Abstract. A thermal parametric model has been developed for analyzing observed regional sea temperature profiles based on a layered structure of temperature fields (mixed layer, thermocline, and deep layers). It contains three major components: (1) a first-guess parametric model, (2) high-resolution profiles interpolated from observed profiles, and (3) fitting of high-resolution profiles to the parametric model. The output of this parametric model is a set of major characteristics of each profile: sea surface temperature, mixed-layer depth, thermocline depth, thermocline temperature gradient, and deep layer stratification. Analyzing nearly 15,000 Yellow Sea historical temperature profiles (conductivity-temperature-depth station, 4825; expendable bathythermograph, 3213; bathythermograph, 6965) from the Naval Oceanographic Office's Master Oceanographic Observation Data Set by this parametric model, the Yellow Sea thermal field reveals dual structure: one layer (vertically uniform) during winter and multilayer (mixed layer, thermocline, sublayer) during summer. Strong seasonal variations were also found in mixed-layer depth, thermocline depth, and thermocline strength.

Abstract. This paper presents an analysis on the space/time statistical thermal structure in the Yellow Sea from the Navy's Master Observation Oceanography Data Set during 1929-1991. This analysis is for the establishment of an Optimum Thermal Interpolation System of the Yellow Sea (a shallow sea), for the assimilation of observational data into coastal o-coordinate ocean prediction models (e.g., the Princeton Ocean Model), and for the design of an optimum observational network. After quality control the data set consists of 35,658 profiles. Sea surface temperatures at 50% and 80% water depths are presented here as representing the thermal structure of surface, middepth, and nearbottom layers. In the Yellow Sea shelf the temporal and spatial signals fluctuate according to the Asian monsoon. Variation of surface forcing from winter to summer monsoon season causes the change of the thermal structure, including the decorrelation scales. Our computation shows that the seasonal variation of the surface horizontal decorrelation scale is around 90 km from 158 km in winter to 251 km in summer and the seasonal variation of the surface temporal decorrelation scale is around 2.4 days from 14.7 days in winter to 12.3 days in summer. The temporal decorrelation scale increases with depth in both summer (evident) and winter (slight). The near-bottom water (rr -0.8) has the longest temporal scale in summer, which could be directly related to the existence of the Yellow Sea Cold Water throughout the summer in the middle of the Yellow Sea. The temporal and spatial decorrelation scales obtained in this study are useful for running optimum interpolation models and for designing an optimum observational network. The minimum sampling density required to detect thermal variability in the Yellow Sea shelf would be 50-80 km and 4-6 day intervals per temperature measurement with the knowledge that the subsurface features will also be adequately sampled.

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This paper investigates the acoustic uncertainty due to hydrographic data error and in turn to determine the necessity of a near real time ocean analysis capability such as the Naval Oceanographic Office's (NAVOCEANO) Modular Ocean Data Assimilation System (MODAS) model in shallow water (such as the Yellow Sea) mine hunting applications using the Navy's Comprehensive Acoustic Simulation System / Gaussian Ray Bundle (CASS/GRAB) model. To simulate hydrographic data uncertainty, Gausiantype errors (produced using the random number generator in MATLAB) with zero mean and three standard deviations (1 m/s, 5 m/s, and 10 m/s) are added to the sound profile. It is found that the acoustic uncertainty depends on the location of the error and sound sources. It is more sensitive to errors in the isothermal structure in the winter than in the layered structure in the summer.

This paper investigates the acoustic uncertainty due to hydrographic data error and in turn to determine the necessity of a near real time ocean analysis capability such as the Naval Oceanographic Office's (NAVOCEANO) Modular Ocean Data Assimilation System (MODAS) model in shallow water (such as the Yellow Sea) mine hunting applications using the Navy's Comprehensive Acoustic Simulation System / Gaussian Ray Bundle (CASS/GRAB) model. To simulate hydrographic data uncertainty, Gausiantype errors (produced using the random number generator in MATLAB) with zero mean and three standard deviations (1 m/s, 5 m/s, and 10 m/s) are added to the sound profile. It is found that the acoustic uncertainty depends on the location of the error and sound sources. It is more sensitive to errors in the isothermal structure in the winter than in the layered structure in the summer.