Identifying prototype states within hydrodynamic model outputs using a self-organizing feature map

Abstract

The Coastal Environmental Modelling Team at CSIRO Marine and Atmospheric Research, in Hobart, Tasmania, Australia, has been modelling hydrodynamic conditions within the estuarine environment of south-eastern Tasmania for several years. Historical model output has been analysed in an effort to identify prototype hydrodynamic states (i.e., frequently encountered typical hydrodynamic situations) exhibited by the estuarine environment over that period. A competitive-learning neural network, the Self-Organizing Feature Map (SOM), was used to identify these prototype states. Once such a network has been trained, each node in its output layer represents a particular pattern in the input data and nodes representing similar patterns are located near to each other on the two-dimensional output grid, while those representing dissimilar patterns are further apart. Estimated daily average surface hydrodynamic conditions (salinity, temperature and ocean current components) within the south-east Tasmanian estuarine environment, from August 2009 to August 2010, were derived from output provided by the hydrodynamic model. The current components were then analysed using a SOM and subsequent inspection of the SOM output grid enabled a number of prototypical hydrodynamic states to be identified within the model outputs.