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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 1
Ann. Geophys., 15, 101–112, 1997
https://doi.org/10.1007/s00585-997-0101-7
© European Geosciences Union 1997
Ann. Geophys., 15, 101–112, 1997
https://doi.org/10.1007/s00585-997-0101-7
© European Geosciences Union 1997

  31 Jan 1997

31 Jan 1997

Ocean feature models − their use and effectiveness in ocean acoustic forecasting

J. Small, L. Shackleford, and G. Pavey J. Small et al.
  • Defence Research Agency, Building A22, Winfrith Technology Centre, Winfrith Newburgh, Dorchester, Dorset, DT2 8XJ, UK

Abstract. The aim of this paper is to test the effectiveness of feature models in ocean acoustic forecasting. Feature models are simple mathematical representations of the horizontal and vertical structures of ocean features (such as fronts and eddies), and have been used primarily for assimilating new observations into forecasts and for compressing data. In this paper we describe the results of experiments in which the models have been tested in acoustic terms in eddy and frontal environments in the Iceland Faeroes region. Propagation-loss values were obtained with a 2D parabolic-equation (PE) model, for the observed fields, and compared to PE results from the corresponding feature models and horizontally uniform (range-independent) fields. The feature models were found to represent the smoothed observed propagation-loss field to within an rms error of 5 dB for the eddy and 7 dB for the front, compared to 10–15-dB rms errors obtained with the range-independent field. Some of the errors in the feature-model propagation loss were found to be due to high-amplitude 'oceanographic noise' in the field. The main conclusion is that the feature models represent the main acoustic properties of the ocean but do not show the significant effects of small-scale internal waves and fine-structure. It is recommended that feature models be used in conjunction with stochastic models of the internal waves, to represent the complete environmental variability.

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