Indian Ocean wave Modeling Beijing, China, 25 to 29 July 2005 Joint Regional Conference on Disaster...

Indian Ocean wave ModelingIndian Ocean wave Modeling

Beijing, China, 25 to 29 July 2005Beijing, China, 25 to 29 July 2005

Joint Regional Conference on Disaster Relief and Management – International Cooperation & Role of ICTJoint Regional Conference on Disaster Relief and Management – International Cooperation & Role of ICTHelpHelp atat Alexandria, Alexandria, Egypt Egypt during the period from 14 to 17 April 2007during the period from 14 to 17 April 2007

Joint Regional Conference on Disaster Relief and Management – International Cooperation & Role of ICTJoint Regional Conference on Disaster Relief and Management – International Cooperation & Role of ICTHeldHeld atat Alexandria, Alexandria, Egypt Egypt during the period from 14 to 17 April 2007during the period from 14 to 17 April 2007

Wave Basic KnowledgeWave Basic Knowledge

Ocean Wave ModelingOcean Wave Modeling

- Hard Computing Approach - Hard Computing Approach

- Soft Computing Approach - Soft Computing Approach

VirtualWave3D ProgramVirtualWave3D Program

Wave Basic KnowledgeWave Basic Knowledge

Anatomy of a waveAnatomy of a waveA wave can be regarded initially as an ideal a sinusoidal wave (though the ideal A wave can be regarded initially as an ideal a sinusoidal wave (though the ideal

regular waves bears little resemblance to real waves observed at sea regular waves bears little resemblance to real waves observed at sea irregular in form and period)irregular in form and period)

Characteristics:• H: Height (from crest to trough)• L: Wavelength (from crest to crest)• T: Period (time required for two successive crests to pass a

point)• h: Depth of water column below mean surface level

Anatomy of a waveAnatomy of a wave

A few waves: A case of temporal- and spatial-scalesA few waves: A case of temporal- and spatial-scales

Waves are classified according to their wave period

What characteristics are used to classify waves?

Disturbing force

Free waves vs. forced waves

Restoring force

Wavelength

Classifying Waves

Classifying WavesJoint Regional Conference on Disaster Relief and Management – International Cooperation & Role of ICTJoint Regional Conference on Disaster Relief and Management – International Cooperation & Role of ICTHelpHelp atat Alexandria, Alexandria, Egypt Egypt during the period from 14 to 17 April 2007during the period from 14 to 17 April 2007

Wave Dynamics And Wind Waves

Waves transmit energy across the ocean’s surface.

Ocean Waves

Orbital waves are waves in which the particles of water move in closed circles as the wave passes.

Progressive waves are waves of moving energy in which the wave form moves in one direction along the surface (or junction) of the transmission medium. Orbital waves are a type of progressive wave because the waveform moves forward

What are the parts of an ocean wave?

Deep Water Waves, Shallow Water Waves

Note the importance of the relationship between wavelength and depth in determining wave type.

Anatomy of wind wavesAnatomy of wind wavesWind waves:Wind waves:

As the wind blows As the wind blows formation of ripples (capillary waves) formation of ripples (capillary waves)As the wind increases in speed As the wind increases in speed waves become larger waves become larger (gravity waves)(gravity waves)

Variety and size of wind-generated waves are controlled by four Variety and size of wind-generated waves are controlled by four principal factors:principal factors:

• Wind velocityWind velocity• Wind durationWind duration• Unobstructed distance over which wind blows (fetch)Unobstructed distance over which wind blows (fetch)• Original sea stateOriginal sea state

What factors affect wind wave development?

Wind strength - wind must be moving faster than the wave crests for energy transfer to continue

Wind duration - winds that blow for a short time will not generate large waves

Fetch - the uninterrupted distance over which the wind blows without changing direction

Factors Affecting Wind Wave Development

Ocean Wave modelingOcean Wave modeling

Ocean wave forecasting techniquesOcean wave forecasting techniques

Hard computing approach by Hard computing approach by using using WAM WAM ((WAWAve ve MModel) odel)

Soft computing approach by Soft computing approach by using using NNs (NNs (NNeural eural NNetworks)etworks)

Hard computing approach by using Hard computing approach by using WAMWAM

Numerical wave modelingNumerical wave modeling

- The most used descriptor of the wave field is the energy The most used descriptor of the wave field is the energy density spectrum in both frequency and direction, where density spectrum in both frequency and direction, where is the frequency, and the direction of propagation. This is the frequency, and the direction of propagation. This representation is particularly useful because we already representation is particularly useful because we already know how to interpret what we know about wave physics know how to interpret what we know about wave physics in terms of the spectral components, .Each component can in terms of the spectral components, .Each component can be regarded as a sinusoidal wave of which we have a be regarded as a sinusoidal wave of which we have a reasonably well-understood theory. From this spectrum, reasonably well-understood theory. From this spectrum, we can deduce most of the parameters expected of an we can deduce most of the parameters expected of an operational wave model namely, the significant wave operational wave model namely, the significant wave height, the frequency spectrum, the peak frequency and height, the frequency spectrum, the peak frequency and secondary frequency maxima, the directional spectrum, secondary frequency maxima, the directional spectrum, the primary wave direction, any secondary wave the primary wave direction, any secondary wave directions, the zero-crossing period, etc.directions, the zero-crossing period, etc.

Input NonlinearDissipationGroup velocity

Angular frequency Latitude LongitudeDirection

Energy balance equationEnergy balance equation

Wave spectrum

Third generation modelsThird generation models

A classification of wave models into first, second and third A classification of wave models into first, second and third generation wave models is also used, which takes into generation wave models is also used, which takes into account the method of handling the nonlinear source term account the method of handling the nonlinear source term

•• First generation models do not have an explicit term. First generation models do not have an explicit term. Nonlinear energy transfers re implicitly expressed through Nonlinear energy transfers re implicitly expressed through the and terms.the and terms.

•• Second generation models handle the term by parametric Second generation models handle the term by parametric methods, for example by applying a reference spectrum methods, for example by applying a reference spectrum (for example the JONSWAP spectrum or the (for example the JONSWAP spectrum or the Pierson/Moskowitz spectrum) to reorganize the energy Pierson/Moskowitz spectrum) to reorganize the energy (after wave growth and dissipation) over the frequencies.(after wave growth and dissipation) over the frequencies.

•• Third generation models calculate the nonlinear energy Third generation models calculate the nonlinear energy transfers explicitly.transfers explicitly.

- The WAM-model is a third The WAM-model is a third generation wave model which generation wave model which solves the wave transport solves the wave transport equation explicitly without any equation explicitly without any presumptions on the shape of presumptions on the shape of the wave spectrum. It the wave spectrum. It represents the physics of the represents the physics of the wave evolution in accordance wave evolution in accordance without knowledge today for without knowledge today for the full set of degrees of the full set of degrees of freedom of a 2D wave freedom of a 2D wave spectrum.spectrum.

WAM modelWAM model

The model was developed at the Max-Planck-Institut The model was developed at the Max-Planck-Institut Meteorologie in Hamburg (Germany) by S. and K. Meteorologie in Hamburg (Germany) by S. and K. Hasselmann with the aid of P. Janssen and G. Komen Hasselmann with the aid of P. Janssen and G. Komen (KNMI, Netherlands), and L. Zambreski and H. (GKSS, (KNMI, Netherlands), and L. Zambreski and H. (GKSS, Germany, ECMWF, Reading, UK). It has been installed at Germany, ECMWF, Reading, UK). It has been installed at about 35 institutions world wide and is used for research about 35 institutions world wide and is used for research and also operational application. It is also being applied and also operational application. It is also being applied for interpreting and assimilating satellite wave data.for interpreting and assimilating satellite wave data.

WAM DEVELOPMENTWAM DEVELOPMENT SWAMP STUDY OF 1st AND 2nd GENERATION WAVE SWAMP STUDY OF 1st AND 2nd GENERATION WAVE

MODELS (1984)MODELS (1984) DEVELOPMENT OF 3rd GENERATION WAVE MODEL DEVELOPMENT OF 3rd GENERATION WAVE MODEL

GOALGOAL : : DEVELOP A 3G WAVE MODEL BASE ON PHYSICSDEVELOP A 3G WAVE MODEL BASE ON PHYSICS IMPLEMENT A GLOBAL VERSION FOR MEDIUM IMPLEMENT A GLOBAL VERSION FOR MEDIUM

RANGE FORECASTINGRANGE FORECASTING DEVELOP NESTED VERSIONS OF WAMDEVELOP NESTED VERSIONS OF WAM PERFORM STUDIES OF DYNAMIC WAVE PERFORM STUDIES OF DYNAMIC WAVE

PROCESSESPROCESSES DEVELOP DATA ASSIMILATION TECHNIQUESDEVELOP DATA ASSIMILATION TECHNIQUES

WAM modelWAM model

Model physics and Model physics and dynamicsdynamics

The WAM model describes the evolution of a two-The WAM model describes the evolution of a two-dimensional ocean wave spectrum. In contrast to first and dimensional ocean wave spectrum. In contrast to first and second generation models, the third generation model second generation models, the third generation model WAM introduces no ad hoc assumptions on the spectral WAM introduces no ad hoc assumptions on the spectral shape. It computes the 2-d wave variance spectrum shape. It computes the 2-d wave variance spectrum through integration of the transport equationthrough integration of the transport equation

Model physics and Model physics and dynamicsdynamics

Wave spectrum Linear wave theory

(wave energy spectrum)

Statistical theory of linear random waves(wave number spectrum)

Ocean Wave model based on WAM Energy balance equationEnergy balance equation

SOURCE TERMS S = SSOURCE TERMS S = Sinin + S + Snlnl + S + Sdsds + S + Sbottombottom

WIND INPUT SOURCE TERM WIND INPUT SOURCE TERM SSinin CYCLE 1-3 BASED ON SYNDER (1981)CYCLE 1-3 BASED ON SYNDER (1981) CYCLE 4 BASED ON JANSSEN (1991)CYCLE 4 BASED ON JANSSEN (1991)

NON-LINEAR SOURCE TERM NON-LINEAR SOURCE TERM SSnlnl BASED ON HASSELMANN’S THEORIES ON WAVE-WAVE BASED ON HASSELMANN’S THEORIES ON WAVE-WAVE

INTERACTIONSINTERACTIONS DISSAPATION SOURCE TERM DISSAPATION SOURCE TERM SSdsds

BASED ON HASSELMANN’S THEORY OF DISSAPATION DUE BASED ON HASSELMANN’S THEORY OF DISSAPATION DUE TO WHITE CAPPING. TUNING TERMTO WHITE CAPPING. TUNING TERM

BOTTOM FRICTION SOURCE TERM BOTTOM FRICTION SOURCE TERM SSbottombottom ONLY APPLIES IN SHALLOW WATERONLY APPLIES IN SHALLOW WATER

Soft computing approach by using Soft computing approach by using NNs NNs (Neural Networks)(Neural Networks)

การประชุ�มสัมมนาวิ ชุาการ เน��องในวินสัถาปนากรมอ�ตุ�น ยมวิ ทยา การประชุ�มสัมมนาวิ ชุาการ เน��องในวินสัถาปนากรมอ�ตุ�น ยมวิ ทยา

OceanOcean Wave model based on GRNN Wave model based on GRNN (Generalize Regression Nearal (Generalize Regression Nearal Network )Network )

U = wind velocity at 10 U = wind velocity at 10 m above the mean sea m above the mean sea level, level, w w : wind direction, : wind direction,

F : fetch length, F : fetch length, HHs s : significant wave : significant wave

height, height, DDww : sea depth : sea depth

ObservingObserving

GRNNGRNN

WAMWAMUNOCAL STATIONUNOCAL STATION

ObservingObserving

GRNNGRNN

WAMWAMHUA-HIN STATIONHUA-HIN STATION

UNOCAL STATIONUNOCAL STATION HUA-HIN STATIONHUA-HIN STATION

Create VirtualWave3D Create VirtualWave3D ProgramProgram

VirtualWave AlgorithmVirtualWave Algorithm• Read Topography (Earth grid Read Topography (Earth grid points)points)• Read WAM Output of all time Read WAM Output of all time steps (every 3 hours)steps (every 3 hours)

• Read Wave Height (WaH)Read Wave Height (WaH)• Read Wave Speed (WaS), Read Wave Speed (WaS), Wave Direction (WaD)Wave Direction (WaD)• Read Wind Speed (WiS), Wind Read Wind Speed (WiS), Wind Direction (WiD)Direction (WiD)• Read Wave FrequencyRead Wave Frequency

• Calculate SWH and ETA of all Calculate SWH and ETA of all pointspoints

• SWH = SWH = (WaH/2)*Cos(2*PI*WaF*t - WaD)(WaH/2)*Cos(2*PI*WaF*t - WaD)• ETA = (WaH/2)*Sin(-ETA = (WaH/2)*Sin(-2*PI*WaF*t)2*PI*WaF*t)

• Visualize and animate all data of Visualize and animate all data of all time stepsall time steps

Methodology - GridMethodology - Grid

Latitude 5N-15N

10 degrees = 41 points

Longitude 95E-105E

10 degrees =41 points

Resolution0.25

degrees = 27.75km

(1 degree = 111km)

SWH(Significant Wave Height) - ETASWH(Significant Wave Height) - ETA

SWHSWHEtaEta

Visualization Results - TY Linda 1997Visualization Results - TY Linda 1997

Visualization Results - TD 2003Visualization Results - TD 2003

Visualization Results - TS Muifa 2004Visualization Results - TS Muifa 2004

Indian Ocean wave Modeling Beijing, China, 25 to 29 July 2005 Joint Regional Conference on Disaster...

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