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Towards Automated Detection of Gulf Stream

North Wall From Concurrent Satellite

Images

Avijit GangopadhyayJeffrey Rezendes

Kevin LydonRamprasad Balasubramanian

Iren Valova

Outline

• Feature Oriented Regional Modeling System (FORMS) for the Western North Atlantic

• Gulf Stream path – issues• Manual Extraction process• Using SSH and SST• Neural Network Ideas• Back to SSH and SST • Future Pathways

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Synoptic Ocean Prediction

• Ocean Prediction is an Initial value problem

• Features define the Initial State

• Examples of Features: Fronts, Eddies, Jets, Upwelling, Cold pools

• Time-scale of prediction: days-to-weeks

Gulf Stream Front, Eddies, Jets

Features in Western North Atlantic

• Gulf Stream• Warm Core Rings• Cold Core Rings• Southern Recirculation Gyre• Northern Recirculation Gyre• Deep Western Boundary

Current• Gangopadhyay et al., 3-part series

in 1997: Journal of Atmospheric and Oceanic Tech. (14) 1314:1365

• Maine Coastal Current• NEC Inflow• GSC Outflow• Jordan Basin Gyre• Wilkinson Basin Gyre• Georges Basin Gyre• Georges Bank Gyre• Tidal Mixing Front• Gangopadhyay et al. 2003: CSR

23 (3-4) 317-353 • Gangopadhyay and Robinson,

2002: DAO 36(2002) 201-232

Deep Sea region (GSMR) Coastal region (GOMGB)

Gulf Stream Front, Eddies, Jets

In general, a coastal current (CC), a front (SSF) and an eddy/gyre (E/G) are represented by:

CC: TM(x, η, z) =TMa(x, z)+ αM(x, z) M(η)

 SSF: Tss(x, y, z) = Tsh (x, z) + (Tsl (x, z) – Tsh(x, z)) (, z)

 E/G: T(r, z) = Tc (z) - [Tc (z) - Tk (z) ] {1-exp(-r/R)}

 where, TM

a(x, z), Tsh (x, z) and Tc (z) are axis, shelf and core

(η) = (0 W)

(, z) = ½ + ½ tanh[(-.Z)/] 

This is what is called “Feature Modeling”

Data and Feature Models

Numerical Model Initialization and

Forecast

Brown et al. (2008a, b), IEEE JOE

SST

Feature Model

July 30, 2001

FORMS Protocol

• Identify Circulation and Water mass features• Regional Synthesis -- Processes from a modeling

perspective• Synoptic Data sets -- in-situ and satellite• Regional Climatology (Background Circulation)• Multiscale Objective Analysis (Climatology +

Feature Models)• Simulation -- Nowcasting/Forecasting• Assimilation

Gulf Stream path identification -- Issues

• Historically, we have looked at SST for guidance on the North Wall

• Similar SST gradients exist elsewhere• Gulf Stream NW does not have a single

isotherm signature on the surface• Clouds• Eddies convolute the path• Large amplitude meandering to the east

often segmented11

Two Approaches

• Dynamics – Based (SST, SSH, other derived fields)

• Neural Network – Learning from the past observed paths and applying to the detection

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Identifying Features

• Sea Surface Temperature (SST)• Sea Surface Height (SSH)• Sea Surface Velocity (SSUV)

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Sea Surface Temperature

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Sea Surface Height

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Sea Surface Velocity

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Extraction by Manual Operator

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Extraction by Isoheight contouring Works better

than using SST

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Approach 2 Neural NetworkMultilayer Perceptron (MLP)

•Type of neural network• Classification technique based on animals’ central

nervous systems•Feed forward network• Input values passed through one or more hidden

layers• Hidden layers connected between input and output

buffers•Sigmoid function applied in hidden layers•Connections between nodes in layers are

weighted•Supervised learning by backpropagation

Multilayer Perceptron visualized

The network visualized

Results visualized

• Blue dots show all points classified by network as part of GSNW

• Black line is constructed from average latitude of all blue points for a longitude

• Red line is the manual, expert-plotted line

Results visualized cont.

Conclusions• Poor results overall• Lack of variation • Indicates a possible overfitting of the

network• Overfitting results when a network

fits its output too closely to its training data

• Too many points• Possibly too low requirements for

classifying points as part of GSNW

Future plans for Neural Networks• New approach: clustering

• Used successfully in the past for feature detection• GSNW is a feature with distinct attributes

• More conducive to visual validation of results• As opposed to automated training of MLP

• Could allow for identification of entire Gulf Stream as a feature

• Takes context of points into account in a way that MLP does not

Back to Dynamics

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Coming Back to SSHA Validation

0.35 m isoheoght contour

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0.50 m isoheight contour

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Difference between GSNW and Axis

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Nowcast -- October 12, 2015

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Forecast 20 October 2015

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Future Directions with SSHA• Use the 0.5 m isoheight contour to identify a

near-axis stream path. Explore seasonality.• Use the zero-vorticity line to converge on a

finer isoheight contour (closer to the axis).• Use a parametric model (offset-curvature

dependence) to extract the North Wall• Validate and verify with concurrent SST and

SSC • Develop a mixed isoheight-zero vorticity

algorithm for eddies

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Thank You!

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