Estamos realizando la búsqueda. Por favor, espere...
Abstract: Autoregressive logistic regression models have been demonstrated to be a powerful tool for statistical simulation of spatial patterns in climate and meteorology fields. In this paper we introduce a statistical framework for the simulation of ocean current patterns based on the autoregressive logistic regression models, and apply it to the Gulf of Mexico Loop Current. The statistical model is forced by three autoregressive terms, the wind stress curl in the Gulf of Mexico and in the Caribbean Sea, and the sea level pressure anomalies over the North Atlantic. It is used to replicate the bi-weekly historical sequence of 8 Loop Current patterns, obtained from a 24-year altimetry derived dataset. The model reproduces the inter-annual and intra-annual variability of the original time series, showing notable fitting capacity. A point-by-point comparison between the actual and simulated pattern series confirms the capability of the model in analysing the evolution of ocean current patterns. The predictive skill of the model is also explored, and the preliminary forecast (up to 3?months) results are encouraging. The presented statistical framework may find more practical applications in the future, such as the generation of statistically sound climate-based oceanographic scenarios for risk analyses, and the mid-term probabilistic prediction of ocean current patterns.
Fuente: Ocean Modelling Volume 136, April 2019, Pages 1-12
Editorial: Elsevier Ltd
Fecha de publicación: 01/04/2019
Nº de páginas: 12
Tipo de publicación: Artículo de Revista
Proyecto español: TRA2014-59570-R ; TRA2017-89164-R
Url de la publicación: https://doi.org/10.1016/j.ocemod.2019.02.010
ANA JULIA ABASCAL SANTILLANA
MARIA SONIA CASTANEDO BARCENA
JOSE ANTONIO ALVAREZ ANTOLINEZ
WEISBERG, ROBERT H.
RAUL MEDINA SANTAMARIA