Dataset Available

Dataset for: Statistical simulation of ocean current patterns using autoregressive logistic regression (ALR) models: A case study in the Gulf of Mexico

Authors: Chiri, Helios, Ana Julia Abascal, Sonia Castanedo, Jose Antonio A. Antolinez, Yonggang Liu, Robert H. Weisberg, and Raul Medina
Published On: Aug 04 2020 20:03 UTC
DOI: https://doi.org/10.7266/VTTJ99YX
UDI: R6.x820.000:0006
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No. of Downloads: 3

No. of Files: 12

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Project Information

Funded By:
Gulf of Mexico Research Initiative

Funding Cycle:
RFP-VI

Research Group:
Effects of Mesoscale Eddies on Three-Dimensional Oil Dispersion: Data Integration, Interpretation and Implications for Oil Spill Models

Point of Contact

Yonggang Liu
University of South Florida / College of Marine Science
yliu@usf.edu

Data Collection Period
1993-01-01
to
2017-08-29
Theme keywords

PCA, Kmeans, ALR, Gulf of Mexico Loop Current, autoregressive logistic regression, ALR model, Principal Components Analysis, PCA, k-means clustering, Copernicus Marine and Environmental Monitoring Service, CMEMS, Archiving Validation and Interpretation of Satellite Oceanographic Data, AVISO+

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Abstract:

This dataset supports the publication “Statistical simulation of ocean current patterns using autoregressive logistic regression (ALR) models: A case study in the Gulf of Mexico.” (https://doi.org/10.1016/j.ocemod.2019.02.010) This dataset includes historical dynamic topography and surface currents from satellite altimetry (1991-01-01 to 2017-08-29) as well as processed views of the Gulf of Mexico Loop Current. The processed data include a time history of 84 empirical orthogonal functions (EOFs), the results of automated pattern identification (principal component analysis and k-means clustering), and the results of three (3) autoregressive logistic regression (ALR) models.

Suggested Citation:

Chiri, Helios, Ana Julia Abascal, Sonia Castanedo, Jose Antonio A. Antolinez, Yonggang Liu, Robert H. Weisberg, and Raul Medina. 2020. Dataset for: Statistical simulation of ocean current patterns using autoregressive logistic regression (ALR) models: A case study in the Gulf of Mexico. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/VTTJ99YX

Publications:

Chiri, H., Abascal, A. J., Castanedo, S., Antolínez, J. A. A., Liu, Y., Weisberg, R. H., & Medina, R. (2019). Statistical simulation of ocean current patterns using autoregressive logistic regression models: A case study in the Gulf of Mexico. Ocean Modelling, 136, 1–12. doi:10.1016/j.ocemod.2019.02.010

Purpose:

Evaluation of the performance of a statistical framework based on autoregressive logistic regression (ALR) models in recreating the bi-weekly evolution of eight (8) Gulf of Mexico Loop Current pattern states derived from historical altimeter data.

Data Parameters and Units:

Output from the analysis are described in the accompanying README.docx; the description is organized by directory. The historical data are housed in a structure aggregating height (h, [cm]), zonal velocity (u, [m/s]), meridional velocity (v, [m/s]), longitude (lon, [decimal degrees]), latitude (lat, [decimal degrees]), time (Matlab format), mean dynamic topography (mdt, [cm]).

Methods:

Altimetry data from the Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO+) distributed by the Copernicus Marine and Environmental Monitoring Service (CMEMS). Details of the decomposition and classification of the data and the model framework may be found in the publication.

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