Publications

Algorithms/Methods

Polarymetric waves sensing, aided by Machine Learning

System for Polarymetric remote sensing of water waves, where the special camera equipped with linear polarization filters is used as the main sensor. The local slopes of water waves are inferred by the level of polarization in each location. The system is solving this complex problem using Machine Learning methods, using simple monochromatic waves as training sets for Neural Networks. The trained networks are capable to produce measurements of spatio-temporal slope and surface elvation maps of irregular (spectral) waves.

Ginio, N., Liberzon, D., Lindenbaum, M., & Fishbain, B. (2023). Efficient machine learning method for spatio-temporal water surface waves reconstruction from polarimetric images. Measurement Science and Technology, 34(5), 055801. https://doi.org/10.1088/1361-6501/acb3eb


Breaking waves detection

Provides accurate detection (above 90% positive detection rate) of breaking wave crests in data of the instantaneous water surface elevation fluctuations as obtained by simple wave gauges. Does not require water surface imaging (i.e. not whitecap counting). Suitable for application in wide variety of waves fields, as the algorithm parameters can be adjusted in correspondence with the analyzed waves field parameters (mean waves period, sampling frequency, etc.).

Detection of breaking waves is achieved by analyzing the signal of the waves instantaneous frequency variations (obtained by means of Hilbert transform). Patterns of the frequency variations associated with the breaking crests are identified using wavelets transform based pattern recognition algorithm.

Full method description is provided here (please don’t forget to cite):

Liberzon, D., Vreme, A., Knobler, S., Bentwich, I., 2019. Detection of breaking waves in single wave gauge records of surface elevation fluctuations. J. Atmos. Ocean. Technol. JTECH-D-19-0011.1. https://doi.org/10.1175/JTECH-D-19-0011.1

The algorithm is implemented in Matlab, and requires WaveletsAnalyzer Toolbox/App.

Download it here

Extract all the files/functions from the archive into one folder and open the DetectBreakersEnsemble.m function. Read the instructions in the function header, and adjust the parameters in the first cell of the function before execution. F2 and F3 pattern mother-wavelets provide the best results.

Note: This algorithm is beta and the author will thank the users for any comments, corrections and recommendations. It is made freely available for the use in scientific research, educational purposes and modeling.Please cite the mentioned above paper.


Golf of Aqaba (Eilat) –  wind waves observations

About 50 hours long measurements of the waves field and wind during June 2017 at the Eilat Inter University Institute for Marine Sciences, Israel.

The data set is freely distributed using the Mendely Data service.

Get the data set here: dx.doi.org/10.17632/kgx4559c67.2

It includes measurements of the water surface instantaneous fluctuations, water depth variations (tidal waves), wind flow velocity mean and fluctuating values. Waves measurements were made in spatial array of 5 wave gauges, allowing derivation of the waves energy propagation direction and directional spread. This data set is highly suitable for education purposes, for classes on water waves, environmental flows, wind turbulence, spectral analysis, statistical properties of time series, and more.

Full description and examples for data processing techniques and the results are published here:

Shani-Zerbib, Rivlin, Liberzon – 2018 – International Journal of Ocean and Coastal Engineering – Data set of wind-waves interactions in

Anabatic (upslope) turbulent flows – Nofit field experiment and water tank physical model

The complete dataset of anabatic (upslope) wind velocity fluctuations measured using the recently developed sonic-hot-film collocated anemometer (two X-shaped hot films were used in this field measurement). The measurements took place on a moderate slope in Nofit, Israel during August 2015.  The dataset is uploaded along with an automated detection algorithm (Matlab code) to capture the beginning and ending times of a bursting event observed in the flow. The data and the complete detection algorithm procedure are detailed in:

Hilel Goldshmid, R., & Liberzon, D. (2020). Obtaining turbulence statistics of thermally driven anabatic flow by sonic-hot-film combo anemometer. Environmental Fluid Mechanics, 20(5), 1221–1249. https://doi.org/10.1007/s10652-018-9649-x

Goldshmid, R. H., & Liberzon, D. (2020). Automated identification and characterization method of turbulent bursting from single-point records of the velocity field. Measurement Science and Technology, 31(10), 105801. https://doi.org/10.1088/1361-6501/ab912b

The water tank physical model was later used to stage controlled experiemnts of thermally driven up-slope flows, discovering the 3D nature and intermittency of the developing turbulent boundary layer.

Goldshmid, R. H., & Liberzon, D. (2023). Laboratory investigation of nominally two-dimensional anabatic flow on symmetric double slopes. Physics of Fluids, 35(11). https://doi.org/10.1063/5.0164984

 


Peer-reviewed journals and books chapters

  1. Vitkin L., Liberzon D., (2014) Grits B. and Kit E., Study of in-situ calibration performance of co-located multi-sensor Hot-Film and Sonic anemometers using “virtual probe” algorithm, Measurement Sci. and Tech., 25, 7, pp 075801. (1.433)
  2. Fernando, H. J. S., Pardyjak, E. R., Di Sabatino, S., … Liberzon, D., … Zsedrovits, T. (2015) THE MATERHORN – Unraveling the Intricacies of Mountain Weather. Bulletin of the American Meteorological Society. doi:10.1175/BAMS-D-13-00131.1. (11.808
  3. Kit E. and Liberzon, D, (2016) 3D-calibration of three- and four-sensor hot-film probes based on collocated sonic using neural networks, Measurement Sci. and Tech., 27, no. 9, 95901-95920.
  4. Itai U. and Liberzon D. (2017) Lagrangian kinematic criterion for the breaking of shoaling waves, Journal of Physical Oceanography, March 2017, http://dx.doi.org/10.1175/JPO-D-16-0289.1.
  5. Weltsch O., Offner A., Liberzon D. and Ramon GZ. (2017) Adsorption-Mediated Mass Streaming in a Standing Acoustic Wave. Phys. Rev. Lett. Vol. 118, 24
  6. Poulsen T.G., Furman A. and Liberzon D. (2017) Effects of wind speed and wind gustiness on subsurface gas transport, Vadoze Zone Journal, Vol. 16, Issue 11.
  7. Poulsen T.G., Furman A. and Liberzon D. (2018) Effect of near-surface wind speed and gustiness on horizontal and vertical porous medium gas transport and gas exchange with the atmosphere, Eur. J. of Souil Sci., Issue 202, pp 1-11.
  8. Hilel Goldshmid R, L. Bardoel S, Hocut CM, et al. (2018) Separation of upslope flow over a plateau. Atmosphere (Basel) 1–11. doi: doi:10.3390/atmos9050165.
  9. Hilel Goldshmid R. and Liberzon D. (2018) Obtaining turbulence statistics of thermally driven anabatic flow by sonic-hot-films combo anemometer, Environmental Fluid Mechanics, 1-29.
  10. Shani-Zerbib A., Rivlin A. Liberzon D. (2018) Data set of wind-waves interactions in the Gulf of Aqaba, International Journal of Ocean and Coastal Engineering, doi: 10.1142/S2529807018500033. The data set is freely available here.
  11. Liberzon D, Vreme A., Knobler S. and Bentwich I. (2019) Detection of breaking waves in single wave gauge records of surface elevation fluctuations. Journal of Atmospheric and Oceanic Technology, doi: 10.1175/JTECH-D-19-0011.1.
  12. Berdugo, N., & Liberzon, D. (2020). Enhancement of water droplet evaporation rate by application of low frequency acoustic field. International Journal of Multiphase Flow, 103217, doi: 10.1016/j.ijmultiphaseflow.2020.103217
  13. Goldshmid, R.H., Liberzon, D., 2020. Automated identification and characterization method of turbulent bursting from single-point records of the velocity field. Meas. Sci. Technol. 31, 105801. https://doi.org/10.1088/1361-6501/ab912b

  14. Offner, A., Berdugo, N., Liberzon, D., 2021. Acoustic-driven droplet evaporation : beyond the role of droplet-gas relative velocity. Int. J. Heat Mass Transf. 171. https://doi.org/10.1016/j.ijheatmasstransfer.2021.121071

  15. Knobler, S., Bar, D., Cohen, R., Liberzon, D., 2021. Wave Height Distributions and Rogue Waves in the Eastern Mediterranean. J. Mar. Sci. Eng. 9, 1–19. https://doi.org/https://doi.org/10.3390/jmse9060660

  16. Mossa M., Hilel Goldshmid R., Liberzon D., Negretti M.E., Sommeria J., Termini D. and De Serio F (2021) Quasi-geostrophic jet-like flow with obstructions, Fluid Mech., 921, A12, doi:10.1017/jfm.2021.501.
  17. Serio, F. De, Goldshmid, R.H., Liberzon, D., Mossa, M., Negretti, M.E., Pisaturo, G.R., Righetti, M., Sommeria, J., Termini, D., Valran, T., Viboud, S. (2021) Turbulent jet through porous obstructions under Coriolis effect : an experimental investigation, Fluids 1–15. https://doi.org/10.1007/s00348-021-03297-2
  18. Shani-Zerbib A., Tayfun M. A., Liberzon D., Statistics of fetch-limited wind waves observed along the western coast of the Gulf of Aqaba. (2021) Ocean Engineering, Vol. 242, 110179. https://doi.org/10.1016/j.oceaneng.2021.110179.
  19. Goldshmid, R.H., Winiarska E., Liberzon D. (2022) Next generation combined sonic-hotfilm anemometer: wind alignment and automated calibration procedure using deep learning, Fluids 63(1) 1-17, doi:10.1007/s00348-022-03381-1.
  20. Knobler S., Winiarska E., Babanin A. and Liberzon D. (2022), Wave Breaking Probabilities Under Wind Forcing in Open Sea and Laboratory, Physics of Fluids, 34, Issue 3, https://doi.org/10.1063/5.0084276.
  21. Eizenberg I., Liberzon D. and Jacobi I. (2022) Pressure Oscillations Due to a Sudden, Finite-Volume, Underwater Air Release, International Journal of Multiphase Flow, https://doi.org/10.1016/j.ijmultiphaseflow.2022.104064.
  22. Knobler, S., Liberzon D., and Fedele F. (2022), Large Waves and Navigation Hazards of the Eastern Mediterranean Sea, Scientific Reports, 12, 16511 https://doi.org/10.1038/s41598-022-20355-9.
  23. Ginio N., Liberzon D., Lindenbaum M. and Fishbain B. (2023), Efficient machine learning method for spatio-temporal water surface waves reconstruction from polarimetric images, Measurement Science and Technology, 34, 055801, https://doi.org/10.1088/1361-6501/acb3eb.
  24. Meisner E., Galvagno M., Andrade D., Liberzon D and Stuhlmeier R. (2023), Wave-by-wave forecast in directional seas using nonlinear dispersion corrections, Physics of Fluids, https://doi.org/10.1063/5.0149980.
  25. Winiarska Ewelina, Soffer Ran, Knopfer Harel, Rene van Hout and Dan Liberzon (2023), The effects of spanwise canopy heterogeneity on the flow field and evaporation rates, Environmental Fluid Mechanics, doi: 1007/s10652-023-09946-w.
  26. Roni H. Goldshmid and Dan Liberzon (2023), Laboratory investigation of nominally two-dimensional anabatic flow on symmetric double slopes, Physics of Fluids, Vol. 35, Iss 11,  doi: 10.1063/5.0164984.