• Askari, F., Geernaert G. L. , Keller W. C. , and Raman S. , 1993: Radar imaging of thermal fronts. Int. J. Remote Sens, 14 , 275294.

  • Atlas, R., Bloom S. C. , Hoffman R. N. , Brin E. , Ardizzone J. , Terry J. , Bungato T. D. , and Jusem J. C. , 1999: Geophysical validation of NSCAT winds using atmospheric data and analysis. J. Geophys. Res, 104 , 1140511424.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banner, M. L., and Phillips O. M. , 1974: On the incipient breaking of small scale waves. J. Fluid Mech, 4 , 647656.

  • Banner, M. L., and Melville W. K. , 1976: On the separation of air-flow over water waves. J. Fluid Mech, 77 , 825842.

  • Beal, R. C., Kudryavtsev V. N. , Thompson D. R. , Grodsky S. A. , Tilley D. G. , Dulov V. A. , and Graber H. C. , 1997: The influence of the marine atmospheric boundary layer on ERS 1 synthetic aperture radar imagery of the Gulf Stream. J. Geophys. Res, 102 , 57995814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boulanger, J. P., and Menkes C. , 1999: Long equatorial wave reflection in the Pacific Ocean during the 1992–1998 TOPEX/Poseidon period. Climate Dyn, 15 , 205225.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bourassa, M. A., Freilich M. H. , Legler D. M. , Liu W. T. , and O'Brien J. J. , 1997: Wind observation from new satellite and research vessels agree. Eos, Trans. Amer. Geophys. Union, 78 , 597602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bourassa, M. A., Vincent D. G. , and Wood W. L. , 1999: A flux parameterization including the effects of capillary waves and sea state. J. Atmos. Sci, 56 , 11231139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., Lin H. , and Ma J. , 2000: On the seasonal inconsistency of altimeter wind speed algorithms. Int. J. Remote Sens, 21 , 21192125.

  • Colton, M. C., Plant W. J. , Keller W. C. , and Geenaert G. L. , 1995: Tower-based measurements of normalized radar cross-section from Lake Ontario: Evidence of wind stress dependence. J. Geophys. Res, 100 , 87918813.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donelan, M. A., Dobson F. W. , Smith S. D. , and Anderson R. J. , 1993: On the dependence of sea surface roughness on wave development. J. Phys. Oceanogr, 23 , 21432149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freilich, M. H., and Dunbar R. S. , 1999: The accuracy of NSCAT1 vector winds: Comparisons with National Data Buoy Center buoys. J. Geophys. Res, 104 , 1123111246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freilich, M. H., and Vanhoff B. A. , 1999: QuikSTAT vector wind accuracy: Initial esimates. Proc. QuikSTAT Cal/Val Early Science Meeting, Pasadena, CA, Jet Propulsion Laboratory.

    • Search Google Scholar
    • Export Citation
  • Geernaert, G. L., 1990: Bulk Parameterization for the Wind Stress and Heat Fluxes. Vol. 1. Kluwer Academic, 91–172.

  • Gilhousen, D. B., 1987: A field evaluation of NDBC moored buoy winds. J. Atmos. Oceanic Technol, 4 , 94104.

  • Graber, H. C., Ebuchi N. , and Vakkayil R. , 1996: Evaluation of ERS-1 scatterometer winds with wind and wave ocean buoy observations. Rosentiel School of Marine and Atmospheric Science Tech. Rep. RSMAS 96-003, 69 pp.

    • Search Google Scholar
    • Export Citation
  • Grima, N., Bentamy A. , Delecluse P. , Katsaros K. , Levy C. , and Quilfen Y. , 1999: Sensitivity of an oceanic general circulation model forced by satellite wind-stress fields. J. Geophys. Res, 104 , 79677989.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, N. E., Long S. R. , and Bliven L. F. , 1981: On the importance of the significant slope in empirical wind wave studies. J. Phys. Oceanogr, 11 , 569573.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johannessen, J. A., Shuchman R. A. , Johannessen O. M. , Davidson K. L. , and Lyzenga D. R. , 1991: Synthetic aperture radar imaging of upper ocean circulation features and wind fronts. J. Geophys. Res, 96 , 1041110422.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keller, W. C., Wismann W. C. , and Alpers W. , 1989: Tower-based measurements of the ocean C-band radar backscattering cross section. J. Geophys. Res, 94 , 924930.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kelly, K. A., Dickinson S. , and Yu Z. , 1999: NSCAT tropical wind stress maps: Implications for improving ocean modeling. J. Geophys. Res, 104 , 1129111310.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kitaigorodskii, S. A., 1973: The Physics of Air—Sea Interaction. Israel Program for Scientific Translations, 237 pp.

  • Lagarias, J. C., Reeds J. A. , Wright M. H. , and Wright P. E. , 1998: Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J. Optim, 9 , 112147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Large, W. G., Morzel J. , and Crawford G. B. , 1995: Accounting for surface wave distortion of the marine wind profile in low-level ocean storms wind measurements. J. Phys. Oceanogr, 25 , 29592971.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Longuet-Higgins, M. S., and Smith N. D. , 1986: Measurements of breaking waves: Implications for wind-stress and wave generation. Proc. Wave Dynamics and Radio Probing of the Ocean Surface, Miami, FL, Inter-Union Commission on Radar Meteorology, 257–264.

    • Search Google Scholar
    • Export Citation
  • Makin, V. K., and Mastenbroek C. , 1996: Impact of waves on air–sea exchange of sensible heat and momentum. Bound.-Layer Meteor, 79 , 279300.

  • Martinez-Diaz-de-Leon, A., Robinson I. S. , Ballestero D. , and Cohen E. , 1999: Wind driven ocean circulation features in the Gulf of Tehuantepac. Int. J. Remote Sens, 20 , 16611668.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mastenbroek, C., Makin V. K. , Garat M. H. , and Giovanangeli J. P. , 1996: Experimental evidence of the rapid distortion of turbulence in the air flow over water waves. J. Fluid Mech, 318 , 273302.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., and Coauthors,. . 1998: The Tropical Ocean Global Atmosphere (TOGA) observing system: A decade of progress. J. Geophys. Res, 103 , 1416914240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Millif, R. F., Large W. G. , Morzel J. , Danabasoglu G. , and Chin T. M. , 1999: Ocean general circulation model sensitivity to forcing from scatterometer winds. J. Geophys. Res, 104 , 1133711358.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nillson, C. S., and Tildesley P. C. , 1995: Imaging of oceanic features by ERS-1 synthetic apperture radar. J. Geophys. Res, 100 , 953967.

  • Phillips, O. M., 1977: The Dynamics of the Upper Ocean. Cambridge University Press, 336 pp.

  • Phillips, O. M., 1984: On the response of short ocean wave components at a fixed wavenumber to ocean current variations. J. Phys. Oceanogr, 14 , 14251433.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Plant, W. J., 1986: A two-scale model of short wind-generated waves and scatterometry. J. Geophys. Res, 91 , 1073510749.

  • Queffeulou, P., Chapron B. , and Bentamy A. , 1999: Comparing Ku-band NSCAT scatterometer and ERS-2 altimeter winds. Trans. Geosci. Remote Sens, 37 , 16621670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quilfen, Y., and Bentamy A. , 1994: Calibration/validation of ERS-1 scatterometerprecision products. Proc. IGARSS'94, Piscataway, NJ, IEEE, 945–947.

    • Search Google Scholar
    • Export Citation
  • Quilfen, Y., Chapron B. , Elfouhaily T. , Katsaros K. , and Tournadre J. , 1998:: Observation of tropical cyclones by high resolution scatterometry. J. Geophys. Res, 103 , 77677786.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quilfen, Y., Chapron B. , Bentamy A. , Gourrion J. , Elfouhaily T. , and Vandemark D. , 1999: Global ERS-1/2 and NSCAT observations: Upwind/crosswind and upwind/downwind measurements. J. Geophys. Res, 104 , 1145911469.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quilfen, Y., Bentamy A. , Delecluse P. , Katsaros K. , Gourrion J. , and Grima N. , 2000: Sea level anomalies measured by TOPEX/Poseidon and derived from an ocean model forced by scatterometer wind-stress fields. Trans. Geosci. Remote Sens, 38 , 18711884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reul, N., Branger H. , Bliven L. F. , and Giovanangeli J. P. , 1999: The influence of oblique waves on the azimuthal response of a Ku-band scatterometer: A laboratory study. Trans. Geosci. Remote Sens, 37 , 3647.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reverdin, G., Frankignoul C. , Kestenare E. , and McPhaden M. , 1994:: Seasonal variability in the surface currents of the equatorial Pacific. J. Geophys. Res, 99 , 2032320344.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, S. D., 1988: Coefficients for sea surface wind stress, heat flux and wind profiles as a function of wind speed and temperature. J. Geophys. Res, 93 , 1546715472.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoffelen, A., 1998: Towards the true surface wind speed: Error modeling and calibration using triple collocation. J. Geophys. Res, 103 , 77557766.

  • Stoffelen, A., and Anderson D. , 1997: Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. J. Geophys. Res, 102 , 57675780.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vandemark, D., Edson J. B. , and Chapron B. , 1997: Altimeter estimation of sea surface wind stress for light to moderate winds. J. Atmos. Oceanic Technol, 14 , 716722.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verschell, M. A., Bourassa M. A. , Weissman D. E. , and O'Brien J. J. , 1999: Model validation of the NASA scatterometer winds. Geophys. Res, 104 , 1135911374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weissman, D. E., and Graber H. C. , 1999: Satellite scatterometer studies of ocean surface stress and drag coefficients using a direct model. J. Geophys. Res, 104 , 1132911336.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woiceshyn, P. M., Wurtele M. G. , Boggs D. H. , McGoldrick L. F. , and Peteherych S. , 1986: The necessity for a new parameterrization of an empirical model for wind/ocean scatterometry. J. Geophys. Res, 91 , 22732288.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, D., and Mendoza C. , 1993: Surface drift effect on wind energy transfer to waves. J. Geophys. Res, 98 , 1452714544.

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The ERS Scatterometer Wind Measurement Accuracy: Evidence of Seasonal and Regional Biases

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  • 1 Département d'Océanographie Spatiale, IFREMER, Centre de Brest, Plouzane, France
  • | 2 Laboratory for Hydrospheric Processes, Wallops Flight Facility, NASA Goddard Space Flight Center, Wallops Island, Virginia
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Abstract

A validation of European Space Agency (ESA) remote sensing satellite (ERS) scatterometer ocean wind measurements is performed using a formalism recently proposed for and applied to NASA scatterometer (NSCAT) and Special Sensor Microwave Imager (SSM/I) measurements. This simple analytical model relates scatterometer measurements to true winds, taking into account errors in the satellite winds as well as errors in the data used for reference. In this study, National Data Buoy Center (NDBC) buoy winds are the chosen reference. In addition, ECMWF analysis winds are used as a third data source to completely determine the errors via a triple collocation analysis. According to this development, the resulting wind speed error analysis indicates that ERS scatterometer estimates are negatively biased at light winds. This result differs from recent results determined using standard regression analysis. It is also shown that ERS and NSCAT measurement accuracies are comparable in an overall sense.

This error model provides a more certain measure of both random and systematic terms and the authors use this tool to look at possible systematic scatterometer wind speed biases in two separate long-term (1992–98) ERS datasets. The chosen approach examines temporal and spatial variation between ocean buoy and ERS-derived winds to identify both seasonal and regional ERS wind error signatures. First, data indicate a time-dependent bias between NDBC and ERS winds that is strongly correlated with the seasonal cycle. Buoy-derived long-wave and atmospheric stability parameter averages exhibit similar cycles and are the likely geophysical links to this scatterometer error. An illustration of regional or spatially varying error sources is further provided using ERS data collocated with Tropical Atmosphere and Ocean (TAO) buoy array measurements. In this case the long-term average wind speed bias between TAO and ERS exhibits well-defined spatial structures within the equatorial belt (10°N, 10°S). Bias variations show qualitative agreement with a near-surface current climatology map for this Pacific region and also with the limited available buoy current measurements. Overall results indicate small but systematic nonwind sea surface effects on scatterometer products. It is concluded that there cannot be one set of values for ERS scatterometer wind validation parameters. Accounting for surface effects on scatterometer measurements may need consideration to ensure proper assimilation of scatterometer data into weather forecasting and climate prediction models.

Corresponding author address: Y. Quilfen, Département d'Océanographie Spatiale, IFREMER, centre de Brest, BP 70, 29280 Plouzané, France. Email: yquilfen@ifremer.fr

Abstract

A validation of European Space Agency (ESA) remote sensing satellite (ERS) scatterometer ocean wind measurements is performed using a formalism recently proposed for and applied to NASA scatterometer (NSCAT) and Special Sensor Microwave Imager (SSM/I) measurements. This simple analytical model relates scatterometer measurements to true winds, taking into account errors in the satellite winds as well as errors in the data used for reference. In this study, National Data Buoy Center (NDBC) buoy winds are the chosen reference. In addition, ECMWF analysis winds are used as a third data source to completely determine the errors via a triple collocation analysis. According to this development, the resulting wind speed error analysis indicates that ERS scatterometer estimates are negatively biased at light winds. This result differs from recent results determined using standard regression analysis. It is also shown that ERS and NSCAT measurement accuracies are comparable in an overall sense.

This error model provides a more certain measure of both random and systematic terms and the authors use this tool to look at possible systematic scatterometer wind speed biases in two separate long-term (1992–98) ERS datasets. The chosen approach examines temporal and spatial variation between ocean buoy and ERS-derived winds to identify both seasonal and regional ERS wind error signatures. First, data indicate a time-dependent bias between NDBC and ERS winds that is strongly correlated with the seasonal cycle. Buoy-derived long-wave and atmospheric stability parameter averages exhibit similar cycles and are the likely geophysical links to this scatterometer error. An illustration of regional or spatially varying error sources is further provided using ERS data collocated with Tropical Atmosphere and Ocean (TAO) buoy array measurements. In this case the long-term average wind speed bias between TAO and ERS exhibits well-defined spatial structures within the equatorial belt (10°N, 10°S). Bias variations show qualitative agreement with a near-surface current climatology map for this Pacific region and also with the limited available buoy current measurements. Overall results indicate small but systematic nonwind sea surface effects on scatterometer products. It is concluded that there cannot be one set of values for ERS scatterometer wind validation parameters. Accounting for surface effects on scatterometer measurements may need consideration to ensure proper assimilation of scatterometer data into weather forecasting and climate prediction models.

Corresponding author address: Y. Quilfen, Département d'Océanographie Spatiale, IFREMER, centre de Brest, BP 70, 29280 Plouzané, France. Email: yquilfen@ifremer.fr

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