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Wind and density driven flow along the Texas-Louisiana continental shelf

This study focuses on predicting wind-driven surface currents along the Texas-Louisiana continental shelf, exploring the impacts of wind and buoyancy on the flow patterns. The research showcases the use of models and highlights the importance of nesting for skill improvement. The findings suggest that despite unpredictable small-scale eddies, the large-scale plume structure can be reproduced without data assimilation.

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Wind and density driven flow along the Texas-Louisiana continental shelf

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  1. Wind and density driven flow along the Texas-Louisiana continental shelf • Rob Hetland • Zhaoru Zhang • Martino Marta-Almeida • Xiaoqian Zhang

  2. The Gulf of Mexico has a number of environmental problems: Oil spills And the list goes on.... Harmful algal blooms NASA MODIS May 24, 2010 Bottom hypoxia Karenia brevis Photo credit: Florida Fish and Wildlife Conservation Commission LUMCON - July 24-27, 2012

  3. Series of Models: Louisiana shelf wind/buoyancy driven flow NEXT TALK Wind-driven surface current predictions for entire gulf – focused on TX shelf TX/LA shelf wind/buoyancy driven flow

  4. Animation courtesy Chris Barker (NOAA R&R)Surface currents provided by Rob Hetland and Steve Baum, TAMUFunding by the Texas General Land Office TABS program

  5. B D F J K N R V W

  6. ~3 hr lag ~12 hr lag

  7. Four examples of non-summer convergent events

  8. Wind Currents

  9. No river case

  10. Wind Obs. Model 2006 2009 10 year mean

  11. Climatological winds over the Texas-Louisiana shelf

  12. Seasonal surface currents from LATEX moorings Non-summer mean Summer mean Cho et al. (JGR, 1998)

  13. JFM mean density cross-section Thermal wind balance currents u|z=h=0 JFM mean along-shore currents Along-shore currents are nearly in thermal-wind balance

  14. Meade et al. (1995) USGS Circular 1133

  15. Low flow year

  16. Medium flow year

  17. High flow year

  18. Every third grid point shown HYCOM IASNFS (subset) NGOM

  19. Model salinity skill Dataset Parent models CLM Nested models Red = good (within 10% of max skill)

  20. where

  21. Perturbed simulations (±5% wind and rivers)

  22. Noise at a point on the shelf Domain average noise

  23. Conclusions: Nesting improves model skill, but it does not appear to matter which model is used*. Although there is significant unpredictable, small-scale eddies at the submesoscale, the large scale plume structure is reproducible in a model without data assimilation. *For salinity, anyways...

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