Effectiveness of Using Multisatellite Wind Speed Estimates to Construct Hourly Wind Speed Datasets with Diurnal Variations

Shin’ichiro Kako Department of Ocean Civil Engineering, Graduate School of Science and Engineering, Kagoshima University, Kagoshima, Japan

Search for other papers by Shin’ichiro Kako in
Current site
Google Scholar
PubMed
Close
,
Atsushi Okuro Oceanographic Command, Maritime Self-Defense Force, Kanagawa, Japan

Search for other papers by Atsushi Okuro in
Current site
Google Scholar
PubMed
Close
, and
Masahisa Kubota Institute of Oceanic Research and Development, Tokai University, Shizuoka, Japan

Search for other papers by Masahisa Kubota in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This study used an optimum interpolation method (OIM) to construct a sea surface wind speed (SSW) dataset with hourly resolution based on merged wind speed analysis products from four satellites [AMSR-2, ASCAT, OceanSat Scatterometer (OSCAT), and WindSat]. To validate this hourly SSW dataset, the OIM dataset was compared with observations obtained from moored buoys. These buoy observations were also compared with the products of each of the four satellites individually. The root-mean-square differences and the correlation coefficients between the buoy observations and the OIM dataset indicated that the accuracy of the dataset was slightly lower than that of the single-satellite products. However, a spectrum analysis at the buoy locations indicated that the OIM dataset was capable of resolving diurnal variations in wind speed, which was a result not reproduced by the single-satellite products. In addition, the study also found that the hourly dataset with diurnal variation was effective in obtaining accurate daily mean values by reducing the sampling error. A comparison of daily mean wind speeds derived from satellite observations with those obtained from buoy observations demonstrated that greater accuracy in daily mean SSW data could be achieved using multisatellite observations in comparison with single-satellite observations. Therefore, the application of multisatellite observations that have different observation times could be a useful and effective approach with which to construct datasets with high temporal resolution and to improve the accuracy of daily mean values.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Shin’ichiro Kako, kako@oce.kagoshima-u.ac.jp

Abstract

This study used an optimum interpolation method (OIM) to construct a sea surface wind speed (SSW) dataset with hourly resolution based on merged wind speed analysis products from four satellites [AMSR-2, ASCAT, OceanSat Scatterometer (OSCAT), and WindSat]. To validate this hourly SSW dataset, the OIM dataset was compared with observations obtained from moored buoys. These buoy observations were also compared with the products of each of the four satellites individually. The root-mean-square differences and the correlation coefficients between the buoy observations and the OIM dataset indicated that the accuracy of the dataset was slightly lower than that of the single-satellite products. However, a spectrum analysis at the buoy locations indicated that the OIM dataset was capable of resolving diurnal variations in wind speed, which was a result not reproduced by the single-satellite products. In addition, the study also found that the hourly dataset with diurnal variation was effective in obtaining accurate daily mean values by reducing the sampling error. A comparison of daily mean wind speeds derived from satellite observations with those obtained from buoy observations demonstrated that greater accuracy in daily mean SSW data could be achieved using multisatellite observations in comparison with single-satellite observations. Therefore, the application of multisatellite observations that have different observation times could be a useful and effective approach with which to construct datasets with high temporal resolution and to improve the accuracy of daily mean values.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Shin’ichiro Kako, kako@oce.kagoshima-u.ac.jp
Save
  • Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, and D. Gombos, 2011: A cross-calibrated multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Amer. Meteor. Soc., 92, 157174, doi:10.1175/2010BAMS2946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bourlès, B. R., and Coauthors, 2008: The PIRATA program: History, accomplishments, and future directions. Bull. Amer. Meteor. Soc., 89, 11111125, doi:10.1175/2008BAMS2462.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clayson, C. A., and A. S. Bogdanoff, 2013: The effect of diurnal sea surface temperature warming on climatological air–sea fluxes. J. Climate, 26, 25462556, doi:10.1175/JCLI-D-12-00062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • Donlon, C. I., and Coauthors, 2007: The Global Ocean Data Assimilation Experiment High-Resolution Sea Surface Temperature Pilot Project. Bull. Amer. Meteor. Soc., 88, 11971213, doi:10.1175/BAMS-88-8-1197.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of air–sea fluxes, updates and verification for the COARE algorithms. J. Climate, 16, 571591, doi:10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kako, S., and M. Kubota, 2006: Relationship between an El Nino event and the interannual variability of significant wave height in the North Pacific. Atmos.–Ocean, 44, 377395, doi:10.3137/ao.440404.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kako, S., A. Isobe, and M. Kubota, 2011: High resolution ASCAT wind vector dataset gridded by applying an optimum interpolation method to the global ocean. J. Geophys. Res., 116, D23107, doi:10.1029/2010JD015484.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuragano, T., and A. Shibata, 1997: Sea surface dynamics height of the Pacific Ocean derived from TOPEX/POSEIDON altimeter data: Calculation method and accuracy. J. Oceanogr., 53, 583599.

    • Search Google Scholar
    • Export Citation
  • Masson, S., P. Terray, G. Madec, J.-J. Luo, T. Yamagata, and K. Takahashi, 2012: Impact of intra-daily SST variability on ENSO characteristics in a coupled model. Climate Dyn., 39, 681707, doi:10.1007/s00382-011-1247-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., and Coauthors, 1998: The Tropical Ocean-Global Atmosphere observing system: A decade of progress. J. Geophys. Res., 103, 14 16914 240, doi:10.1029/97JC02906.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1175/2008BAMS2608.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NDBC, 2009: Handbook of automated data quality control checks and procedures. NDBC Tech. Doc. 09-02, 78 pp. [Available online at http:// www.ndbc.noaa.gov/NDBCHandbookofAutomatedDataQualityControl2009.pdf.]

  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 21852208, doi:10.1175/JCLI-D-12-00823.1.

  • Tomita, H., and M. Kubota, 2011: Sampling error of daily mean surface wind speed and air specific humidity due to Sun-synchronous satellite sampling and its reduction by multi-satellite sampling. Int. J. Remote Sens., 32, 33893404, doi:10.1080/01431161003749428.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 335 113 3
PDF Downloads 277 36 3