space. Proc. IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Vol. I, Seoul, South Korea, IEEE, 288–291, doi:10.1109/IGARSS.2005.1526164.

  • Le Vine, D. M., , Lagerloef G. S. E. , , Colomb F. R. , , Yeh S. H. , , and Pellerano F. A. , 2007: Aquarius: An instrument to monitor sea surface salinity from space. IEEE Trans. Geosci. Remote Sens., 45, 20402050.

    • Search Google Scholar
    • Export Citation
  • Lukas, R., , and Lindstrom E. , 1991: The mixed layer of the western equatorial Pacific Ocean. J. Geophys. Res., 96, 33433357.

  • McPhaden, M. J., 1995: The TAO array is completed. Bull. Amer. Meteor. Soc., 76, 739741.

  • McPhaden, M. J.,, and Coauthors, 2009: RAMA: The Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction. Bull. Amer. Meteor. Soc., 90, 459480.

    • Search Google Scholar
    • Export Citation
  • Picaut, J., , Ioualalen M. , , Delcroix T. , , Masia F. , , Murtugudde R. , , and Vialard J. , 2001: The oceanic zone of convergence on the eastern edge of the Pacific warm pool: A synthesis of results and implications for ENSO and biogeochemical phenomena. J. Geophys. Res., 106, 23632386.

    • Search Google Scholar
    • Export Citation
  • Ponte, R. M., , and Lyard F. , 2002: Effects of unresolved high-frequency signals in altimeter records inferred from tide gauge data. J. Atmos. Oceanic Technol., 18, 534539.

    • Search Google Scholar
    • Export Citation
  • Ray, R. D., 1998: Spectral analysis of highly aliased sea-level signals. Geophys. Res. Lett., 103, 24 99125 003.

  • Riser, S. C., , Ren L. , , and Wong A. , 2008: Salinity in ARGO. Oceanography, 21, 5667.

  • Servain, J., , Busalacchi A. , , McPhaden M. , , Moura A. , , Reverdin G. , , Vienna M. , , and Zebiak S. , 1998: A Pilot Research Moored Array in the Tropical Atlantic (PIRATA). Bull. Amer. Meteor. Soc., 79, 20192031.

    • Search Google Scholar
    • Export Citation
  • Schlax, M. G., , and Chelton D. B. , 1994: Aliased tidal errors in TOPEX/Poseidon sea surface height data. J. Geophys. Res., 99, 24 76124 775.

    • Search Google Scholar
    • Export Citation
  • Sharma, R., , Agarwal N. , , Momin I. M. , , Basu S. , , and Agarwal V. K. , 2010: Simulated sea surface salinity variability in the tropical Indian Ocean. J. Climate, 23, 65426554.

    • Search Google Scholar
    • Export Citation
  • Wunsch, C., , and Stammer D. , 1995: The global frequency–wavenumber spectrum of oceanic variability estimated from TOPEX/POSEIDON altimetric measurements. J. Geophys. Res., 100, 24 89524 910.

    • Search Google Scholar
    • Export Citation
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Assessing Temporal Aliasing in Satellite-Based Surface Salinity Measurements

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  • 1 Atmospheric and Environmental Research, Inc., Lexington, Massachusetts
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Abstract

The Aquarius/Satelite de Aplicaciones Cientificas-D (SAC-D) salinity remote sensing mission is intended to provide global mapping of sea surface salinity (SSS) fields over the next few years. Temporal and spatial averages of the satellite salinity retrievals produce monthly mean fields on 1° grids with target accuracies of 0.2 psu. One issue of relevance for the satellite-derived products is the potential for temporal aliasing of rapid fluctuations into the climate (monthly averaged) values of interest. Global daily SSS fields from a data-assimilating, eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) solution are used to evaluate whether the potential aliasing error is large enough to affect the accuracy of the SSS retrievals. For comparison, salinity data collected at a few in situ stations over the tropical oceans are also used. Based on the HYCOM daily series, over many oceanic regions, a significant part of the total salinity variability is contributed by rapid fluctuations at periods aliased in the satellite retrievals. Estimates of the implicit aliasing error in monthly mean salinity estimates amount to 0.02 psu on average and >0.1 psu in some coastal, tropical, western boundary current, and Arctic regions. Comparison with in situ measurements suggests that HYCOM can underestimate the effect at some locations. While local aliased variance can be significant, the estimated impact of aliasing noise on the overall Aquarius system noise is negligible on average, when combined with effects of other instrument and geophysical errors. Effects of aliased variance are strongest at the shortest periods (<6 months) and become negligible at the annual period.

Corresponding author address: Nadya Vinogradova, AER Inc., 131 Hartwell Ave., Lexington, MA 02421. E-mail: nadya@aer.com

Abstract

The Aquarius/Satelite de Aplicaciones Cientificas-D (SAC-D) salinity remote sensing mission is intended to provide global mapping of sea surface salinity (SSS) fields over the next few years. Temporal and spatial averages of the satellite salinity retrievals produce monthly mean fields on 1° grids with target accuracies of 0.2 psu. One issue of relevance for the satellite-derived products is the potential for temporal aliasing of rapid fluctuations into the climate (monthly averaged) values of interest. Global daily SSS fields from a data-assimilating, eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) solution are used to evaluate whether the potential aliasing error is large enough to affect the accuracy of the SSS retrievals. For comparison, salinity data collected at a few in situ stations over the tropical oceans are also used. Based on the HYCOM daily series, over many oceanic regions, a significant part of the total salinity variability is contributed by rapid fluctuations at periods aliased in the satellite retrievals. Estimates of the implicit aliasing error in monthly mean salinity estimates amount to 0.02 psu on average and >0.1 psu in some coastal, tropical, western boundary current, and Arctic regions. Comparison with in situ measurements suggests that HYCOM can underestimate the effect at some locations. While local aliased variance can be significant, the estimated impact of aliasing noise on the overall Aquarius system noise is negligible on average, when combined with effects of other instrument and geophysical errors. Effects of aliased variance are strongest at the shortest periods (<6 months) and become negligible at the annual period.

Corresponding author address: Nadya Vinogradova, AER Inc., 131 Hartwell Ave., Lexington, MA 02421. E-mail: nadya@aer.com
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