Patterns of ocean current variability on the West Florida Shelf using the self‐organizing map

Abstract

[1] Patterns of ocean current variability are examined on the West Florida Shelf by a neural network analysis based on the self-organizing map (SOM), using time series of moored velocity data that span the interval October 1998-September 2001. Three characteristic spatial patterns are extracted in a 3 Â 4 SOM array: spatially coherent southeastward and northwestward flow patterns with strong currents and a transition pattern of weak currents. On the synoptic weather timescale the variations of these patterns are coherent with the local winds. On the seasonal timescale the variations of the patterns are coherent with both the local winds and complementary sea surface temperature patterns. The currents are predominantly southeastward during fall-winter months (from October to March) and northwestward during summer months (from June to September). The spatial patterns extracted by the (nonlinear) SOM method are asymmetric, a feature that is not captured by the (linear) empirical orthogonal function method. Thus we find for the synoptic weather and longer timescales that (1) southeastward currents are generally stronger than northwestward currents, (2) the coastal jet axis is located further offshore for southeastward currents than for northwestward currents, and (3) the velocity vector rotations with depth are larger in shallower water when the currents are southeastward relative to when the currents are northwestward.

Unsupervised model-based clustering for typological classification of Middle Bronze Age flanged axes

“…They allow representation of a multidimensional dataset by nonlinear projection of artefacts in a lower dimension space, usually represented by discrete locations in a regular 2D lattice. Despite the loss of linearity in the output space, the topological relationships between objects (the order of the distances) are preserved (Liu and Weisberg, 2005). In our case, the use of SOMs can provide a first, exploratory step for further clustering of large datasets (Yin, 2008).…”

Wind regimes and their relation to synoptic variables using self-organizing maps

“…The SOM PAK can handle missing data points (Kohonen et al, 1996;Samad and Harp, 1992). Allowing gappy input data is one of the many advantages of the SOM over other conventional methods (Liu and Weisberg, 2005). The SOM PAK routines compute the distance calculations and reference vector modification steps using the available data components.…”

A Review of Self-Organizing Map Applications in Meteorology and Oceanography

“…Since the introducion and demonstration of the use of the SOM to the oceanography community by Richardson et al (2003), SOM applications have been steadily increased in physical oceanography (e.g., Risien et al, 2004;Liu & Weisberg, 2005Leloup et al, 2007Leloup et al, , 2008Iskandar et al, 2008), and other disciplinary of oceanography as well (e.g., Chazottes et al, 2006Chazottes et al, , 2007Telszewski et al, 2009). The SOM is used in analyzing many kinds of oceanographic data, such as satellite ocean color, chlorophyll, sea surface temperature, sea surface height, in situ and modeled ocean currents, etc (Table 2).…”

“…For instance, it is estimated that less than 5% of all remotely sensed images are ever viewed by human eyes or actually used (Petrou, 2004). Also, accurately extracting key features and characteristic patterns of variability from a large data set is vital to correctly understanding the interested ocean and atmospheric processes (e.g., Liu & Weisberg, 2005). With the increasing quantity and type of data available in meteorological and oceanographic research there is a need for effective feature extraction methods.…”

“…Nowadays, the SOM is often used as a statistical tool for multivariate analysis, because it is both a projection method that maps high dimensional data to lowdimensional space, and a clustering and classification method that order similar data patterns onto neighboring SOM units. SOM applications are becoming increasingly useful in geosciences (e.g., Liu and Weisberg, 2005), because it has been demonstrated to be an effective feature extraction technique that has many advantages over conventional data analysis method (e.g., Liu et al 2006a). The present paper serves as a survey of the SOM applications in meteorology and oceanography community.…”