In this study, we will show how ocean surface wave displacement can be estimated from sensors onboard a subsurface moving autonomous underwater vehicle (AUV). The approach is to use a high-resolution vertical accelerometer along with a highresolution pressure sensor mounted coincidently on an AUV. We apply this approach to data collected in summer 2005, in northeast Monterey Bay, CA, during an engineering experiment as a part of the Layered Organization in the Coastal Ocean (LOCO) program. The AUV used was the School for Marine Science and Technology, University of Massachusetts Dartmouth T-REMUS vehicle. Data were collected while the vehicle traveled at a constant speed 1.2 m/s and a constant depth of 10 m in water of depth 19.5 m. The local wind-generated surface wave field was relatively weak and dominated by surface wave swell of frequencies of 1/12 and 1/6 Hz, respectively. Surface waves of these frequencies in this depth of water have wavelengths of 150 and 55 m, respectively, both values being much larger than the length of the T-REMUS, which is 2 m. This condition, along with the fact that the AUV is moving over an order of magnitude slower than the phase speed associated with these surface waves, allows estimation of the frequency spectrum of surface waves by sensors onboard the AUV as well as interpretation of how the AUV responds to the surface wave field. From our estimation technique, we have confirmed that the two frequencies of 1/12 and 1/6 Hz were the dominant surface wave frequencies present and that the AUV-based estimated spectral values agreed very closely with in situ observations made by a fixed slow rise profiler located 200 m away. Pitch spectra indicated that the AUV responded to the higher frequency swell component of 1/6 Hz by adjusting its depth to try to follow the surface-wave-induced pressure. At the lower frequency, the AUV tended to follow the surface wave coherently.

Measurements of turbulence were performed in four frontal locations near the mouths of Block Island Sound (BIS) and Long Island Sound (LIS). These measurements extend from the offshore front associated with BIS and Mid-Atlantic Bight Shelf water, to the onshore fronts near the Montauk Point (MK) headland, and the Connecticut River plume front. The latter feature is closely associated with the major fresh water input to LIS. Turbulent kinetic energy (TKE) dissipation rate, epsilon, was obtained using shear probes mounted on an autonomous underwater vehicle. Offshore, the BIS estuarine outflow front showed, during spring season and ebb tide, maximum TKE dissipation rate, epsilon, estimates of order 10(-5) W/kg, with background values of order 10(-6) to 10(-9) W/kg. Edwards et al. [Edwards, C.A., Fake, T.A., and Bogden, P.S., 2004a. Spring-summer frontogenesis at the mouth of Block Island Sound: 1. A numerical investigation into tidal and buoyancy-forced motion. Journal of Geophysical Research 109 (C12021), doi:10.1029/2003JC002132.] model this front as the boundary of a tidally driven, baroclinically adjusted BIS flow around the MK headland eddy. At the entrance to BIS, near MK, two additional fronts are observed, one of which was over sand waves. For the headland site front east of MK, without sand waves, during ebb tide, e estimates of 10(-5) to 10(-6) W/kg were observed. The model shows that this front is at the northern end of an anti-cyclonic headland eddy, and within a region of strong tidal mixing. For the headland site front further northeast over sand waves, maximum c estimates were of order 10(-4) W/kg within a background of order 10(-7)-10(-6) W/kg. From the model, this front is at the northeastern edge of the anti-cyclonic headland eddy and within the tidal mixing zone. For the Connecticut River plume front, a surface trapped plume, during ebb tide, maximum epsilon estimates of 10(-5) W/kg were obtained, within a background of 10(-6) to 10(-8) W/kg. Of all four fronts, the river plume front has the largest finescale meansquare shear, S-2 similar to 0.15 s(-2). All of the frontal locations had local values of the buoyancy Reynolds number indicating strong isotropic turbulence at the dissipation scales. Local values of the Froude number indicated shear instability in all of the fronts. Published by Elsevier B.V.

In this study we show that ocean surface wave swell can be measured from a subsurface transiting AUV. Data were obtained onboard an extended REMUS 100, 2m long, which carried a RGL turbulence package and a SeaBird CTD cantilevered off of the port and starboard sides of the bow, respectively. The data reported in this manuscript was obtained in the summer of 2005, in northeast Monterey Bay, as part of the Layered Organization of the Coastal Ocean (LOCO) experiment. The AUV traveled at 1.2 m/s, sampling at 10 m depth, in the region of the 20 m isobath. In the study region, surface wave swell have a wavelength and phase velocity an order of magnitude greater than the AUV length and transit speed, respectively, with swell wave height often no larger than a meter. A simple model for the T-REMUS response to swell displacement is presented. The model allows use of pressure and vertical acceleration measured on board T-REMUS as input parameters. Four legs, both crossing and following the bathymetry, were performed. We observed a well defined peak in the estimated spectra at .08-.09 Hz (T ~ 11.8 s), typical of swell. A secondary peak also occurs at ~.17 Hz (T~ 5.9 s), twice the swell frequency, and corresponds to the AUV pitch response. For ground truth, swell observations where made with a pressure sensor at a fixed station within 2 km of the AUV transit. Comparison between these two data sets showed excellent agreement at the swell peak frequency.

We report observations of the structure of the front that surrounds the plume of the Connecticut River in Long Island Sound (LIS). Salinity, temperature, and velocity in the near‐surface waters were measured by both towed and ship‐mounted sensors and an autonomous underwater vehicle. We find that the plume front extends south from the mouth of the river, normal to the direction of the tidal flow in LIS and then curves to the east to parallel the tidal current. The layer depth at the front and the cross‐front jumps in salinity and near‐surface velocity all tend to decrease as distance from the source increases. This is qualitatively consistent with the prediction of layer models. In the across‐front direction, the plume layer depth increases from zero to the asymptotic value within a few times the plume depth (∼5 m). Vertical motion is generated in this zone, and there is evidence of overturning. Farther from the front, the high‐frequency salinity standard deviation decays exponentially with a length scale of 30 m. Assuming that the salinity fluctuations are a consequence of turbulence, we find that the rate of turbulent kinetic energy dissipation decreases exponentially in the across‐front direction with a decay scale L G ≈ 15 m. Estimates based on AUV‐mounted shear probes are consistent with this estimate. We present an explanation of the physics that determines L G and provide a simple formula to guide the choice of resolution in models that are designed to resolve the frontal structure.

Abstract : LONG-TERM GOALS: To improve AUV mission effectiveness, and the quality of AUV-based scientific research, by quantifying important ambient process contributors to platform noise, drag, and instabilities. OBJECTIVES: (1) To quantify AUV response to water column dynamics through spectral and temporal analysis, with particular attention to (a) swell and surface waves, (2) solitons and internal waves, and (3) currents with transverse components. (2) To provide predictive dynamic models for important processes and platform interactions. Also, To provide detailed suggestions for improvement in vehicle design for platform quietness, stability, improved endurance, controller design/optimization, and interactive intelligent response to ambient processes in the littoral ocean.

The terms of the steady-state, homogeneous turbulent kinetic energy budgets are obtained from measurements of turbulence and fine structure from the small autonomous underwater vehicle (AUV) Remote Environmental Measuring Units (REMUS). The transverse component of Reynolds stress and the vertical flux of heat are obtained from the correlation of vertical and transverse horizontal velocity, and the correlation of vertical velocity and temperature fluctuations, respectively. The data were obtained using a turbulence package, with two shear probes, a fast-response thermistor, and three accelerometers. To obtain the vector horizontal Reynolds stress, a generalized eddy viscosity formulation is invoked. This allows the downstream component of the Reynolds stress to be related to the transverse component by the direction of the finescale vector vertical shear. The Reynolds stress and the vector vertical shear then allow an estimate of the rate of production of turbulent kinetic energy (TKE). Heat flux is obtained by correlating the vertical velocity with temperature fluctuations obtained from the FP-07 thermistor. The buoyancy flux term is estimated from the vertical flux of heat with the assumption of a constant temperature-salinity (T-S) relationship. Turbulent dissipation is obtained directly from the usage of shear probes. A multivariate correction procedure is developed to remove vehicle motion and vibration contamination from the estimates of the TKE terms. A technique is also developed to estimate the statistical uncertainty of using this estimation technique for the TKE budget terms. Within the statistical uncertainty of the estimates herein, the TKE budget on average closes for measurements taken in the weakly stratified waters at the entrance to Long Island Sound. In the strongly stratified waters of Narragansett Bay, the TKE budget closes when the buoyancy Reynolds number exceeds 20, an indicator and threshold for the initiation of turbulence in stratified conditions. A discussion is made regarding the role of the turbulent kinetic energy length scale relative to the length of the AUV in obtaining these estimates, and in the TKE budget closure.

In August 2005, numerous test events were conducted in Narragansett Bay (under adverse, moderate, and high signal-to-noise ratio (SNR) conditions) to validate shallow-water acoustic-based detection, localization, and ranging algorithms against surface craft and divers. These measurements were completed at the Naval Undersea Warfare Center Division Newport's Broadband Ocean Acoustic Laboratory, which is a shallow-water development facility for evolving acoustic and light-based technologies that are of interest to the U.S. Navy in areas such as Force Defense and Port and Harbor Security. It is shown that relatively common ambient environmental conditions in Narragansett Bay (such as wind speeds greater than 15 knots) create adverse acoustic conditions and generally poor target detection performance. As expected, the acoustic-based algorithms performed well at moderate to high values of SNR.

In this manuscript we examine whether measurements obtained from an Autonomous Underwater Vehicle (AUV) equipped with standard microstructure and fine structure sensors can be used to close the Turbulent Kinetic Energy (TKE) and Temperature Variance TV (heat) budgets. Classical turbulence theory is used to estimate the dissipation rate, ε, and the rate of change of the variance of temperature, χ. The turbulent Reynolds stress production and heat flux terms are also obtained directly from measurements. Examination is made of data obtained from an experiment in Narragansett Bay RI in a strong tidally driven stratified shear field. The turbulent field observed, although of limited spatial extent, can be segmented into three different regimes, a strongly turbulent isotropic regime, an anisotropic regime, and a weakly turbulent regime. For these three regimes the TKE budget can be closed within a factor of 2. Closure of the TV budget is not as good, although there appears to be a trend of increasing closure with decreasing turbulence level. This might be explained by the fact that the spatial filtering effect of the finite size of the AUV, being of order 2.3 meters in length, has more of an impact on the calculation of the heat flux term than on the Reynolds stress term. Mixing efficiencies of order .2 are found for all three regimes, with a slight trend of increasing with increasing buoyancy Reynolds number. 1. Background/Introduction Although there is and has been a great deal of interest in oceanography in turbulent mixing, both for application to the subgrid scale parameterization in ocean numerical models and for process studies, direct measurements of mixing are very limited. To obtain the flux (mixing) of momentum, dissipation rate, ε, is typically calculated from turbulent velocity shear measurements and then combined with a local (finescale) value of the vertical shear field. For the temperature (and salinity fields) indirect methods are typically employed to obtain their fluxes, relying on some version of the Osborn (1980) formulation, and assuming a constant mixing efficiency of typically .2. In the past two decades, there have been increased efforts in the laboratory (Ivy et al., 1998, Itsweire et al., 1986, Stillinger et al., 1983) as well as the field (Fleury and Lueck, 1994, Moum, 1990) to obtain directly and simultaneously the fluxes of momentum and heat without recourse to invoking some specific value for mixing efficiency. However, it should be stated that often values of order .2 are obtained in these studies, lending credence to that assumption. Turbulence mixing has been the subject of many reviews, see, for example, Gregg, 1987, Gargett, 1989, and Caldwell and Moum, 1995. For the past thirty years the standard technique of measuring turbulent quantities in the ocean has been by means of microstructure profilers pioneered by Cox et al., 1969, Osborn, 1974, and Gregg et al., 1982. These techniques provide very high-resolution 144 GOODMAN AND LEVINE vertical distribution of turbulent quantities and more recently, with the advent of rapid loosely tether profilers, some horizontal information on the distribution of the turbulent quantities. Efforts are presently underway to obtain fixed-point time series and horizontal sampling of turbulent quantities. For a review of microstructure observational techniques see the special series of articles in the Microstructure Sensors Special Issue (J. Atmos. Oceanic Tech., 16(11), November 1999) and the recent article by Lueck et al. (2002) on velocity microstructure measurements. Horizontal transects of turbulence can resolve structures on scales not resolvable with vertical profiling (Yamazaki et al., 1990). In the past, horizontal sampling, using towed bodies and submarines, has provided unique views of internal waves (Gargett, 1982), salt fingers (Fleury and Lueck, 1992), and turbulence (Osborn, 1985). Vibration measurements taken aboard the NUWC vehicle Large Diameter Unmanned Underwater Vehicle (LDUUV) in Narragansett Bay (Levine and Lueck, 1999) indicated that this platform was sufficiently stable to obtain horizontal measurements of dissipation rate in shallow water. Following this, Levine et al. (2000) demonstrated that a small AUV could also be used to measure the turbulent dissipation rate. This manuscript addresses the question of how much information on the turbulent fields can be obtained from a small AUV equipped with standard fine and microstructure sensors. Although results are particular to one deployment in a very specific setting, aspects of this study should be generalizable to other similar situations. The key issue addressed is whether one can use an AUV to obtain directly measurements of the fluxes of momentum and heat and thus obtain in situ estimates of the eddy viscosity, kν , and the diffusivity kρ, key parameters in mixing studies and numerical models. To perform these types of measurements a variety of assumptions must still be made. It should be noted that the ability to close the turbulent budgets is an important constraint used in assessing the validity of these assumptions and in the validity of the techniques themselves. Following this introduction the manuscript is organized into the following sections: 2. Description of the Turbulence AUV Vehicle, 3. Estimating the TKE and TV Budgets, 4. Results from the Narragansett Bay September 2000 Experiment, and 5. Summary and Conclusions. 2. Description of the Turbulence AUV Vehicle The Turbulence AUV, shown below in Figure 1, performs synoptic microstructure and finestructure measurements (Levine et al. , 2001). It is an extended REMUS vehicle (von Alt et al., 1994) 2.3 m in length, .18 m in diameter, weighing 56 kg in air. With the first-generation turbulence-measuring AUV, we are currently limited to the mid-water column, for safety, and to an endurance of 4 hours using rechargeable lead-acid batteries. However, modifications to the internal frame enable rapid redeployment with fresh batteries. Figure 1. Turbulent REMUS AUV Sensors include two FSI CTDs, an upwardand downward-looking 1.2 MHz ADCP, a University of Victoria Turbulence Package (two orthogonal thrust probes, three accelerometers and one FP07 fast response thermistor), and a SONTEK ADV-O. Vibration studies have led to reductions in noise transmitted to the shear probes with the use of a damping material and a probe stiffener attached to the forward end of the UVIC pressure case. Also contained in the AUV are a variety of standard REMUS “hotel sensors”, including pitch, roll, heading, depth, latitude, and longitude. In addition, the AUV navigates using a SBL system, using onboard forward-looking and moored transponders. For safety, the AUV is tracked from a surface vessel using a Trackpoint II transponder. For a more complete description of the techniques used to calculate the dissipation rate, see Levine et al., 2001. Absolute velocity is obtained from Upward and Down ward ADCPs Upper CTD Lower CTD Thrust probe EDDY VISCOSITY AND DIFFUSIVITY FROM AUV MEASUREMENTS 145 the bottom tracked mode of the downward-looking ADCP, which combined with Doppler obtained from volume scattering allows very precise absolute water velocity and shear. With these sensors, the AUV is capable of measuring the key finescale gradients of velocity, temperature, salinity, and density, as well as the turbulent temperature and the two-component velocity shear field, the components transverse to the direction of AUV motion. The response of the shear probe and the corrections needed to estimate the unresolved high wavenumber contribution are discussed in Macoun and Lueck (2002). The response of a REMUS vehicle to the larger turbulent scales and the use of the vehicle vertical motion to infer turbulence at these scales using a Kalman filter technique are discussed by Hayes and Morrison (2002). Raw data from the shear probes is processed to remove noise associated with platform vibration. This transfer function method utilizes data from accelerometers mounted in the pressure case, directly behind the probe mounts. The combination of noise limiting vehicle modifications, discussed above, and the use of the all accelerometers has resulted in a noise floor of order 10 W/kg. The addition of the probe stiffener raises the resonant frequency of the probes into the kilohertz range. The frequency response of the FP07 fast thermistor used in the UVIC turbulence package, 25 Hz (Lueck et al., 2002), is too slow for capturing the high wavenumber end of the thermal microstucture using the AUV, typically moving relative to the water at 1 m/s. For calibration, this fast thermistor is compared with synoptic data from the CTD platinum thermometer. To estimate stratification, two Falmouth Scientific Instruments CTDs are mounted above and below the centerline of the AUV. These instruments, 2" FSI MCTDs, utilize an inductive conductivity cell, a platinum resistance thermometer, and a silicone pressure sensor. Correspondingly, the manufacturer claims accuracies of ±0.0002 S/m, ±0.002°C, and 0.02% of full scale pressure (100 db) for these sensors. Because of drift problems with these sensors for the study presented here, stratification was estimated from individual CTD vertical profiles during launch and recovery. To estimate finescale velocity shear, a modified version of the RDI 1200 kHz Workhorse navigator ADCP was integrated in the AUV hull. Upwardand downward-looking transducers share one set of electronics and ping alternatively. The manufacturer claims accuracies for water velocities of ±0.2% ±1mm/s. We selected eight 0.5-m bins for both the upward and downward transducers. Since the vehicle diameter is 0.18 m and the blanking distance is .25 m, the edge of the first bin is located 0.34 m from the AUV centerline. Because of the potential of side lobe scattering we ignore the first bin and use the second and third bins to estimate the finescale shear above and below

James O'Donnell, Dan Codiga, Christopher Edwards, David Ullman, David Hebert, Joseph Rice, Edward Levine, Petra Steggman, Ivar Babb, Phone: (860) 405-9208 Fax: (860) 405-9153 email: james.odonnell@uconn.edu Dept. of Marine Sciences, University of Connecticut, 1084 Shennecossett Road,Groton, CT 06340 Department of Ocean Sciences, University of California, Santa Cruz, CA Graduate School of Oceanography, University of Rhode Island, Narragansett, RI SPAWAR Systems Center, San Diego NUWC, Newport RI 02841 National Undersea Research Center, University of Connecticut, Groton, CT

James O'Donnell, Dan Codiga, Christopher Edwards, David Ullman, David Hebert, Joseph Rice, Edward Levine, Petra Steggman, Ivar Babb, Phone: (860) 405-9208 Fax: (860) 405-9153 email: james.odonnell@uconn.edu Dept. of Marine Sciences, University of Connecticut, 1084 Shennecossett Road,Groton, CT 06340 Department of Ocean Sciences, University of California, Santa Cruz, CA Graduate School of Oceanography, University of Rhode Island, Narragansett, RI SPAWAR Systems Center, San Diego NUWC, Newport RI 02841 National Undersea Research Center, University of Connecticut, Groton, CT

: A Front-Resolving Observational Network with Telemetry (FRONT) is being developed for a region of the coastal ocean. The long-term goal is to demonstrate and evaluate a real-time data collection network in concert with a data-assimilative dynamical model, which is designed to resolve or parameterize the needed range of scales, from mesoscales to microscales.

Strategies are investigated for a simulated unmanned underwater vehicle (UUV) conducting environmental sampling missions in ocean frontal zones. The combination of environmental, vehicle, and navigation models enables the use of the simulator to investigate sampling and mapping choices. Initially, ocean frontal zones are simply modeled to characterize aspects of a deep ocean front similar to the Gulf Stream North Wall, and to simulate cross-frontal aspects of a shallow water front. The vehicle simulation is based on a nonlinear six degree of freedom model that allows for the effects of buoyancy and ocean currents to be incorporated. Forces on the vehicle are modeled as modified Taylor series expansions of the state of the vehicle. Two typical missions for UUV-based sampling are considered: cross-frontal density and downstream velocity structure for the deep ocean front, and cross-frontal cross-stream and vertical velocity structure for the shallow water front. These results provide a baseline for including more realistic sensor, navigation, and dynamics modeling

High-frequency environmental acoustics studies were conducted during July 1993, on the continental shelf edge east of New Jersey. Internal solitons previously observed in this region near the shelf/slope front propagate in packets, usually in the summer seasonal thermocline, and have been associated with anomalous low frequency sound propagation. Acoustic pings were collected using a towed sled instrumented with sonar arrays. Synoptic measurements to characterize the solitons including sound velocity profiles sampled every 10 min over a tidal cycle, and moored data including current, temperature, and conductivity. Acoustic measurements were taken during sled tows parallel to the bottom bathymetry, normal to the propagation direction, over a region determined from bottom cores to be nearly homogeneous fine sand. Measurements were taken using the sled as a source for backscatter measurements, and also using moored acoustic sources and the sled based transducers as receivers. The observed solitons had amplitudes of approximately 10 m and periods of several minutes. The backscatter variability during soliton events was observed to approximately 10–20 dB, and will be compared to modeled predictions based on environmental data.

The vertical motion of a neutrally buoyant float is determined from the solution to the nonlinear forced harmonic oscillator equation originally set forth by Voorhis. Float response to forced vertical oscillations is characterized by the response ratio, r = ξr/ξw, where ξr, is the vertical displacement of an isopycnal relative to the float, and ξw is the vertical displacement of an isopycnal relative to its initial equilibrium position. For isopycnal displacements with frequencies much less than the resonant frequency of the float, the goat can be considered to be in near dynamic equilibrium with the forcing, and r is a function of the relative compressibility between the float and seawater, s = γf/ γw, and the normalized buoyancy frequency N = N/Ω, where Ω is a characteristic float frequency defined by Ω2 = gξw[1 − (αfαw−1)]− 1, where αf, αw are the coefficients of thermal expansion of the float and water, respectively. For the new dynamic equilibrium case, data obtained from a float deployment in a Gulf Stream meander result in an observed r value close to the predicted value. For the case of float response to an isopycnal displacement of frequency near the resonant frequency of the float, vertical motion depends on drag, in addition to the material properties of the float and seawater. The bandwidth over which resonance can occur is parametrized by the 'Q' factor, the inverse of the normalized bandwidth, which for cylindrical floats is predicted to be greater than 1, indicating sharp resonance. From a float deployment in the Gulf Stream region it was estimated that Q ≈ 5. For this case, the spectrum of float temperature, which was used as an indicator of the relative response between the float and a displaced isopycnal, and the spectrum of the float pressure, used as an indicator of float displacement, did scale according to that predicted by the condition of near equilibrium response, up to of order the resonant frequency of the float.