Contributions in Science, Number 187 - Caldwell and Caldwell
Mr. Patrick Caldwell Pacific Islands Liaison NOAA/NESDIS Data Centers August 27, 2009 Photo:...
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Transcript of Mr. Patrick Caldwell Pacific Islands Liaison NOAA/NESDIS Data Centers August 27, 2009 Photo:...
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Mr. Patrick CaldwellPacific Islands LiaisonNOAA/NESDIS Data CentersAugust 27, 2009
Photo: Debbie and Kimbal Milikan
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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)
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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
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November 9, 2002
Collaborative Surf Forecast is Born
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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?
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Research Focus: North Shore - best available data
Photo: A.MozoBillabong XXL 2007
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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
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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
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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
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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”
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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
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2002- Hawaii Scale in the periscope!!!!!!
Totally tastytubes, brah
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1) Spatial variabilitySimulating Waves Nearshore (SWAN) Model
Incident 2.5 m, 14 second from 315o
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.
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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
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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 frequentAverage height
Significant height (H1/3)
H1/10
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
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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
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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
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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
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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)
Caldwell, 2005, J.Coas.Res.
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Validation of North Shore Surf ObservationsSurf Observation minus Buoy-estimated Surf Height
Surf observations are temporally consistent
Ratio =Difference /EstimatedHeight
Caldwell, 2005, J.Coas.Res.
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Another show of confidence in the GC dataset- high correlation to the buoy-estimated surf height
Difference shows a quasi-normal distribution
Caldwell, 2005, J.Coas.Res.
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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
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Error Estimates
Magnitude of Error increases with height
AverageError ~15%
Caldwell, 2005, J.Coas.Res.
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North Shore Oahu Surf Climatology
Caldwell, 2005, Validity of North Shore Surf Observations, Journal Coastal Res.
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0
2
4
6
8
10
12
14
16
18
20
SEP OCT NOV DEC JAN FEB MAR APR MAY
W-WNWWNW-NWNW-NNWNNW-NN-NNE
North Shore Surf Direction Climatology
Caldwell, 2005, JCR
No.DaysPerMonth(> 2 Hsf)
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Surf Climatology
Caldwell, 2005, JCR
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5’
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
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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.
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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.
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StudyArea
Waimea buoy
Caldwell and Aucan 2007, J.Coas.Res.
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5’
Photo: C.Ferrari
Waimea, January 10, 2004
Case Study: Three Sites, Same Day
Assume H1/10th (chose photographs with higher heights)
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5’
Photo: Hankfotos.com, Surfer: K.Bradshaw
Outside Logs, January 10, 2004
Hank verified “H1/10th”, not clean-up set
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5’
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
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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
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January 19, 2008, Sunset BeachWaimea buoy: 7’, 15 sec
Photo: Alan Mozo
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(1) 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
2 5
1 4//
[( / )( / )]
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
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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)Caldwell and Aucan 2007, J.Coas.Res.
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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
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NWSHigh surfadvisory
NWSHigh surfwarning
Weakness:1) Short-period(windswellcorrectionadapted)
2) Extremesurf (fewvalidation points)
3) Widespectra –overcalls it,break energyInto separatebands
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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
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Wave Runup Issues:
Safety!
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December 1-4, 1969
Back-to-back giantsurf episodes
($1500K 1970 dollars)
Neap tides!
PropertyProtection
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Overview: MethodologyData Waves: 51001, Waimea Tides: Haleiwa and KaneoheProcedure 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.
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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
Caldwell et. al. 2009, J.Coas.Res.
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Semi-diurnalmixed tide
Caldwell et. al. 2009, J.Coas.Res.
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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
Caldwell et. al. 2009, J.Coas.Res.
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Results
Caldwell et. al. 2009, J.Coas.Res.
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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)
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Case Study: February 23, 1986
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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 DataNo 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
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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 DataNo sand LanisSand Lanis
Marginal
Significant
Extreme
Nominal Categorizing
12/04/2007
12/07/2006
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Joint Probability Model
Caldwell et. al. 2009, J.Coas.Res.
Contours are annual average number of hoursNote widespread nature of extremes
Exceedence distributionis one minus cumulativedistribution
Assume Hs and tides independent
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Photo: Patrick Holzman
-Surf information vital for …*protection of life and property*understanding near shore processes - beach dynamics - ecosystem variability - engineering - coastal planning