• Atlas, R., , R. N. Hoffman, , S. C. Bloom, , J. C. Jusem, , and J. Ardizzone, 1996: A multiyear global surface wind velocity dataset using SSM/I wind observations. Bull. Amer. Meteor. Soc., 77 , 869882.

    • Search Google Scholar
    • Export Citation
  • Benada, R., 1997: Merged GDR (TOPEX/Poseidon) Generation B (user's guide). Rep. D-11007, Jet Propulsion Laboratory, Pasadena, CA, 84 pp.

    • Search Google Scholar
    • Export Citation
  • Bentamy, A., , Y. Quilfen, , F. Gohin, , N. Grima, , M. Lenaour, , and J. Servain, 1996: Determination and validation of average fields from scatterometer measurements. Global Atmos. Ocean Syst., 4 , 129.

    • Search Google Scholar
    • Export Citation
  • Bentamy, A., , P. Queffeulou, , Y. Quilfen, , and K. Katsaros, 1999: Ocean surface wind fields estimated from satellite active and passive microwave instruments. IEEE Trans. Geosci. Remote Sens., 37 , 24692486.

    • Search Google Scholar
    • Export Citation
  • Boutin, J., , and J. Etcheto, 1990: Seasat scatterometer versus Scanning Multichannel Microwave Radiometer wind speed: A comparison on a global scale. J. Geophys. Res., 95 , 2227522288.

    • Search Google Scholar
    • Export Citation
  • Boutin, J., , and J. Etcheto, 1996: Consistency of Geosat, SSM/I, and ERS-1 global surface wind speeds—Comparison with in situ data. J. Atmos. Oceanic Technol., 13 , 183197.

    • Search Google Scholar
    • Export Citation
  • Boutin, J., , L. Siefridt, , J. Etcheto, , and B. Barnier, 1996: Comparison of ECMWF and satellite ocean wind speeds from 1985 to 1992. Int. J. Remote Sens., 17 , 28972913.

    • Search Google Scholar
    • Export Citation
  • Boutin, J., , J. Etcheto, , M. Rafizadeh, , and D. C. E. Bakker, 1999: Comparison of NSCAT, ERS-2 active microwave instruments, Special Sensor Microwave Imager, and Carbon Interface Ocean Atmosphere buoy wind speed: Consequences for the air–sea CO2 exchange coefficient. J. Geophys. Res., 104 , 1137511392.

    • Search Google Scholar
    • Export Citation
  • Busalacchi, A. J., , R. M. Atlas, , and E. C. Hackert, 1993: Comparison of Special Sensor Microwave Imager vector wind stress with model-derived and subjective products for the tropical Pacific. J. Geophys. Res., 98 , 69616977.

    • Search Google Scholar
    • Export Citation
  • Chen, G., , and R. Ezraty, 1996: Alias impacts on the recovery of sea level amplitude and energy from altimeter measurements. Int. J. Remote Sens., 17 , 35673576.

    • Search Google Scholar
    • Export Citation
  • Chen, G., , B. Chapron, , J. Tournadre, , K. Katsaros, , and D. Vandemark, 1997: Global oceanic precipitation: A joint view by TOPEX and the TOPEX microwave radiometer. J. Geophys. Res., 102 , 1045710471.

    • Search Google Scholar
    • Export Citation
  • Chen, G., , B. Chapron, , J. Tournadre, , K. Katsaros, , and D. Vandemark, 1998: Identification of possible wave damping by rain using TOPEX and TMR data. Remote Sens. Environ., 63 , 4048.

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

    • Search Google Scholar
    • Export Citation
  • Chen, G., , B. Chapron, , R. Ezraty, , and D. Vandemark, 2002a: A global view of swell and wind sea climate in the ocean by satellite altimeter and scatterometer. J. Atmos. Oceanic Technol., 19 , 18491859.

    • Search Google Scholar
    • Export Citation
  • Chen, G., , R. Ezraty, , C. Fang, , and L. Fang, 2002b: A new look at the zonal pattern of the marine wind system from TOPEX measurements. Remote Sens. Environ., 79 , 1522.

    • Search Google Scholar
    • Export Citation
  • Chen, G., , J. Ma, , C. Fang, , and Y. Han, 2003: Global oceanic precipitation derived from TOPEX and TMR: Climatology and variability. J. Climate, 16 , 38883904.

    • Search Google Scholar
    • Export Citation
  • Dunbar, R. S., 1997: NASA Scatterometer: High-resolution merged geophysical data product (user's guide). Jet Propulsion Laboratory, Pasadena, CA, 17 pp.

    • Search Google Scholar
    • Export Citation
  • Ebuchi, N., , H. Kawamura, , and Y. Toba, 1992: Growth of wind waves with fetch observed by the Geosat altimeter in the Japan Sea under winter monsoon. J. Geophys. Res., 97 , 809819.

    • Search Google Scholar
    • Export Citation
  • Freilich, M. H., , and R. S. Dunbar, 1999: The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys. J. Geophys. Res., 104 , 1123111246.

    • Search Google Scholar
    • Export Citation
  • Fu, L-L., , and A. Cazenave, Eds.,. 2001: Satellite Altimetry and Earth Sciences. International Geophysical Series, Vol. 69, Academic Press, 463 pp.

    • Search Google Scholar
    • Export Citation
  • Fu, L-L., , E. J. Christensen, , C. A. Yamarone, , M. Lefebvre, , Y. Ménard, , M. Dorrer, , and P. Escudier, 1994: TOPEX/POSEIDON mission overview. J. Geophys. Res., 99 , 2436924381.

    • Search Google Scholar
    • Export Citation
  • Glazman, R. E., , and S. H. Pilorz, 1990: Effects of sea maturity on satellite altimeter measurements. J. Geophys. Res., 95 , 28572870.

  • Glazman, R. E., , G. G. Pihos, , and J. Ip, 1988: Scatterometer wind speed bias induced by the large-scale component of the wave field. J. Geophys. Res., 93 , 13171328.

    • Search Google Scholar
    • Export Citation
  • Gourrion, J., , D. Vandemark, , S. Bailey, , and B. Chapron, 2000: Satellite altimeter models for surface wind speed developed using ocean satellite crossovers. IFREMER Tech. Rep. IFREMER-DROOS-2000-02, 62 pp.

    • Search Google Scholar
    • Export Citation
  • Gower, J. F. R., 1996: Intercalibration of wave and wind data from TOPEX/POSEIDON and moored buoys off the west coast of Canada. J. Geophys. Res., 101 , 38173829.

    • Search Google Scholar
    • Export Citation
  • Halpern, D., , A. Hollingsworth, , and F. Wentz, 1994: ECMWF and SSM/I global wind speeds. J. Atmos. Oceanic Technol., 11 , 779788.

  • Hoffman, R. N., , F. Wentz, , D. Long, , and K. Arai, 1994: Atmospheric losses at 14 GHz. NSCAT SWT Attenuation Subpanel Report, Jet Propulsion Laboratory, Pasadena, CA, 13 pp.

    • Search Google Scholar
    • Export Citation
  • Hollinger, J. P., , J. L. Pierce, , and G. A. Poe, 1990: SSMI instrument evaluation. IEEE Trans. Geosci. Remote Sens., 28 , 781790.

  • Hwang, P. A., , and O. H. Shemdin, 1988: The dependence of sea surface slope on atmospheric stability and swell conditions. J. Geophys. Res., 93 , 1390313912.

    • Search Google Scholar
    • Export Citation
  • Hwang, P. A., , W. J. Teague, , G. A. Jacobs, , and D. W. Wang, 1998: A statistical comparison of wind speed, wave height, and wave period from satellite altimeters and ocean buoys in the Gulf of Mexico region. J. Geophys. Res., 103 , 1045110468.

    • Search Google Scholar
    • Export Citation
  • Meissner, T., , D. Smith, , and F. Wentz, 2001: A 10 year intercomparison between collocated Special Sensor Microwave Imager oceanic surface wind speed retrievals and global analyses. J. Geophys. Res., 106 , 1173111742.

    • Search Google Scholar
    • Export Citation
  • Naderi, F. M., , M. H. Freilich, , and D. G. Long, 1991: Spaceborne radar measurement of wind velocity over the ocean: An overview of the NSCAT scatterometer system. Proc. IEEE, 79 , 850866.

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

    • Search Google Scholar
    • Export Citation
  • Quilfen, Y., , B. Chapron, , and D. Vandemark, 2001: On the ERS scatterometer wind measurement accuracy: Evidence of seasonal and regional biases. J. Atmos. Oceanic Technol., 18 , 16841697.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., , R. Atlas, , S. D. Schubert, , and C. S. Willet, 1996: A comparison of surface wind products over the North Pacific Ocean. J. Geophys. Res., 101 , 10111023.

    • Search Google Scholar
    • Export Citation
  • Schlax, M. G., , and D. B. Chelton, 1994: Aliased tidal errors in Topex/Poseidon sea surface height data. J. Geophys. Res., 99 , 2476124775.

    • Search Google Scholar
    • Export Citation
  • Vachon, P. W., , and F. W. Dobson, 1996: Validation of wind vector retrieval from ERS-1 SAR images over the ocean. Global Atmos. Ocean Syst., 5 , 177187.

    • Search Google Scholar
    • Export Citation
  • WAMDI group, 1988: The WAM model—A third generation ocean wave prediction model. J. Phys. Oceanogr., 18 , 17751810.

  • Wentz, F. J., , and D. K. Smith, 1999: A model function for the ocean-normalized radar cross section at 15 GHz derived from NSCAT observations. J. Geophys. Res., 104 , 1149911514.

    • Search Google Scholar
    • Export Citation
  • Witter, D. L., , and D. B. Chelton, 1991: A Geosat altimeter wind speed algorithm and a method for altimeter wind speed algorithm development. J. Geophys. Res., 96 , 88538860.

    • Search Google Scholar
    • Export Citation
  • Young, I. R., 1999: Seasonal variability of the global ocean wind and wave climate. Int. J. Climatol., 19 , 931950.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 17 17 1
PDF Downloads 4 4 0

An Intercomparison of TOPEX, NSCAT, and ECMWF Wind Speeds: Illustrating and Understanding Systematic Discrepancies

View More View Less
  • 1 Ocean Remote Sensing Institute, Ocean University of China, Qingdao, China
© Get Permissions
Restricted access

Abstract

The availability of multiple satellite missions with wind measuring capacity has made it more desirable than ever before to integrate wind data from various sources in order to achieve an improved accuracy, resolution, and duration. A clear understanding of the error characteristics associated with each type of data is needed for a meaningful merging or combination. The two kinds of errors—namely, random error and systematic error—are expected to evolve differently with increasing volume of available data. In this study, a collocated ocean Topography Experiment (TOPEX)–NASA Scatterometer (NSCAT)–ECMWF dataset, which covers 66°S–66°N and spans the entire 10-month lifetime of NSCAT, is compiled to investigate the systematic discrepancies among the three kinds of wind estimates, yielding a number of interesting results. First, the satellite-derived wind speeds are found to have a larger overall bias but a much smaller overall root-mean-square (rms) error compared to ECMWF winds, implying that they are highly converging but are systematically biased. Second, the TOPEX and NSCAT wind speed biases are characterized by a significant “phase opposition” with latitude, season, and wind intensity, respectively. Third, the TOPEX (NSCAT) bias exhibits a low–high–low (high–low–high) pattern as a function of wind speed, whose turning point at 14.2 m s−1 coincides well with the transitional wind speed from swell dominance to wind sea dominance in wave condition, suggesting that the degree of wave development plays a key role in shaping wind speed bias.

Corresponding author address: Dr. Ge Chen, Ocean Remote Sensing Institute, Ocean University of China, 5 Yushan Road, Qingdao 266003, China. Email: gechen@public.qd.sd.cn

Abstract

The availability of multiple satellite missions with wind measuring capacity has made it more desirable than ever before to integrate wind data from various sources in order to achieve an improved accuracy, resolution, and duration. A clear understanding of the error characteristics associated with each type of data is needed for a meaningful merging or combination. The two kinds of errors—namely, random error and systematic error—are expected to evolve differently with increasing volume of available data. In this study, a collocated ocean Topography Experiment (TOPEX)–NASA Scatterometer (NSCAT)–ECMWF dataset, which covers 66°S–66°N and spans the entire 10-month lifetime of NSCAT, is compiled to investigate the systematic discrepancies among the three kinds of wind estimates, yielding a number of interesting results. First, the satellite-derived wind speeds are found to have a larger overall bias but a much smaller overall root-mean-square (rms) error compared to ECMWF winds, implying that they are highly converging but are systematically biased. Second, the TOPEX and NSCAT wind speed biases are characterized by a significant “phase opposition” with latitude, season, and wind intensity, respectively. Third, the TOPEX (NSCAT) bias exhibits a low–high–low (high–low–high) pattern as a function of wind speed, whose turning point at 14.2 m s−1 coincides well with the transitional wind speed from swell dominance to wind sea dominance in wave condition, suggesting that the degree of wave development plays a key role in shaping wind speed bias.

Corresponding author address: Dr. Ge Chen, Ocean Remote Sensing Institute, Ocean University of China, 5 Yushan Road, Qingdao 266003, China. Email: gechen@public.qd.sd.cn

Save