1. 1 Towards Automated Detection of Gulf Stream North Wall From Concurrent Satellite Images Avijit Gangopadhyay Jeffrey Rezendes Kevin Lydon Ramprasad Balasubramanian Iren Valova

2. 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 2

3. 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

4. Gulf Stream Front, Eddies, Jets 

5. 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)

6. Gulf Stream Front, Eddies, Jets 

7. In general, a coastal current (CC), a front (SSF) and an eddy/gyre (E/G) are represented by: CC:T M (x, η, z) =T M a (x, z)+ α M (x, z)  M (η) SSF:T ss (x, y, z) = T sh (x, z) + (T sl (x, z) – T sh (x, z))  ( , z) E/G:T(r, z) = T c (z) - [T c (z) - T k (z) ] {1-exp(-r/R)} where, T M a (x, z), T sh (x, z) and T c (z) are axis, shelf and core  (η) =  (0    W)  ( , z) = ½ + ½ tanh[(  - .Z)/  ] This is what is called “Feature Modeling”

8. Data and Feature Models Numerical Model Initialization and Forecast Brown et al. (2008a, b), IEEE JOE

9. SST Feature Model July 30, 2001

10. 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

11. 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 segmented 11

12. Two Approaches Dynamics – Based (SST, SSH, other derived fields) Neural Network – Learning from the past observed paths and applying to the detection 12

13. 13

14. Identifying Features Sea Surface Temperature (SST) Sea Surface Height (SSH) Sea Surface Velocity (SSUV) 14

15. Sea Surface Temperature 15

16. Sea Surface Height 16

17. Sea Surface Velocity 17

18. Extraction by Manual Operator 18

19. Extraction by Isoheight contouring Works better than using SST 19

20. Approach 2 Neural Network Multilayer 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

21. Multilayer Perceptron visualized

22. The network visualized

23. 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

24. Results visualized cont.

25. 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

26. 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

27. Back to Dynamics 27

28. 28

29. Coming Back to SSHA Validation 0.35 m isoheoght contour 29

30. 0.50 m isoheight contour 30

31. Difference between GSNW and Axis 31

32. Nowcast -- October 12, 2015 32

33. Forecast 20 October 2015 33

34. 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 34

35. Thank You! 35