School of Mechanical Engineering Seminar
Wednesday, November 16^th, 2022, at 14:00

Wolfson Building of Mechanical Engineering, Room 206

Giora Hasson
Advisor:  Prof. Yaron Yitzhak-Toledo

HF-radar remote sensing of ocean surface current shear using first order Bragg peaks

Among the various instrumentations available for measuring sea characteristics, high-frequency (HF) radars show some unique advantages. These shore-based remote-sensing systems enable over-the-horizon monitoring of surface currents, waves and winds. This allows for large real-time measurement coverage areas of tens to hundreds of kilometers. The main approach for surface current mapping utilizes the Bragg scattering from waves propagating toward the radar which are of wavelength of half of the transmitted electromagnetic wave. Similar waves that propagate away from the radar also create a similar scattering signal, but it is commonly neglected as it is assumed to provide redundant information. Through numerical and analytical solutions of an exact wave-shearing current model, we show that this signal contains significant information when the upper layer current has a vertical profile.  We present a new methodology for measuring the vertical shear of the upper layer current using the waves propagating towards and away from a single HF radar. Exploiting this additional information reduces the complexity to estimate the vertical shear and can be leveraged to increase the knowledge of the upper layer momentum budget. The suggested method is examined through a field measurement campaign using HF and in-situ instrument on Israel’s mediterranean continental shelf with good preliminary results.        

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סטודנטים וסטודנטיות שנה א' לתואר ראשון בהנדסה - בואו למפגש ההיכרות בפקולטה, שבו תקבלו מידע חשוב ורלוונטי על תכניות הלימודים במסלולים השונים ועל תהליך הרישום לקורסים.

המפגש יתקיים ביום שלישי | 6.9 | 11:00 | אולם בר שירה

מה בתוכנית?

במפגש ישתתפו דקאן הפקולטה, ראשי תכניות הלימודים, מזכירות הסטודנטים, נציגי הסטודנטים, ונציגי "צועדים ביחד".

11:00 - כנס כללי לתלמידי הפקולטה באולם בר-שירה (צמוד לפקולטה למדעי החברה):

12:00 – מפגש לפי יחידות הלימודי על פי החלוקה הבאה:

             תלמידי התכניות להנדסה מכנית  - אולם טאו, בניין וולפסון.

             תלמידי התכניות להנדסה ביו-רפואית - אולם 001, בניין ברודקום.

             תלמידי הנדסת תעשייה וניהול –  אודיטוריום 020.

             תלמידי מדע והנדסה של חומרים  - בניין וולפסון – חדר 134.

             תלמידי מדעים דיגיטליים להיי-טק - 102 ברודקום

13:00 – מפגש קבוצתי עם חונכי התכנית 'צועדים יחד', המדור לקידום סטודנטים, דקאנט הסטודנטים.

שימו לב

מפגש הסברה והיכרות לתלמידות ולתלמידים החדשים שהתקבלו להנדסת חשמל בפקולטה להנדסה לשנה"ל תשפ"ג יתקיים ביום שלישי ה-6.9.2022 בשעה 12:30 באולם בר-שירה.

במפגש ישתתפו דקאן הפקולטה, ראשי תכניות הלימודים, מזכירות הסטודנטים, נציגי הסטודנטים, ונציגי "צועדים ביחד".

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School of Mechanical Engineering Seminar

Wednesday, November 9, 2022 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

On deep learning and numerical wave forecasting models

Yuval Yevnin

PhD student of Prof. Yaron Toledo

School of mechanical engineering

Ocean waves have been affecting human life for millennia, from the early days of hunter-gatherer tribes fishing, through the explorations of the Viking age to modern trade and warfare. In most of those years the understanding and prediction ability of the seas and oceans was intuitive at best. This started to change during and after the second world war, as modern operational wave forecasting models were developed. The third generation of these stochastic numerical models are based on the wave action equation, and have been in use since the late 1980’s.

In this talk we discuss both improvements to the currently used operational models, as well as what we see as the next big step in ocean wave forecasting – the use of deep learning models.

First, an addition of a source term accounting for bottom wave reflection to WAVEWATCH III operational forecasting model is presented. This source terms theoretical background and derivation were extended from previous work. Next, it was implemented and was shown to improve the forecast in the near-shore area and in shallow water.

Second, a deep learning model for short-term forecasting of ocean wave height was developed. The model utilizes in-situ buoy measurements and mid-range operational model forecasts as input and predicts the short-term wave height at the buoy location. The model was shown to improve the forecast RMSE by as much as 77% for one hour horizon and by ~12% for up to 12 hours. In Addition, the model was also shown to be transferable to buoys at other locations without further training.

Finally, an advanced deep learning model for improved wind and consequent wave forecasts is presented. The model improves wind velocity magnitude forecast by ~10% and by using the improved wind in an operational wave model, a similar 10% improved wave height is achieved. The model can be localized in space and time, which is shown to produce 35% improvement in forecasting wave height at the Aegean Sea during the months of May to Septembers, when the Etesian wind is dominant.

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