Loop Current

In the last decade, deep learning models have been successfully applied to a variety of applications and solved many tasks. The ultimate goal of this study is to produce deep learning models to improve the skills of forecasting ocean dynamic events in general and those of the Loop Current (LC) system in particular. A specific forecast target is to predict the geographic location of the (LC) extension and duration, LC eddy shedding events for a long lead time with high accuracy. Also, this study aims to improve the predictability of velocity fields (or more precisely, velocity volumes) of subsurface currents. In this dissertation, several deep learning based prediction models have been proposed. The core of these models is the Long-Short Term Memory (LSTM) network. This type of recurrent neural network is trained with Sea Surface Height (SSH) and LC velocity datasets. The hyperparameters of these models are tuned according to each model's characteristics and data complexity. Prior to training, SSH and velocity data are decomposed into their temporal and spatial counterparts.A model uses the Robust Principle Component Analysis is first proposed, which produces a six-week lead time in forecasting SSH evolution. Next, the Wavelet+EOF+LSTM (WELL) model is proposed to improve the forecasting capability of a prediction model. This model is tested on the prediction of two LC eddies, namely eddy Cameron and Darwin. It is shown that the WELL model can predict the separation of both eddies 10 and 14 weeks ahead respectively, which is two more weeks than the DAC model. Furthermore, the WELL model overcomes the problem due to the partitioning step involved in the DAC model. For subsurface currents forecasting, a layer partitioning method is proposed to predict the subsurface field of the LC system. A weighted average fusion is used to improve the consistency of the predicted layers of the 3D subsurface velocity field. The main challenge of forecasting of the LC and its eddies is the small number of events that have occurred over time, which is only once or twice a year, which makes the training task difficult. Forecasting the velocity of subsurface currents is equally challenging because of the limited insitu measurements.

The Gulf of Mexico (GoM) contains a variety oceanographic features including;
the Loop Current, cyclonic/anticyclonic eddies, common water, and the Mississippi River
Plume. The relationship these features have on the community assemblages of Families
Lutjanidae and Serranidae has been of great interest from both biological and economic
standpoints. These families represent some of the most economically important fisheries
in the GoM. Identifying the role these features play in the transportation of larval and
juvenile nearshore species to offshore environments is vital to resource managers. Using
data collected shortly after the Deepwater Horizon Oil Spill via the NOAA Natural
Resource Damage Assessment in 2011 as well as cruises conducted by the Deep Pelagic
Nekton Dynamics of the Gulf of Mexico (DEEPEND) Consortium from 2015-2017, the
faunal composition and abundance of these families were analyzed with respect to
seasonality, oceanographic features, depth distribution, and time.