Estimating the High-Resolution Mean Sea-Surface Velocity Field by Combined use of Altimeter and Drifter Data for Geoid Model Improvement

  • Shiro Imawaki
  • Hiroshi Uchida
  • Kaoru Ichikawa
  • Daisuke Ambe
Part of the Space Sciences Series of ISSI book series (SSSI, volume 17)


The mean sea-surface height obtained from satellite altimeters is different from the geoid by the amount of mean sea-surface dynamic topography associated with ocean currents. Assuming geostrophy at the sea surface, the mean sea-surface dynamic topography can be obtained from the mean sea-surface velocity field. This field is derived by combining anomalies (i.e.. deviations from the mean) of sea-surface velocity obtained from altimeter data and in situ surface velocities estimated from trajectories of surface drifting-buoys (hereafter, drillers). Where a drifter measured the surface velocity, the temporal mean velocity can be estimated by subtracting the altimeter-derived velocity anomaly at that time from the drifter-measured surface velocity. The method is applied to the surface flow field of the North Pacific, using TOPEX/POSEIDON and ERS-I/2 altimeter data, and WOCE- TOGA surface drifter data obtained from October 1992 through December 2000. The temporal mean velocity lield is estimated with a resolution of quarter degrees in both latitude and longitude. The obtained mean velocity field clearly shows the Kuroshio and Kuroshio Extension, which are narrower and stronger than the climatological mean features derived from historical hydrographic data averaged over several decades. Instantaneous velocities are estimated by summing up these temporal mean velocities and anomalies, every ten days during the eight years. They compare well with in situ velocities measured by the surface drifters. The instantaneous velocity field shows energetic fluctuation of the Kuroshio Extension vividly.


Surface Velocity Altimeter Data Geoid Height Kuroshio Extension Satellite Altimeter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Shiro Imawaki
    • 1
  • Hiroshi Uchida
    • 2
  • Kaoru Ichikawa
    • 3
  • Daisuke Ambe
    • 4
  1. 1.Research Institute for Applied MechanicsKyushu UniversityJapan
  2. 2.Japan Marine Science and Technology CenterJapan
  3. 3.Research Institute for Applied MechanicsKyushu UniversityJapan
  4. 4.Interdisciplinary Graduate School of Engineering SciencesKyushu UniversityJapan

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