%0 Journal Article %1 Alvarez:2007:JMS %A Alvarez, A. %A Orfila, A. %A Basterretxea, G. %A Tintore, J. %A Vizoso, G. %A Fornes, A. %D 2007 %J Journal of Marine Systems %K Front Ocean Satellite algorithms, data, evolution genetic prediction, programming, %N 1-4 %P 299--313 %R doi:10.1016/j.jmarsys.2005.11.017 %T Forecasting front displacements with a satellite based ocean forecasting (SOFT) system %V 65 %X 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.

@article{Alvarez:2007:JMS, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Alvarez, A. and Orfila, A. and Basterretxea, G. and Tintore, J. and Vizoso, G. and Fornes, A.}, biburl = {https://www.bibsonomy.org/bibtex/2f19e81356c5a332e8d03e0c8510369e5/brazovayeye}, doi = {doi:10.1016/j.jmarsys.2005.11.017}, interhash = {e40f9716c3dd44ed6235df4dbda438a8}, intrahash = {f19e81356c5a332e8d03e0c8510369e5}, journal = {Journal of Marine Systems}, keywords = {Front Ocean Satellite algorithms, data, evolution genetic prediction, programming,}, month = {March}, note = {Marine Environmental Monitoring and Prediction - Selected papers from the 36th International Liege Colloquium on Ocean Dynamics}, number = {1-4}, pages = {299--313}, timestamp = {2008-06-19T17:35:36.000+0200}, title = {Forecasting front displacements with a satellite based ocean forecasting ({SOFT}) system}, volume = 65, year = 2007 }