A Hierarchical Spatial Model for Constructing Wind Fields from Scatterometer Data in the Labrador Sea

  • J. A. Royle
  • L. M. Berliner
  • C. K. Wikle
  • R. Milliff
Part of the Lecture Notes in Statistics book series (LNS, volume 140)


Wind fields are important for many geophysical reasons, but high resolution wind data over ocean regions are scarce and difficult to collect. A satellite-borne scatterometer produces high resolution wind data. We constructed a hierarchical spatial model for estimating wind fields over the Labrador sea region based on scatterometer data. The model incorporates spatial structure via a model of the u and y components of wind conditional on an unobserved pressure field. The conditional dependence is parameterized in this model through the physically based assumption of geostrophy. The pressure field is parameterized as a Gaussian random field with a stationary correlation function. The model produces realistic wind fields, but more importantly it appears to be able to reproduce the true pressure field, suggesting that the parameterization of geostrophy is useful. This further suggests that the model should be able to produce reasonable predictions outside of the data domain.


Wind Field Pressure Field Measurement Error Variance Surface Wind Field Scatterometer Data 
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 New York 1999

Authors and Affiliations

  • J. A. Royle
  • L. M. Berliner
  • C. K. Wikle
  • R. Milliff

There are no affiliations available

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