Abstract
Patterns of variability and the dynamics of the ocean circulation on the West Florida Shelf (WFS) are investigated using multi-year, shelf-wide oceanographic observations from moored Acoustic Doppler Current Profiler (ADCP) arrays,hydrographic cruises, High-Frequency (HF) radars, satellites, and coastal tide gauges.Novel neural network techniques, Self-Organizing Map (SOM) and Growing Hierarchical Self-Organizing Maps (GHSOM), are introduced as feature extraction methods in physical oceanography. The SOM is demystified and demonstrated to be a useful feature extraction method in a series of performance evaluations using artificial data sets comprising known patterns. It is then applied to velocity time series from moored ADCP arrays and to a joint HF-radar and ADCP data set, respectively, to extract patterns of ocean current variability, and it is shown to be a useful technique for extracting dynamically consistent ocean current patterns. The extracted characteristic patte
rns of upwelling/downwelling variability are coherent with the local winds on the synoptic weather time scale, and coherent with both the local winds and thecomplementary Sea Surface Temperature (SST) patterns on the seasonal time scale. Thecurrents are predominantly southeastward during fall-winter and northwestward during summer. The GHSOM is used to describe the SST seasonal variation. As feature extraction methods, both the SOM and the GHSOM have advantages over the conventional Empirical Orthogonal Function method.The circulation dynamics are examined, first through depth-averaged momentum balances at selected locations and then via sea surface height (SSH) estimates across the inner shelf. Dominant dynamics of the shelf circulation are diagnosed and a method is discussed for estimating along-shelf currents from coastal sea level and wind data. Nontidal coastal sea level fluctuations are related to both the offshore SSH and the dynamical responses of the inner shelf to wind and bu
oyancy forcing. The across-shelf distribution of the SSH is estimated from the velocity, hydrography, wind, and coastal sea level data.Subtracting the variability that may be accounted for by inner shelf dynamical responses yields a residual at the 50 m isobath that compares well with satellite altimetry data. This suggests the possibility of calibrating satellite SSH data on the continental shelf.
Original language | American English |
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Journal | Default journal |
State | Published - Jun 1 2006 |
Keywords
- Coastal oceanography
- Continental shelf dynamics
- Observations
- Time series
- Data analysis
- Neural network
- Growing hierarchical self-organizing maps
Disciplines
- American Studies
- Arts and Humanities