Predictability of the Loop Current Variation and Eddy Shedding Process in the Gulf of Mexico Using an Artificial Neural Network Approach

Abstract

Abstract A novel approach based on an artificial neural network was used to forecast sea surface height (SSH) in the Gulf of Mexico (GoM) in order to predict Loop Current variation and its eddy shedding process. The empirical orthogonal function analysis method was applied to decompose long-term satellite-observed SSH into spatial patterns (EOFs) and time-dependent principal components (PCs). The nonlinear autoregressive network was then developed to predict major PCs of the GoM SSH in the future. The prediction of SSH in the GoM was constructed by multiplying the EOFs and predicted PCs. Model sensitivity experiments were conducted to determine the optimal number of PCs. Validations against independent satellite observations indicate that the neural network–based model can reliably predict Loop Current variations and its eddy shedding process for a 4-week period. In some cases, an accurate forecast for 5–6 weeks is possible.