Statistical Simulation of Ocean Current Patterns Using Autoregressive Logistic Regression Models: a Case Study in the Gulf of Mexico
autoregressive logistic regression, gulf of mexico, loop current, ocean current patterns, statistical modelling
Digital Object Identifier (DOI)
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.
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Citation / Publisher Attribution
Ocean Modelling, v. 136, p. 1-12
Scholar Commons Citation
Chiri, Helios; Abascal, Ana J.; Castanedo, Sonia; Antolínez, José A. A.; Liu, Yonggang; Weisberg, Robert H.; and Medina, Raul, "Statistical Simulation of Ocean Current Patterns Using Autoregressive Logistic Regression Models: a Case Study in the Gulf of Mexico" (2019). Marine Science Faculty Publications. 403.