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Jiali Zhang, Liang Zhang, Anmin Zhang, Lianxin Zhang, Dong Li, and Xuefeng Zhang

Abstract

Sound speed profile (SSP) affecting underwater acoustics is closely related to the temperature and the salinity fields. It is of great value to obtain the temperature and the salinity information through the high-precision sound speed profiles. In this paper, a data assimilation scheme by introducing sound speed profiles as a new constraint is proposed within the framework of 3DVAR data assimilation [referenced as SSP-constraint 3DVAR (SSPC-3DVAR)], which aims at improving the analysis accuracy of initial fields of the temperature and salinity in coastal sea areas. To validate the performance of the new assimilation scheme, ideal experiments are first carried out to show the advantages of the new proposed SSPC-3DVAR. Then the temperature, the salinity, and the SSP observations from field experiments in a coastal area are assimilated into the Princeton Ocean Model to validate the performance of short-time forecasts, adopting the SSPC-3DVAR scheme. Results show that it is efficient to improve the estimate accuracy by as much as 14.6% and 11.1% for the temperature and salinity, respectively, when compared with the standard 3DVAR. It demonstrates that the proposed SSPC-3DVAR approach works better in practice than the standard 3DVAR and will primarily benefit from variously and widely distributed observations in the future.

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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
VINCENT T. WOOD, ROBERT P. DAVIES-JONES, and ALAN SHAPIRO

Abstract

Single-Doppler radar data are often missing in important regions of a severe storm due to low return power, low signal-to-noise ratio, ground clutter associated with normal and anomalous propagation, and missing radials associated with partial or total beam blockage. Missing data impact the ability of WSR-88D algorithms to detect severe weather. To aid the algorithms, we develop a variational technique that fills in Doppler velocity data voids smoothly by minimizing Doppler velocity gradients while not modifying good data. This method provides estimates of the analysed variable in data voids without creating extrema.

Actual single-Doppler radar data of four tornadoes are used to demonstrate the variational algorithm. In two cases, data are missing in the original data, and in the other two, data are voided artificially. The filled-in data match the voided data well in smoothly varying Doppler velocity fields. Near singularities such as tornadic vortex signatures, the match is poor as anticipated. The algorithm does not create any velocity peaks in the former data voids, thus preventing false triggering of tornado warnings. Doppler circulation is used herein as a far-field tornado detection and advance-warning parameter. In almost all cases, the measured circulation is quite insensitive to the data that have been voided and then filled. The tornado threat is still apparent.

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Matthew E. Gropp and Casey E. Davenport

Abstract

Deep convective thunderstorm tracking methodologies and software have become useful and necessary tools across many applications, from nowcasting to model verification. Despite many available options, many of these pre-existing methods lack a customizable, fast, and flexible methodology that can track supercell thunderstorms within convective-allowing climate datasets with coarse temporal and spatial resolution. This project serves as one option to solve this issue via an all-in-one tracking methodology, built upon several open-source Python libraries, and designed to work with various temporal resolutions, including hourly. Unique to this approach is accounting for varying data availability of different model variables, while still sufficiently and accurately tracking specific convective features; in this case, supercells were the focus. To help distinguish supercells from ordinary cells, updraft helicity and other three-dimensional atmospheric data were incorporated into the tracking algorithm to confirm its supercellular status. Deviant motion from the mean wind was also used identify supercells. The tracking algorithm was tested and performed on a dynamically-downscaled regional climate model dataset with 4 km horizontal grid spacing. Each supercell was tracked for its entire lifetime over the course of 26 years of model output, resulting in a supercell climatology over the central United States. Due to the tracking configuration and dataset used, the tracking performs most consistently for long-lived and strong supercells compared to weak and short-lived supercells. This tracking methodology allows for customizable open-source tracking of supercells in any downscaled convective-allowing dataset, even with coarse temporal resolution.

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Christopher D. Curtis and Sebastián M. Torres

Abstract

Range-oversampling processing is a technique that can be used to lower the variance of radar-variable estimates, reduce radar update times, or a mixture of both. There are two main assumptions for using range-oversampling processing: accurate knowledge of the range correlation and uniform reflectivity in the radar resolution volume. The first assumption has been addressed in previous research; this work focuses on the uniform reflectivity assumption. Earlier research shows that significant reflectivity gradients can occur in storms; we utilized those results to develop realistic simulations of radar returns that include effects of reflectivity gradients in range. An important consideration when using range-oversampling processing is the resulting change in the range weighting function. The range weighting function can change for different types of range-oversampling processing and some techniques, such as adaptive pseudowhitening, can lead to different range weighting functions at each range gate. To quantify the possible effects of differing range weighting functions in the presence of reflectivity gradients, we developed simulations to examine varying types of range-oversampling processing with two receiver filters: a matched receiver filter and a wider-bandwidth receiver filter (as recommended for use with range oversampling). Simulation results show that differences in range weighting functions are the only contributor to differences in radar reflectivity measurements. Results from real weather data demonstrate that the reflectivity gradients that occur in typical severe storms do not cause significant changes in reflectivity measurements, and the benefits from range-oversampling processing outweigh the possible isolated effects from large reflectivity gradients.

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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
Robert Sanchez, Fiamma Straneo, and Magdalena Andres

Abstract

Monitoring the heat content variability of glacial fjords is crucial to understanding the effects of oceanic forcing on marine-terminating glaciers. A Pressure-sensor equipped Inverted Echo Sounder (PIES) was deployed mid-fjord in Sermilik Fjord in southeast Greenland from August 2011 to September 2012 alongside a moored array of instruments recording temperature, conductivity and velocity. Historical hydrography is used to quantify the relationship between acoustic travel time and the vertically-averaged heat content, and a new method is developed for filtering acoustic return echoes in an ice-influenced environment. We show that PIES measurements, combined with a knowledge of the fjord’s two-layer density structure, can be used to reconstruct the thickness and temperature of the inflowing water. Additionally, we find that fjord-shelf exchange events are identifiable in the travel time record implying the PIES can be used to monitor fjord circulation. Finally, we show that PIES data can be combined with moored temperature records to derive the heat content of the upper layer of the fjord where moored instruments are at great risk of being damaged by transiting icebergs.

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BINGTIAN LI, ZEXUN WEI, YONGGANG WANG, XINYU GUO, TENGFEI XU, and XIANQING LV

Abstract

An enhanced harmonic analysis (S_TIDE) approach is adopted to examine the seasonal variations of internal tidal amplitudes in the northern South China Sea (SCS). Results of idealized experiments reveal that the seasonality can be captured by S_TIDE. By applying S_TIDE to mooring data, observed seasonality of internal tidal amplitudes in the northern SCS are explored. Not diurnal and semidiurnal internal tides (ITs), but overtides and long-period constituents of ITs exhibit clear seasonal cycles. However, differences between amplitudes of the eastward velocity and the northward counterpart are evident for K1, M2 and MK3, which may be caused by the intensification of background currents. Amplitudes of those ITs are stronger at intersection time between spring and summer in the eastward direction, but weaker in the northward direction. EOF analysis reveals that modes of diurnal ITs are higher than those of seimidiurnal ITs, which induces relatively more complicated seasonal variations. In addition to intensification of background currents, influences of surface tides and stratification will also induce variations of internal tidal amplitudes, introducing tremendous difficulty in predicting variation trends of internal tidal amplitudes, which greatly reduces predictability of ITs.

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Emily M. Riley Dellaripa, Aaron Funk, Courtney Schumacher, Hedanqiu Bai, and Thomas Spangehl

Abstract

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolution. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the sub-grid scale. This work adapts Quickbeam to the dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate-to-heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land/ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP sub-grid algorithm by applying a more realistic, non-deterministic approach to assigning GCM grid box convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated sub-grid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud cover assumption.

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V.V. Sterlyadkin, K.V. Kulikovsky, A.V. Kuzmin, E.A. Sharkov, and M.V. Likhacheva

Abstract

A direct optical method for measuring the “instantaneous” profile of the sea surface with an accuracy of 1 mm and a spatial resolution of 3 mm is described. Surface profile measurements can be carried out on spatial scales from units of millimeters to units of meters with an averaging time of 10−4 s. The method is based on the synchronization of the beginning of scanning a laser beam over the sea surface and the beginning of recording the radiation scattered on the surface onto the video camera matrix. The heights of all points of the profile are brought to a single point in time, which makes it possible to obtain “instantaneous” profiles of the sea surface with the frequency of video recording. The measurement technique and data processing algorithm are described. The errors of the method are substantiated. The results of field measurements of the parameters of sea waves are presented: amplitude spectra, distribution of slopes at various spatial averaging scales. The applied version of the wave recorder did not allow recording capillary oscillations, but with some modernization it will be possible. The method is completely remote, does not distort the properties of the surface, is not affected by wind, waves and sea currents, it allows you to measure the proportion of foam on the surface. The possibility of applying the proposed method at any time of the day and in a wide range of weather conditions has been experimentally proved.

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