Variability and Sensitivity of Coupled Mixed Layer-Acoustic Model Systems.

Abstract

This study is the first reported analysis of coupled mixed layer-acoustic model systems. The analysis emphasizes the performance of the combined systems rather than the acoustic or ocean models separately. Acoustic variability of the coupled model systems was studied in terms of the median detection range (MDR). Synoptic time variations of MDR as a function of figure of merit, frequency and receiver depth were analyzed during the month of May 1980 at OWS 'Papa' in order to provide a better insight into the operational capabilities of model systems to accurately represent the actual oceanic variability. The results of this limited analysis revealed that the model systems displayed more day-to-day acoustic (MDR) variability than did direct environmental input (BT). The capability to accurately model the thermal structure was reviewed with the following results. No significant correlation was observed between the EOTS model and the actual BT mixed layer depths while there appeared to be a strong positive correlation between the ODT model (driven by atmospheric forcing) and the BT mixed layer depths.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1981
Accession Number
ADA102659

Entities

People

  • Calvin R. Dunlap
  • Roland W. Garwood Jr.
  • Rory H. Fisher

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Acoustics
  • Data Sets
  • Databases
  • Detection
  • Figure Of Merit
  • Frequency
  • Grids
  • Heat Flux
  • Measurement
  • Model Theory
  • Oceanography
  • Sea Surface Temperature
  • Solar Radiation
  • Surface Temperature
  • Temperature Gradients
  • United States Naval Academy
  • Weather Stations

Readers

  • Acoustical Oceanography.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Regression Analysis.