Forecasting front displacements with a satellite based ocean forecasting (SOFT) system

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Alvarez:2007:JMS,
  author =       "A. Alvarez and Alejandro Orfila and 
                 G. Basterretxea and J. Tintore and G. Vizoso and A. Fornes",
  title =        "Forecasting front displacements with a satellite based
                 ocean forecasting (SOFT) system",
  journal =      "Journal of Marine Systems",
  year =         "2007",
  volume =       "65",
  number =       "1-4",
  pages =        "299--313",
  month =        mar,
  note =         "Marine Environmental Monitoring and Prediction -
                 Selected papers from the 36th International Liege
                 Colloquium on Ocean Dynamics",
  keywords =     "genetic algorithms, genetic programming, Satellite
                 data, Ocean prediction, Front evolution",
  DOI =          "doi:10.1016/j.jmarsys.2005.11.017",
  abstract =     "Relatively long term time series of satellite data are
                 nowadays available. These spatiotemporal time series of
                 satellite observations can be employed to build
                 empirical models, called satellite based ocean
                 forecasting (SOFT) systems, to forecast certain aspects
                 of future ocean states. The forecast skill of SOFT
                 systems predicting the sea surface temperature (SST) at
                 sub-basin spatial scale (from hundreds to thousand
                 kilometres), has been extensively explored in previous
                 works. Thus, these works were mostly focused on
                 predicting large scale patterns spatially stationary.
                 At spatial scales smaller than sub-basin (from tens to
                 hundred kilometres), spatiotemporal variability is more
                 complex and propagating structures are frequently
                 present. In this case, traditional SOFT systems based
                 on Empirical Orthogonal Function (EOF) decompositions
                 could not be optimal prediction systems. Instead, SOFT
                 systems based on Complex Empirical Orthogonal Functions
                 (CEOFs) are, a priori, better candidates to resolve
                 these cases.

                 In this work we study and compare the performance of an
                 EOF and CEOF based SOFT systems forecasting the SST at
                 weekly time scales of a propagating mesoscale
                 structure. The SOFT system was implemented in an area
                 of the Northern Balearic Sea (Western Mediterranean
                 Sea) where a moving frontal structure is recurrently
                 observed. Predictions from both SOFT systems are
                 compared with observations and with the predictions
                 obtained from persistence models. Results indicate that
                 the implemented SOFT systems are superior in terms of
                 predictability to persistence. No substantial
                 differences have been found between the EOF and
                 CEOF-SOFT systems.",
}

Genetic Programming entries for Alberto Alvarez Diaz Alejandro Orfila G Basterretxea Joaquin Tintore Subirana G Vizoso A Fornes

Citations