Surf Research in Hawaii: Using Historical Records to Improve Surf and Coastal Flood Forecasts

transcript

Mr. Patrick CaldwellPacific Islands LiaisonNOAA/NESDIS Data CentersAugust 27, 2009

Photo: Debbie and Kimbal Milikan

Talk Outline*Background and motivation*Validating historic surf observations*Translating observations*Empirical method to estimate surf*Surf-related coastal flood forecasts* Buoy spectral density composites (not time for all)

My Background:-Surfer in high school, 1970s(South Carolina)

-Meteorology FSU,1984

-NOAA Data Center, UH Ocean. Dept, 1987

-Surf forecasting -Email 1993-1997

-Internet 1997

-NWS 2002-present

Example of EmailForecast 1995

Background

November 9, 2002

Collaborative Surf Forecast is Born

Only Deep Water Swell Height, Period, and Direction No Surf Heights

Surf Technical Advisory Group results: How to explicitly define surf height? How to translate deep water swell to surf heights? How to validate those heights?

Research Focus: North Shore - best available data

Photo: A.MozoBillabong XXL 2007

Data: Buoys and Visual Surf Observations

Buoys Advantages:-Around the clock, high freq. samples-Wave spectrum

Disadvantages in understanding surf-Data gaps-Not surf height

Historic Visual Surf Database, 1968-Present

Primary visualreporting locations

Goddard-Caldwell Dataset

Wave Cams

Daily Observations:- Surf News Network- Lifeguards-Coconut wireless- recent years: cams

Database CaretakerLarry Goddard: 1968-1987Pat Caldwell: 1987-present

Daily value(upper-end of reported range (H1/10) for timeof day of highestbreakers)

Recent years:Validation, Internet surfPictures on web

Visual Surf Observations

Pros: - explicitly quantify breaker size - inherent knowledge base - longest, most continuous (daily data since 8/1968)

Cons: - subjectivity - only daylight, only few times/day - historically (often now) made in Hawaii scale

Why surf observations are important? - validation - surf climatology - research (eg., empirical estimates) *most requested NODC dataset in Hawaii

Observations in history/science

Hawaiian language:135 words: moods of sea and surf149 words: wind87 words: rain27 words: clouds

Harold Kent, “Treasury of Hawaiian Words in 101 Categories”

Beaufort Wind ScaleDeveloped in 1805 by Sir Francis Beaufort of England

Visual observationsto estimate wind speedsat sea on a scale of 1-12

Other Observations used In science: rogue waves

2002- Hawaii Scale in the periscope!!!!!!

Totally tastytubes, brah

1) Spatial variabilitySimulating Waves Nearshore (SWAN) Model

Incident 2.5 m, 14 second from 315o

Incident 6.5 m, 19 second from 317o

20misobath

Height (m)

Understanding surfobservations in termsof spatial and temporal

surf height variability

Until extra-large or higher!(Waimea the reporting spot)

Surf observations made at zones of high refraction

Simulating Waves Nearshore (SWAN) Model

Caldwell, 2005, J.Coas.Res.

2) Temporal variabilityWhat is the range given in surf reports?(if report given as X to Y (ocn Z), what does that mean?)

29 November, 2004

Waimea Buoy: 8’ 17 sec 325 deg:

Aloha, this is GQ withyour morning report,Sunset is 8-10 ocn 12

Photo courtesy: Merrifield/Millikan

Which heights occur more often?For heights of people filling astadium, most would be centered closely around theaverage height, with farless people at the extremeshort or tall level.

Over a given time period, ifevery wave is sized and counted,most of the waves will be lessthan the average wave height.

Most frequent

Average height

Significant height (H1/3)

H1/100Wave height

Count ofpeople of each size

5’ 5.5’ 6’ 6.5’height

average

Waves are different—Rayleigh Distribution

Normal Distribution

H1/100 = 1.32 * H1/10

H1/3 = 0.79 * H1/10

Count ofwaves of each size

For Rayleigh distributions, one parameter can be calculatedfrom another using simple multiplicative constants, forexample, knowing the H1/10, one can calculate

29 November, 2004

Benchmarks(surfers)

Surf report: H1/3 to H1/10, ocn H1/100

With dominant energy 14-20 sec,roughly 4 waves per minute, or100 waves in 25 minutes. Assume-waves in each set similar size- idealized 3 waves per setH1/3: mean of highest 33, or 11 setsIn 25 minutes, or one set every 2.5 minH1/10: ave of highest 10, or 3 sets in 25 minor one set every 8.5 minutesH1/100th: one set in 75 minuteshighest 3 waves out of 300 waves(clean up or sneaker set)

*waves are constantly arriving, however, reportswere traditionally made by surfers for surfers who emphasize the smaller percentage of larger waves

Just as waves arrive in groups, or sets as surfers call it, there are also groups of groups, that is, spells (~0.5-2 hours) with muchmore frequent arrivals, and conversely, low energy time spans.

Active arrivalpattern

Lull inarrivals

Kilo Nalu Wave Sensor,offshore Honolulu duringhigh southerly swell episode

Is Hawaii scale non-scientific(ie, inconsistent?)

All Visual Surf Observations:-Course resolution-Hour to hour variability-Error increases with size-Research shows tendencyto underestimate surf heights

What makes a dataset valid? ConsistencyData Criteria-Oct-March-light winds-daylight hrs

Buoy-Estimate-Assumes no loss of energy due to bottom friction-No refraction*only a proxy (test value)-Daylight maximum (assume 10 hr travel time)

Validation of North Shore Surf ObservationsSurf Observation minus Buoy-estimated Surf Height

Surf observations are temporally consistent

Ratio =Difference /EstimatedHeight

Another show of confidence in the GC dataset- high correlation to the buoy-estimated surf height

Difference shows a quasi-normal distribution

Three-way Comparisons: Buoy 51001, Waimea Buoy, and GC Observations (directionally filtered-- NW and NNW only)

Kauaishadowingof WNWcomponent

High correlation among the three datasets—gives more confidence in GC database

Error Estimates

Magnitude of Error increases with height

AverageError ~15%

North Shore Oahu Surf Climatology

Caldwell, 2005, Validity of North Shore Surf Observations, Journal Coastal Res.

SEP OCT NOV DEC JAN FEB MAR APR MAY

WNW-NW

NW-NNW

North Shore Surf Direction Climatology

Caldwell, 2005, JCR

No.DaysPerMonth(> 2 Hsf)

Surf Climatology

Caldwell, 2005, JCR

Photo: C.Ferrari

Sunset, November 22, 2002, Hsf=8

Translation from Hawaii Scale to Trough-to-Crest Heights

Value recorded in the Goddard-Caldwell database

The trough-to-crest surf height is defined as the vertical distance between the crest and the preceding trough at the moment and location along the wave front of highest cresting. For zones of high refraction with A-shaped peaks, theheight refers to the center of the “A”.

Errors: - trough identification ~ 10% of height - five-feet unit ~ +/- 6 inches or 10% of height

Next Project:

Presented: Wave WorkshopTurtle Bay, Nov. 2004

Method: Photographic Evidence

Translation is a factor of twoFor the full range of breaker sizes Encountered in Hawaii within the10-20% margin of error.

This assumes the height is defined asthe vertical distance between the crestand the preceding trough at the moment and location along the wave front

of highest cresting and zones of high refraction (outer reefs) are included for extreme days when Waimea Bay was the reporting location.

Caldwell and Aucan 2007, J.Coas.Res.

Photo:Jamie Ballenger

Waimea, Jan. 25, 2003, HSF=25

The Waimea Curveball: translation Hawaii scale to Face changes

Historic Database from zones of highest refraction until Sunset Beach is too large (~15 Hawaii scale). For days of heights >= 15 Hawaii scale, Waimea was/is the reportinglocation. However, under such conditions, this is no longer a zone of maximum refraction.

StudyArea

Waimea buoy

Photo: C.Ferrari

Waimea, January 10, 2004

Case Study: Three Sites, Same Day

Assume H1/10th (chose photographs with higher heights)

Photo: Hankfotos.com, Surfer: K.Bradshaw

Outside Logs, January 10, 2004

Hank verified “H1/10th”, not clean-up set

Photo: E.Aeder Surfer: P.Cabrina, Note: Billabong XXL 2004 winner, as 70’

Peahi (Jaws), January 10, 2004

This likely H1/100“Sneaker Set”

**Result: 1968- visual surf observations translated to peak face, for extra-large days, refers to zones of high refraction on outer reefs

Deep water significant wave heightdoes not mirror energy flux at shore– need at least dominant wave period orideally directional spectra

Kailua, January 27, 2008, photo: P.Caldwell

Project: Estimate surf from deep water data/predictions

January 19, 2008, Sunset BeachWaimea buoy: 7’, 15 sec

Photo: Alan Mozo

where: Hb = shoaling-only predicted wave height at breaking Ho = deep water significant wave height P = dominant wave period g = gravity

H b oH g gP4 5

[( / )( / )]

Empirical Method:

Data: - Daily Surf Observations (HSF * 2) - Waimea Buoy maximum between 7am-5pm

* Conservation of energy flux* Ignores refraction, diffraction, bottom friction, currents, wave-wave interactions, and wind

Following Komar and Gaughan, 1973

Days removed from data: - strong trades - moderate or stronger onshore winds - 10o < wave direction < 270o

Kr(Hb) = -0.003*Hb3 + 0.0099*Hb

2 - 0.0250*Hb + 1.0747Hsurf = Hb * Kr(Hb)

Kr: coefficient of refractionHb: shoaling only estimatorHsurf: estimated surf height (shoaling + refraction)

H1/100 = 1.32 * H1/10

H1/3 = 0.79 * H1/10

PushWaimeaBuoy dataThroughFormula

Note howthe spreadamongst theH1/3 to H1/100increases withsize, matchingwell with observations

Journal of Coastal Research, Sept. 2007

NWSHigh surfadvisory

NWSHigh surfwarning

Weakness:1) Short-period(windswellcorrectionadapted)

2) Extremesurf (fewvalidation points)

3) Widespectra –overcalls it,break energyInto separatebands

MotivationHistorical Context ForUnderstanding Wave Run-up

Journal of Coastal ResearchMay 2009Coinciding High Surf/TidesNorth Shore, Oahu

Photo: Dolan Eversole, DLNR

High Wave Run-up fromWinter Extratropical Cyclones

Jan 30, 2007

Wave Runup Issues:

Safety!

December 1-4, 1969

Back-to-back giantsurf episodes

($1500K 1970 dollars)

Neap tides!

PropertyProtection

Overview: MethodologyData Waves: 51001, Waimea Tides: Haleiwa and Kaneohe

Procedure Correct 51001 Hs to Waimea Calculate hourly surf height Compare surf to tides, sort by category Derive recurrence, duration, joint probability

Caldwell et. al. 2009, J.Coas.Res.

Example: Haleiwa Predicted Tides 2007

*Categories of tidal level based on standard deviations

Heights above 1, 1.5, and 2 σ occur 15.6, 7.2, and 2.5% of the time

Semi-diurnalmixed tide

Hs: 51001 versus Waimea Buoy

Why 51001 > Waimea?

-Closer to source * attentuation from dispersion greater closer to source * big surf episodes in Hawaii, source closer, so difference greater-Shadowing Niihau/Kauai

Results

Results-Decrease in occurrence as surf height and tide increase

-Hawaii scale used as basis for surf height categories *essential for validation *based on bench marks *temporally consistent (Caldwell, JCR, 2005)

Case Study: February 23, 1986

1 σ 1.5 σ 2 σ

15 Hsf

20 Hsf

25 Hsf

30 Hsf

10 Hsf

suspiciousdata

+C.Kontoes: Dept. of Transportation, NWS Storm Data

No sand LanisSand Lanis

Other Validation

1/13/2008, 7:10 am, buoy ~ 28 Hsf tide ~ 1.24 σ(HNL sea level anomaly 1/08 2.2cm)

1/30/2007 1:22am, buoy ~ 27 Hsf tide ~ 1.73 σ(HNL sea level anomaly 1/07: 3.9 cm)

Photo: PC 9:45am

Photo: D.Eversole, ~8am

1 σ 1.5 σ 2 σ

15 Hsf

20 Hsf

25 Hsf

30 Hsf

10 Hsf

suspiciousdata

+C.Kontoes: Dept. of Transportation, NWS Storm Data

No sand LanisSand Lanis

Marginal

Significant

Extreme

Nominal Categorizing

12/04/2007

12/07/2006

Joint Probability Model

Contours are annual average number of hoursNote widespread nature of extremes

Exceedence distributionis one minus cumulativedistribution

Assume Hs and tides independent

Photo: Patrick Holzman

-Surf information vital for …*protection of life and property*understanding near shore processes - beach dynamics - ecosystem variability - engineering - coastal planning

Surf Research in Hawaii: Using Historical Records to Improve Surf and Coastal Flood Forecasts

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