Location of Repository

A New Statistical Modelling Approach to Ocean Front Detection from SST Satellite Images

By Jo Hopkins, Peter Challenor and Andrew G.P. Shaw


Ocean fronts are narrow zones of intense dynamic activity that play an important role in global ocean-atmosphere interactions. Owing to their highly variable nature, both in space and time, they are notoriously difficult features to adequately sample using traditional in-situ techniques. In this paper we propose a new statistical modelling approach to detecting and monitoring ocean fronts from AVHRR SST satellite images that builds on the 'front following' algorithm of Shaw and Vennell (2000). Weighted local likelihood is used to provide a smooth, non-parametric description of spatial variations in the position, mean temperature, width and temperature change of an individual front within an image. Weightings are provided by a Gaussian kernel function whose width is automatically determined by likelihood cross-validation. The statistical model fitting approach allows estimation of the uncertainty of each parameter to be quantified, a capability not possessed by other techniques. The algorithm is shown to be robust to noise and missing data in an image, problems that hamper many of the existing front detection schemes. The approach is general and could be used with other remotely sensed data sets, model output or data assimilation products

Topics: GC
Year: 2010
OAI identifier: oai:eprints.soton.ac.uk:68757
Provided by: e-Prints Soton
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://dx.doi.org/10.1175/2009... (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.