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Ocean reanalysis data-driven machine learning prediction of Loop Current eddy movements in the Gulf of Mexico

Anna B. Lowe, Tianning Wu, Ruoying (Roy) He

North Carolina State University

NASA Perpetual Ocean

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My research background

Carmel Bay’s circulation

Rockfish larval transport

Lagrangian transport near fronts

ML to identify eddies

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Loop Current sheds eddies in the Gulf of Mexico

Loop Current Eddies (LCE):

150 km diameter
1,000 m deep
Strong, circular flow (4 knots)

He et al (2003); He et al. (2004); He et al (2005), He and Wilkin (2006), He and McGillicuddy (2008), He et al. (2008); Li and He (2009); Chen and He (2010);

Hyun and He (2010), He et al. (2011 a, b); Yao et al (2012); Zhao and He (2012); He et al. (2018); Zeng and He (2018)

NRL

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Over 4,000 oil platforms in the Gulf of Mexico affected by LCE eddies
Eddies affect: worker safety, drilling and production operations, structures, spill cleanup
$100Ms/year lost to oil rig downtime

Loop Current Eddies threaten safety of oil operations

Eddy

Deep Water Horizon 2010

TX

LA

MS

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Purpose of this study

Develop accurate prediction process of Loop Current Eddy (LCE) shedding process and movement through the oil fields

Industry needs:

long-range forecast (~3 months)
Short-range forecast (days-hours)

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CNAPS model output

Coupled Northwest Atlantic Prediction System (CNAPS)
EnKF data assimilated case
~4 km horizontal resolution

NW Atlantic site: http://omgsrv1.meas.ncsu.edu:8080/ocean-circulation2

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27 years of daily average fields (1993-2020)

Training data: 17 years (1993-2010)
Validation data: 5 years (2010-2015)
Testing data: 5 years (2015-2020)

Surface Variables:

SST
SSH
u- and v-velocity / speed
relative vorticity

UNET model setup

Weyn et al. (2020)

Batch size = 32

Filter num = 16

Epochs = 100

One of the great things about the CNAPS model output is that we have 35 years of daily average fields spanning 1980 to 2015. We divide the model output into 3 categories: the training data includes the first 25 years, the validation data includes 5 years, and the testing data is also 5 years.

In our model, we include 5 surface variables as our predictors: the SST, SSH, speed, relative vorticity, and horizontal divergence calculated from the flow fields.

We train our UNET model using the 25 years of training model output, and then analyze how well that trained model does on identifying the loop current and eddies from the five surface predictor variables in the validation data. Of course the last dataset, the testing data, will not be used until the very end.

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Work in Progress

Industry desires a 3-month forecast

When do the eddies form?
Where will they travel?
How strong when they reach the oil fields?

Create a prediction tool that industry will use by harnessing AI2ES partnerships

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THANK YOU!

Advisor: Ruoying (Roy) He

NCSU OOMG Lab Members: Tianning Wu, Joe Zambon

AI2ES Project Collaborators: Ryan Lagerquist, Randy Chase, Christopher Wirz, Mariana Cains, Julie Demuth, Imme Ebert-Uphoff

Funding generously provided by the National Science Foundation