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Travis Miles, Wayne Slade, and Scott Glenn

Abstract

Suspended particle size and concentration are critical parameters that are necessary to understand water quality, sediment dynamics, carbon flux, and ecosystem dynamics, among other ocean processes. In this study we detail the integration of a Sequoia Scientific, Inc., Laser In Situ Scattering and Transmissometry (LISST) sensor into a Teledyne Webb Research Slocum autonomous underwater glider. These sensors are capable of measuring particle size, concentration, and beam attenuation by particles in size ranges from 1.00 to 500 μm at a resolution of 1 Hz. The combination of these two technologies provides the unique opportunity to measure particle characteristics persistently at specific locations or to survey regional domains from a single profiling sensor. In this study we present the sensor integration framework, detail quality assurance and control procedures, and provide a case study of storm-driven sediment resuspension and transport. Specifically, Rutgers glider RU28 was deployed with an integrated LISST-Glider for 18 days in September of 2017. During this period, it sampled the nearshore environment off coastal New Jersey, capturing full water column sediment resuspension during a coastal storm event. A novel method for in situ background corrections is demonstrated and used to mitigate long-term biofouling of the sensor windows. In addition, we present a method for removing schlieren-contaminated time periods utilizing coincident conductivity temperature and depth, fluorometer, and optical backscatter data. The combination of LISST sensors and autonomous platforms has the potential to revolutionize our ability to capture suspended particle characteristics throughout the world’s oceans.

Open access
Seth F. Zippel, J. Thomas Farrar, Christopher J. Zappa, Una Miller, Louis St. Laurent, Takashi Ijichi, Robert A. Weller, Leah McRaven, Sven Nylund, and Deborah Le Bel

Abstract

Upper-ocean turbulence is central to the exchanges of heat, momentum, and gasses across the air/sea interface, and therefore plays a large role in weather and climate. Current understanding of upper-ocean mixing is lacking, often leading models to misrepresent mixed-layer depths and sea surface temperature. In part, progress has been limited due to the difficulty of measuring turbulence from fixed moorings which can simultaneously measure surface fluxes and upper-ocean stratification over long time periods. Here we introduce a direct wavenumber method for measuring Turbulent Kinetic Energy (TKE) dissipation rates, ϵ, from long-enduring moorings using pulse-coherent ADCPs. We discuss optimal programming of the ADCPs, a robust mechanical design for use on a mooring to maximize data return, and data processing techniques including phase-ambiguity unwrapping, spectral analysis, and a correction for instrument response. The method was used in the Salinity Processes Upper-ocean Regional Study (SPURS) to collect two year-long data sets. We find the mooring-derived TKE dissipation rates compare favorably to estimates made nearby from a microstructure shear probe mounted to a glider during its two separate two-week missions for O (10−8) ≤ ϵO (10−5) m2 s−3. Periods of disagreement between turbulence estimates from the two platforms coincide with differences in vertical temperature profiles, which may indicate that barrier layers can substantially modulate upper-ocean turbulence over horizontal scales of 1-10 km. We also find that dissipation estimates from two different moorings at 12.5 m, and at 7 m are in agreement with the surface buoyancy flux during periods of strong nighttime convection, consistent with classic boundary layer theory.

Open access
Cesar M. Salazar Aquino, Boonleng Cheong, and Robert D. Palmer

Abstract

In this paper, a novel technique is proposed to mitigate the so-called blind range on radars that use pulse compression. It is well known that the blind range is caused by the strong leak-through into the receiver during the transmission cycle. The proposed technique is called progressive pulse compression (PPC) and is based on partial decoding. PPC uses a portion of the uncontaminated received signal in conjunction with pulse compression to estimate the echoes from the incomplete signal. The technique does not require the use of a fill pulse or any hardware modifications. PPC can be divided into three steps. The fist step is to discard all the received signals during the transmit cycle and apply a smooth taper for continuous transition from zero to one. The second step is to perform the pulse compression using matched filter. The combination of these two steps is equivalent to performing pulse compression using a progressively changing template to partially extract the uncontaminated received signal for compression. The third step is to compensate for the progressively changing template so that proper reflectivity values can be recovered. This technique has been tested on the PX-1000 and will be implemented on PX-10k in the near future. These two radars are designed and operated by the Advanced Radar Research Center at the University of Oklahoma and are both X-band software-defined solid-state systems. The results presented in this paper are collected using the PX-1000 radar.

Open access
Hyun Mee Kim and Dae-Hui Kim

Abstract

In this study, the effect of boundary-condition configurations in the regional Weather Research and Forecasting (WRF) Model on the adjoint-based forecast sensitivity observation impact (FSOI) for 24 h forecast error reduction was evaluated. The FSOI has been used to diagnose the impact of observations on the forecast performance in several global and regional models. Different from the global model, in the regional model, the lateral boundaries affect forecasts and FSOI results. Several experiments with different lateral boundary conditions were conducted. The experimental period was from 1 to 14 June 2015. With or without data assimilation, the larger the buffer size in lateral boundary conditions, the smaller the forecast error. The nonlinear and linear forecast error reduction (i.e., observation impact) decreased as the buffer size increased, implying larger impact of lateral boundaries and smaller observation impact on the forecast error. In most experiments, in terms of observation types (variables), upper-air radiosonde observations (brightness temperature) exhibited the greatest observation impact. The ranking of observation impacts was consistent for observation types and variables among experiments with a constraint in the response function at the upper boundary. The fractions of beneficial observations were approximately 60%, and did not considerably vary depending on the boundary conditions specified when calculating the FSOI in the regional modeling framework.

Open access
Dion Häfner, Johannes Gemmrich, and Markus Jochum

Abstract

The occurrence of extreme (rogue) waves in the ocean is for the most part still shrouded in mystery, because the rare nature of these events makes them difficult to analyze with traditional methods. Modern data-mining and machine-learning methods provide a promising way out, but they typically rely on the availability of massive amounts of well-cleaned data. To facilitate the application of such data-hungry methods to surface ocean waves, we developed the Free Ocean Wave Dataset (FOWD), a freely available wave dataset and processing framework. FOWD describes the conversion of raw observations into a catalog that maps characteristic sea state parameters to observed wave quantities. Specifically, we employ a running-window approach that respects the nonstationary nature of the oceans, and extensive quality control to reduce bias in the resulting dataset. We also supply a reference Python implementation of the FOWD processing toolkit, which we use to process the entire Coastal Data Information Program (CDIP) buoy data catalog containing over 4 billion waves. In a first experiment, we find that, when the full elevation time series is available, surface elevation kurtosis and maximum wave height are the strongest univariate predictors for rogue wave activity. When just a spectrum is given, crest–trough correlation, spectral bandwidth, and mean period fill this role.

Open access
Lucas M. Merckelbach and Jeffrey R. Carpenter

Abstract

Autonomous, buoyancy-driven ocean gliders are increasingly used as a platform for the measurement of turbulence microstructure. In the processing of such measurements, there is a sensitive (quartic) dependence of the turbulence dissipation rate ϵ on the speed of flow past the sensors, or alternatively, the speed of the glider through the ocean water column. The mechanics of glider flight is therefore examined by extending previous flight models to account for the effects of ocean surface waves. It is found that due to the relatively small buoyancy changes used to drive gliders, the surface wave-induced motion, superimposed onto the steady-state motion, follows to a good approximation the motion of the wave orbitals. Errors expected in measuring ϵ at the ocean near-surface due to wave-induced relative velocities are generally less than 10%. However, pressure perturbations associated with the wave motion can cause significant perturbations in the glider-measured pressure signal and consequently also in the measured vertical glider velocity signal. This effect of surface waves is only present in the shallow water regime. It arises from an incomplete cancellation of the wave-induced pressure perturbation with the hydrostatic component due to vertical glider displacements, whereas for deep-water waves this cancellation is complete.

Open access
Björn Lund, Hanjing Dai, Hans C. Graber, Cédric M. Guigand, Brian K. Haus, John A. Lodise, Guillaume Novelli, Tamay Özgökmen, Michael A. Rebozo, Edward H. Ryan, Ruben Carrasco, Jochen Horstmann, and Michael Streßer

Abstract

Our unmanned aerial system (UAS) current mapping is based on optical video data of the sea surface. We use three-dimensional fast Fourier transform and least-squares fitting to measure the surface waves’ phase velocities and the currents via the linear dispersion relationship. Our UAS is a low-cost off-the-shelf quadcopter with inaccurate camera position and attitude measurements, which may cause spurious currents as large as the signal. We present a novel wave-based UAS heading and position correction, improving the image rectification accuracy by a factor of ~3.5 and the current measurements’ temporal repeatability by factors of 1.8 to 4.8. This validation study maps the currents at high spatiotemporal resolution (5 m and 4 s) across the ~700 m wide tidally dominated Bear Cut channel in Miami, Florida. The UAS currents are compared to flotsam tracks, obtained through automated UAS video object detection and tracking, drifter tracks, and acoustic Doppler current profiler measurements. The root-mean-square errors of the cross- and along-channel currents are better than 0.03 m/s for the flotsam comparison and better than 0.06 m/s for the drifter comparison; the latter revealed a 0.06 m/s along-wind bias due to wind- and wave-driven vertical current shear. UAS current mapping could be used to monitor river discharge, buoyant pollutants, or submesoscale fronts and eddies; the proposed wave-based heading and position correction enables its use in areas without ground control points.

Open access
Pichaya Lertvilai, Paul L.D. Roberts, and Jules S. Jaffe

Abstract

The development of a low-cost Video Velocimeter (VIV) to estimate underwater bulk flow velocity is described. The instrument utilizes a simplified particle image correlation technique to reconstruct an average flow velocity vector from video recordings of ambient particles. The VIV uses a single camera with a set of mirrors that splits the view into two stereoscopic views, allowing estimation of the flow velocity vector. The VIV was validated in a controlled flume using ambient seawater, and subsequently field tested together with an acoustic Doppler velocimeter with both mounted close to the coastal seafloor. When used in nonturbulent flow, the instrument can estimate mean flow velocity parallel to the front face of the instrument with root-mean-squared errors of the main flow within 10% of the ±20 cm s−1 measurement range when compared to an acoustic Doppler velocimeter (ADV). The predominant feature of the VIV is that it is a cost-effective method to estimate flow velocity in complex benthic habitats where velocity parallel to the sea floor is of interest.

Open access
Aart Overeem, Hylke de Vries, Hassan Al Sakka, Remko Uijlenhoet, and Hidde Leijnse

Abstract

The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Z hk h relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZ h and attenuation correction is limited to 10 dB; and 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase K dp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.

Open access
J.C. Hubbert, G. Meymaris, U. Romatschke, and M. Dixon

Abstract

Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars. In this two-part paper ground clutter mitigation is addressed using a time-domain regression filter. Clutter filtering is now widely accomplished with spectral processing where the times series of data corresponding to a radar resolution volume are transformed with a Discrete Fourier Transform after which the zero and near-zero velocity clutter components are eliminated by setting them to zero. Subsequently for reectivity, velocity and spectrum width estimates, interpolation techniques are used to recover some of the power loss due to the clutter filter, which has been shown to reduce bias. The spectral technique requires that the I (in-phase) and Q (quadrature) time series be windowed in order to reduce clutter power leakage away from zero and near-zero velocities. Unfortunately, window functions such as the Hamming, Hann and Blackman attenuate the time series signal by 4.01, 4.19 and 5.23 dB for 64-point times series, respectively, and thereby effectively reduce the number of independent samples available for estimating the radar parameters of any underlying weather echo. Here in Part 1 a regression filtering technique is investigated, via simulated data, which does not require the use of such window functions and thus provides for better weather signal statistics. In Part 2 (Hubbert et al. 2021) the technique is demonstrated using both S-Pol and NEXRAD data. It is shown that the regression filter rejects clutter as effectively as the spectral technique but has the distinct advantage that estimates of the radar variables are greatly improved. The technique is straightforward and can be executed in real time.

Open access