• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.183-191
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• 2021

A time-series variation of temperature, salinity, and underwater sound speed was analyzed using an Array for Real-time Geostrophic Oceanography (ARGO) float which autonomously collects temperature and salinity for about 10month with 2 days cycle among 12 floats in the center of the Yellow Sea. As a result, the underwater sound channel appeared below the thermocline as the surface sound channel, which is dominant in the winter season, reduced in April. Besides, for a certain time in the spring season, the sound ray reflected the sea surface frequently due to the short-term temperature inversion effect. Based on the case of successful observation of ARGO float in the shallow water, using prolonged monitoring unmanned platform may contribute to predicting sound transmission loss if the temperature inversion and sound channel including background environment focusing are investigated in the center of the Yellow Sea.

• Atmosphere
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• v.14 no.1
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• pp.4-23
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• 2004

To monitor the world's oceans and understand the role of the oceans for climate change, an Array for Real-time Geostrophic Oceanography (ARGO) program has been carried out since year 2000. Autonomous profiling floats of about 820 are reporting the vertical temperature, salinity, and pressure profiles of the upper 2000 m underwater at regular time intervals. Meteorological Research Institute (METRI) of Korea Meteorological Administration (KMA) launched 45 floats at the East Sea and the western Pacific to understand characteristics of water properties and develop the global ocean observation system as a part of international cooperation project. In this study, we introduce ARGO program, METRI-ARGO and the features of APEX float itself and their data formats. We also describe the significant points to be considered for using ARGO data.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.293-304
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• 2024

In this paper, efficient and reliable methodologies for conducting sea trials to evaluate the performance of hull-mounted sonar systems is discussed. These systems undergo performance verification during ship construction via sea trials. However, the evaluation procedures often lack detailed consideration of variabilities in detection performance due to seabed topography, seasonal factors. To resolve this issue, temperature and salinity structure data were collected from 1967 to 2022 using ARGO floats and ocean observers data. The paper proposes an efficient and reliable sea trial method incorporating Bellhop modeling. Furthermore, a machine learning model applying a Physics-Informed Neural Networks was developed using the acquired data. This model predicts the sound speed profile at specific points within the sea trial area, reflecting seasonal elements of performance evaluation. In this study, we predicted the seasonal variations in sound speed structure during sea trial operations at a specific location within the trial area. We then proposed a strategy to account for the variability in detection performance caused by seasonal factors, using results from Bellhop modeling.