DIRECTIONAL WAVE SPECTRA USING NORMAL SPREADING FUNCTION

Similar documents
Coastal Engineering Technical Note

Report Documentation Page

SW06 Shallow Water Acoustics Experiment Data Analysis

Use of Wijsman's Theorem for the Ratio of Maximal Invariant Densities in Signal Detection Applications

Ocean Acoustics Turbulence Study

Diagonal Representation of Certain Matrices

Sensitivity of West Florida Shelf Simulations to Initial and Boundary Conditions Provided by HYCOM Data-Assimilative Ocean Hindcasts

System Reliability Simulation and Optimization by Component Reliability Allocation

Report Documentation Page

Quantitation and Ratio Determination of Uranium Isotopes in Water and Soil Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS)

High Resolution Surface Characterization from Marine Radar Measurements

Analysis Comparison between CFD and FEA of an Idealized Concept V- Hull Floor Configuration in Two Dimensions. Dr. Bijan Khatib-Shahidi & Rob E.

Report Documentation Page

REGENERATION OF SPENT ADSORBENTS USING ADVANCED OXIDATION (PREPRINT)

Guide to the Development of a Deterioration Rate Curve Using Condition State Inspection Data

Understanding Near-Surface and In-cloud Turbulent Fluxes in the Coastal Stratocumulus-topped Boundary Layers

Estimation of Vertical Distributions of Water Vapor and Aerosols from Spaceborne Observations of Scattered Sunlight

Determining the Stratification of Exchange Flows in Sea Straits

A report (dated September 20, 2011) on. scientific research carried out under Grant: FA

Marginal Sea - Open Ocean Exchange

Super-Parameterization of Boundary Layer Roll Vortices in Tropical Cyclone Models

Attribution Concepts for Sub-meter Resolution Ground Physics Models

Improved Parameterizations Of Nonlinear Four Wave Interactions For Application In Operational Wave Prediction Models

P. Kestener and A. Arneodo. Laboratoire de Physique Ecole Normale Supérieure de Lyon 46, allée d Italie Lyon cedex 07, FRANCE

Crowd Behavior Modeling in COMBAT XXI

Broadband matched-field source localization in the East China Sea*

CRS Report for Congress

Understanding Near-Surface and In-Cloud Turbulent Fluxes in the Coastal Stratocumulus-Topped Boundary Layers

Swash Zone Dynamics: Modeling and Data Analysis

Thermo-Kinetic Model of Burning for Polymeric Materials

Assimilation of Synthetic-Aperture Radar Data into Navy Wave Prediction Models

Real-Time Environmental Information Network and Analysis System (REINAS)

Scattering of Internal Gravity Waves at Finite Topography

Closed-form and Numerical Reverberation and Propagation: Inclusion of Convergence Effects

Super-Parameterization of Boundary Layer Roll Vortices in Tropical Cyclone Models

PIPS 3.0. Pamela G. Posey NRL Code 7322 Stennis Space Center, MS Phone: Fax:

Experimental and Theoretical Studies of Ice-Albedo Feedback Processes in the Arctic Basin

Rogue Wave Statistics and Dynamics Using Large-Scale Direct Simulations

Estimation of Vertical Distributions of Water Vapor from Spaceborne Observations of Scattered Sunlight

Predictive Model for Archaeological Resources. Marine Corps Base Quantico, Virginia John Haynes Jesse Bellavance

Predicting Tropical Cyclone Formation and Structure Change

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER

Computer Simulation of Sand Ripple Growth and Migration.

Internal Tide Generation in the Indonesian Seas

Improvement of Mesoscale Numerical Weather Prediction For Coastal Regions of Complex Terrain FY2003

Parameterizing the Effects of Upper-Ocean Large Eddies on Air-Sea Interaction

Coupled Ocean-Atmosphere Modeling of the Coastal Zone

HYCOM Caspian Sea Modeling. Part I: An Overview of the Model and Coastal Upwelling. Naval Research Laboratory, Stennis Space Center, USA

Contract No. N C0123

Comparative Analysis of Flood Routing Methods

Abyssal Current Steering of Upper Ocean Current Pathways in an Ocean Model with High Vertical Resolution

Coastal Mixing and Optics

FRACTAL CONCEPTS AND THE ANALYSIS OF ATMOSPHERIC PROCESSES

On Applying Point-Interval Logic to Criminal Forensics

SENSORS FOR MEASURING THE VOLUME SCATTERING FUNCTION OF OCEANIC WATERS

A 1/10th Degree Global Ocean Simulation Using the Parallel Ocean Program

Shallow Water Fluctuations and Communications

Regional Stratification and Shear of the Various Streams Feeding the Philippine Straits ESR Component

REPORT DOCUMENTATION PAGE

Evaluation of the effects of Boundary Conditions and Atmospheric Forcing in the SoFLA-HYCOM domain. Villy Kourafalou and Ge Peng UM/RSMAS

Characterization of Caribbean Meso-Scale Eddies

Metrology Experiment for Engineering Students: Platinum Resistance Temperature Detector

REPORT DOCUMENTATION PAGE. Theoretical Study on Nano-Catalyst Burn Rate. Yoshiyuki Kawazoe (Tohoku Univ) N/A AOARD UNIT APO AP

Modulation Instability of Spatially-Incoherent Light Beams and Pattern Formation in Incoherent Wave Systems

High-Fidelity Computational Simulation of Nonlinear Fluid- Structure Interaction Problems

Grant Number: N IP

LAGRANGIAN MEASUREMENTS OF EDDY CHARACTERISTICS IN THE CALIFORNIA CURRENT

Improvements in Modeling Radiant Emission from the Interaction Between Spacecraft Emanations and the Residual Atmosphere in LEO

INFRARED SPECTRAL MEASUREMENTS OF SHUTTLE ENGINE FIRINGS

Topographic Effects on Stratified Flows

Experimental and Theoretical Studies of Ice-Albedo Feedback Processes in the Arctic Basin

Volume 6 Water Surface Profiles

Investigation of the Air-Wave-Sea Interaction Modes Using an Airborne Doppler Wind Lidar: Analyses of the HRDL data taken during DYNAMO

Models of Marginal Seas Partially Enclosed by Islands

USMC Enlisted Endstrength Model

Development and Application of Acoustic Metamaterials with Locally Resonant Microstructures

Improved Source Term Specification Improved Evolutionary Characteristics of Directional Spectra

Satellite Observations of Surface Fronts, Currents and Winds in the Northeast South China Sea

uniform distribution theory

Nonlinear Internal Waves: Test of the Inverted Echo Sounder

Molecular Characterization and Proposed Taxonomic Placement of the Biosimulant 'BG'

Testing Turbulence Closure Models Against Oceanic Turbulence Measurements

Surface Fluxes and Wind-Wave Interactions in Weak Wind Conditions

VLBA IMAGING OF SOURCES AT 24 AND 43 GHZ

Optimizing Robotic Team Performance with Probabilistic Model Checking

Advanced Numerical Methods for NWP Models

Hurricane Wave Topography and Directional Wave Spectra in Near Real-Time

NAVGEM Platform Support

Internal Waves and Mixing in the Aegean Sea

Grant Number: N IP To compare obtained theoretical results with NPAL experimental data.

The Sediment Budget Calculator: A Webtool to Develop a Family of Viable Inlet and Adjacent Beach Sediment Budget Solutions

Two-Dimensional Simulation of Truckee River Hydrodynamics

Impact of Typhoons on the Western Pacific Ocean DRI: Numerical Modeling of Ocean Mixed Layer Turbulence and Entrainment at High Winds

Dynamics of Droplet-Droplet and Droplet-Film Collision. C. K. Law Princeton University

Effects of Constrictions on Blast-Hazard Areas

Analysis of Infrared Measurements of Microbreaking and Whitecaps

Analysis of Mixing and Dynamics Associated with the Dissolution of Hurricane-Induced Cold Wakes

Design for Lifecycle Cost using Time-Dependent Reliability

Analysis of South China Sea Shelf and Basin Acoustic Transmission Data

Modeling the Impact of Extreme Events on Margin Sedimentation

Transcription:

CETN-I-6 3185 DIRECTIONAL WAVE SPECTRA USING NORMAL SPREADING FUNCTION PURPOSE : To present a parameterized model of a directional spectrum of the sea surface using an energy spectrum and a value for the mean wave direction. l'he model used in this Technical Note is the Normal Spreading Function. BACKGROUND: With the growing interest in directional spectral estimation and modeling of so-called "Spreading Functions," there is a need for methods by which the directional spectral density can be easily computed using various types of data. If one has data from directional type measuring devices, it is then possible to estimate a directional spectrum directly. In many cases, the raw data are not available but have been reduced to estimates of the energy spectrum and a mean direction estimate. This is the situation that will be addressed in this note. There are many functional models in the literature with which to represent the directional spectra of the sea surface. This CE'IX will present the normal spreading function which is one of the most simple and easily used models. This model has been shown to fit prototype data (Sand 1984). For a more detailed explanation of directional wave spectra, see Panicker (1974). DIJ?ECTIONAL SPECTRA: The directional spectrum of the sea surface is modeled as the energy spectrum times a function of angular spreading, usually called a spreading function. The directional spectrum is: E(f,e) = E(f) D(f,6) where E(f) = energy spectral density function U. g. Army Engineer Waternays Experiment Station. Coastal Engineering Research Center P. 0. lox 631, Vicksburg, Mississippi 391gO

Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE MAR 1985 2. REPORT TYPE 3. DATES COVERED 00-00-1985 to 00-00-1985 4. TITLE AND SUBTITLE Directional Wave Spectra Using Normal Spreading Function 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Army Engineer Waterways Experiment Station,Coastal Engineering Research Center,Vicksburg,MS,39180 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR S ACRONYM(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES 11. SPONSOR/MONITOR S REPORT NUMBER(S) 14. ABSTRACT With the growing interest in directional spectral estimation and modeling of so-called "Spreading Functions," there is a need for methods by which the directional spectral density can be easily computed using various types of data. If one has data from directional then possible to estimate a directional spectrum raw data are not available but have been reduced spectrum and a mean direction estimate. This is addressed in this note. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Same as Report (SAR) 18. NUMBER OF PAGES 5 19a. NAME OF RESPONSIBLE PERSON Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18

D(f,e) = spreading function E (f,(3) = directional spectral density function f = frequency in cycles per second 8 = direction in radians The function D(f,9> can be represented by a wrapped normal function of the following form: D(f,e) = k = -1 czi-'a where u = the directional spreading parameter in radians eo= the mean wave direction in radians for u < n/3 radians. If e. is not close to zero (or 27~), then the spreading function can be approximated by a normal bump centered at 13~, and the terms for._ k = -1 and k = 1 may be dropped. Thus D(f,e) = exp[-+t-:o fia r] This is the well known normal probability function where e. and 8 are functions of frequency. The most commonly used spreading function is the COSINE 2s model (Long 1980). 'Ihe normal model is used here as an approxi- mation due to its mathematical simplicity. EXAMPLE: Suppose a given location is partially sheltered by an obstacle such that only the wave energy associated with the angular band e1, O2 is of interest (Mathiesen 1984) (see Figure 1). For this example, assume that no diffraction or refraction occurs at the inlet entrance. If we are given the energy spectrum of the sea surface and a mean direction 8 offshore of the 0

CETN-I-6 3185 Figure 1. Partially sheltered location obstacle, then the energy E associated with the angle interval 01, 82 is approximately E = m. J D(e) de (1) where mo is the variance of the sea surface m m = 0 / -03 E(f) df The dependency on frequency has been dropped from D(f,e) leaving D(e). This implies that all frequencies have the same mean direction and speed. The validity of this assumption depends on the narrow bandedness of the energy spectral density function. For fairly narrow spectra (e.g., a swell train), the assumption is good. For situations where the spectral density is not 3

narrow banded (e.g., a locally generated sea train) this method will usually yield incorrect results. The energy reduction factor due to sheltering A can be shown as the integral in the following manner: A= / e2 5 D(0) de (2) This integral is evaluated in the following way: A= / Y - e. z(x) dx, (3) where Z(x) is the standard normal equation 3 is further reduced to probability function. The integral in Z(x) dx, - Z(x) dx, -00 The values for P (Z) are available in standard the CRC Standard Mathematical Tables (Selby 1981). normal tables such as Suppose that e2 = 2.90 radians and = 2.20 radians where 5. radians. A value of u = 0.524 radians (based on 30'spread) an approximate value for u is not known and one is unwilling value, then a general range of 15 to 60 deg may be assumed and eo = 2.15 is assumed. If to assume a the analysis computed at the end points of this range to obtain upper and lower bounds on 4

CETN-I-6 3185 the resulting wave energy estimates. This range corresponds to the range for the COSINE 2s model that has been shown to hold for data obtained under various conditions (Sand 1981). Then equation 4 becomes: A = P(1.43) - P(O.10) A = 0.92-0.54 = 0.38 Finally, E = AM0 = 0.38 MO represents the approximate energy associated with the angles of interest. This represents a 62 percent reduction in wave energy due to sheltering. CONCLUSION: The directional spectrum is useful in applications where the angular distribution of wave energy is of interest. One such example has been presented. l'he wrapped normal spreading function is a good model for obtaining an approximation to the directional spectrum of the sea surface. This is due to its relative simplicity, and it can be evaluated using existing tables. ADDITIONAL INFORMATION: Contact Mr. J. Michael Hemsley, CERC Measurement and Analysis Branch, (601) 634-2075. Prototype REFERENCES: Long, R. B. 1980. "The Statistical Evaluation of Directional Spectrum Estimates Derived from Pitch/Roll Buoy Data," Journal of Physical Oceanography, Vol 10, p 944. Mathiesen, M. 1984 (Feb). "Personal Communication." Panicker, N. N. 1974. "Review of Techniques for Directional Wave Spectra," Ocean Wave Measurement and Analysis Proceedings, ASCE, New Orleans, LA, Vol 1, pp 669-688. Sand, S. E. 1981 (Sep). "Short and Long Wave Directional Spectra," Proceedings of the Conference on Directional Wave Spectra Applications, ASCE, New York, N.Y. 1984. "Deterministic Decomposition of Pitch-and-Roll Buoy Measurements," ioastal Engineering, Vol 8, pp 243-263. Selby, S. M. 1981. CRC Standard Mathematical Tables, 25th ed., Boca Raton, Florida. 5