Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

Abstract

Abstract-In this paper, a method for merging partial overlapping time series of ocean profiles into a single time series of profiles using empirical orthogonal function (EOF) decomposition with the objective analysis is presented. The method is used to handle internal waves passing two or more mooring locations from multiple directions, a situation where patterns of variability cannot be accounted for with a simple time lag. Data from one mooring are decomposed into linear combination of EOFs. Objective analysis using data from another mooring and these patterns is then used to build the necessary profile for merging the data, which is a linear combination of the EOFs. This method is applied to temperature data collected at a two vertical moorings in the 2006 New Jersey Shelf Shallow Water Experiment (SW06). Resulting profiles specify conditions for 35 days from sea surface to seafloor at a primary site and allow for reliable acoustic propagation modeling, mode decomposition, and beamforming.

Long distance passive localization of vocalizing sei whales using an acoustic normal mode approach

“…In addition, there is also a seasonal thermocline present that will account for strong variability in the water column and thus is important to the propagation. Figure 3 shows a time series of the water column sound speed at the receiver array, as obtained by moored oceanographic sensors (Lin et al, 2010). In this time series, we observed (Fig.…”

“…To do this, we must first create replica mode functions at the receiver location to correlate against. These mode functions are created by using full water-column sound speed profile (SSP) measurements (Lin et al, 2010) and a homogenous bottom geoacoustic model at the receiver as input into a standard acoustic normal mode program (in this case KRAKEN (Porter, 1991). We assumed a 150 m sediment layer thickness with 1640 m/s sound speed, 1.9 g/cm 3 density, and 0.2 dB/k attenuation over a 1740 m/s basement.…”

A Coupled-Mode Shallow-Water Model for Tidal Analysis: Internal Tide Reflection and Refraction by the Gulf Stream

“…During these experiments, numerous oceanographic and acoustic measurements were collected from July through September 2006 (Newhall et al 2007;Lynch and Tang 2008;Colosi et al 2012), and primitive equation forecasts with data assimilation, reanalyses, and adaptive sampling recommendations were issued in real time using the Multidisciplinary Simulation, Estimation, and Assimilation System (MSEAS; Lermusiaux et al 2006;Haley and Lermusiaux 2010;Lin et al 2010;Colin et al 2013). Afterward, more than 1400, implicit, two-way-nested primitive equation reanalyses were completed to improve all aspects of the field estimation.…”

Horizontal refraction of propagating sound due to seafloor scours over a range-dependent layered bottom on the New Jersey shelf

“…Salinity was spatially interpolated from an environmental mooring, approximately 1.5 km to the west-northwest of the Shark VLA at 39 07.175 0 N, 73 16.640 0 W, by fitting the temperature data to the temperature versus salinity curve (T-S curve). 32 Although range-dependent water-column sound-speed profiles and rough sea surface conditions are known to cause significant 3-D effects, 1-3,33-36 a single, averaged profile and flat sea surface are used in this work so that the modeled effects are due to variability of the topography and subbottom structure alone with a baseline vertical refraction due to the mean sound speed gradient.…”