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

Surf Research in Hawaii: Using Historical Records to Improve Surf and Coastal Flood Forecasts. Mr. Patrick Caldwell Pacific Islands Liaison NOAA/NESDIS Data Centers August 27, 2009. Photo: Debbie and Kimbal Milikan. Talk Outline. *Background and motivation

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

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  1. Surf Research in Hawaii: Using Historical Records to Improve Surf and Coastal Flood Forecasts Mr. Patrick Caldwell Pacific Islands Liaison NOAA/NESDIS Data Centers August 27, 2009 Photo: Debbie and Kimbal Milikan

  2. 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)

  3. Background • 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 Email Forecast 1995

  4. November 9, 2002 Collaborative Surf Forecast is Born

  5. 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?

  6. Research Focus: North Shore - best available data Photo: A.Mozo Billabong XXL 2007

  7. 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

  8. Goddard-Caldwell Dataset Historic Visual Surf Database, 1968-Present Database Caretaker Larry Goddard: 1968-1987 Pat Caldwell: 1987-present • Daily Observations: • - Surf News Network • - Lifeguards • Coconut wireless • - recent years: cams Daily value (upper-end of reported range (H1/10) for time of day of highest breakers) Primary visual reporting locations Recent years: Validation, Internet surf Pictures on web Wave Cams

  9. 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

  10. Observations in history/science Hawaiian language: 135 words: moods of sea and surf 149 words: wind 87 words: rain 27 words: clouds Harold Kent, “Treasury of Hawaiian Words in 101 Categories”

  11. Beaufort Wind Scale Developed in 1805 by Sir Francis Beaufort of England Visual observations to estimate wind speeds at sea on a scale of 1-12 Other Observations used In science: rogue waves

  12. 2002- Hawaii Scale in the periscope!!!!!! Totally tasty tubes, brah

  13. Simulating Waves Nearshore (SWAN) Model Incident 2.5 m, 14 second from 315o 20m isobath 315o Understanding surf observations in terms of spatial and temporal surf height variability Surf observations made at zones of high refraction Height (m) Incident 6.5 m, 19 second from 317o 1) Spatial variability Simulating Waves Nearshore (SWAN) Model Until extra-large or higher! (Waimea the reporting spot) Caldwell, 2005, J.Coas.Res.

  14. 29 November, 2004 Aloha, this is GQ with your morning report, Sunset is 8-10 ocn 12 2) Temporal variability What is the range given in surf reports? (if report given as X to Y (ocn Z), what does that mean?) Photo courtesy: Merrifield/Millikan Waimea Buoy: 8’ 17 sec 325 deg:

  15. Which heights occur more often? Normal Distribution For heights of people filling a stadium, most would be centered closely around the average height, with far less people at the extreme short or tall level. Count of people of each size average 5’ 5.5’ 6’ 6.5’ height Waves are different— Rayleigh Distribution Most frequent Count of waves of each size Average height Over a given time period, if every wave is sized and counted, most of the waves will be less than the average wave height. Significant height (H1/3) H1/10 Wave height H1/100 For Rayleigh distributions, one parameter can be calculated from another using simple multiplicative constants, for example, knowing the H1/10, one can calculate H1/100 = 1.32 * H1/10 H1/3 = 0.79 * H1/10

  16. *waves are constantly arriving, however, reports were traditionally made by surfers for surfers who emphasize the smaller percentage of larger waves 29 November, 2004 Benchmarks (surfers) • With dominant energy 14-20 sec, • roughly 4 waves per minute, or • 100 waves in 25 minutes. • Assume • waves in each set similar size • idealized 3 waves per set • H1/3: mean of highest 33, or 11 sets • In 25 minutes, or one set every 2.5 min • H1/10: ave of highest 10, or 3 sets in 25 min • or one set every 8.5 minutes • H1/100th: one set in 75 minutes • highest 3 waves out of 300 waves • (clean up or sneaker set) Surf report: H1/3 to H1/10, ocn H1/100

  17. Kilo Nalu Wave Sensor, offshore Honolulu during high southerly swell episode Lull in arrivals Active arrival pattern 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 much more frequent arrivals, and conversely, low energy time spans.

  18. All Visual Surf Observations: • Course resolution • Hour to hour variability • -Error increases with size • -Research shows tendency • to underestimate surf heights Is Hawaii scale non-scientific (ie, inconsistent?)

  19. What makes a dataset valid? Consistency Data 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.

  20. Validation of North Shore Surf Observations Surf Observation minus Buoy-estimated Surf Height Ratio = Difference / Estimated Height Surf observations are temporally consistent Caldwell, 2005, J.Coas.Res.

  21. 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.

  22. Three-way Comparisons: Buoy 51001, Waimea Buoy, and GC Observations (directionally filtered-- NW and NNW only) Kauai shadowing of WNW component High correlation among the three datasets— gives more confidence in GC database

  23. Error Estimates Magnitude of Error increases with height Average Error ~15% Caldwell, 2005, J.Coas.Res.

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

  25. North Shore Surf Direction Climatology No. Days Per Month (> 2 Hsf) Caldwell, 2005, JCR

  26. Surf Climatology Caldwell, 2005, JCR

  27. Next Project: Translation from Hawaii Scale to Trough-to-Crest Heights Method:Photographic Evidence Value recorded in the Goddard-Caldwell database Sunset, November 22, 2002, Hsf=8 5’ Photo: C.Ferrari 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, the height refers to the center of the “A”. Errors: - trough identification ~ 10% of height - five-feet unit ~ +/- 6 inches or 10% of height Presented: Wave Workshop Turtle Bay, Nov. 2004

  28. Translation is a factor of two For the full range of breaker sizes Encountered in Hawaii within the 10-20% margin of error. This assumes the 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 crestingand 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.

  29. The Waimea Curveball: translation Hawaii scale to Face changes Waimea, Jan. 25, 2003, HSF=25 Photo:Jamie Ballenger 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 reporting location. However, under such conditions, this is no longer a zone of maximum refraction.

  30. Waimea buoy Study Area Caldwell and Aucan 2007, J.Coas.Res.

  31. Case Study: Three Sites, Same Day Waimea, January 10, 2004 Photo: C.Ferrari 5’ Assume H1/10th (chose photographs with higher heights)

  32. Outside Logs, January 10, 2004 5’ Photo: Hankfotos.com, Surfer: K.Bradshaw Hank verified “H1/10th”, not clean-up set

  33. Peahi (Jaws), January 10, 2004 This likely H1/100 “Sneaker Set” 5’ Photo: E.Aeder Surfer: P.Cabrina, Note: Billabong XXL 2004 winner, as 70’ **Result: 1968- visual surf observations translated to peak face, for extra-large days, refers to zones of high refraction on outer reefs

  34. Project: Estimate surf from deep water data/predictions Deep water significant wave height does not mirror energy flux at shore– need at least dominant wave period or ideally directional spectra Kailua, January 27, 2008, photo: P.Caldwell

  35. January 19, 2008, Sunset Beach Waimea buoy: 7’, 15 sec Photo: Alan Mozo

  36. Empirical Method: Data: - Daily Surf Observations (HSF * 2) - Waimea Buoy maximum between 7am-5pm Following Komar and Gaughan, 1973 Days removed from data: - strong trades - moderate or stronger onshore winds - 10o < wave direction < 270o * Conservation of energy flux * Ignores refraction, diffraction, bottom friction, currents, wave-wave interactions, and wind

  37. Kr(Hb) = -0.003*Hb3 + 0.0099*Hb2 - 0.0250*Hb + 1.0747 Hsurf = Hb * Kr(Hb) Kr: coefficient of refraction Hb: shoaling only estimator Hsurf: estimated surf height (shoaling + refraction) Caldwell and Aucan 2007, J.Coas.Res.

  38. Push Waimea Buoy data Through Formula H1/100 = 1.32 * H1/10 H1/3 = 0.79 * H1/10 Note how the spread amongst the H1/3 to H1/100 increases with size, matching well with observations Journal of Coastal Research, Sept. 2007

  39. Weakness: • Short- • period • (windswell • correction • adapted) • 2) Extreme • surf (few • validation • points) • 3) Wide • spectra – • overcalls it, • break energy • Into separate • bands NWS High surf warning NWS High surf advisory

  40. High Wave Run-up from Winter Extratropical Cyclones Motivation Journal of Coastal Research May 2009 Coinciding High Surf/Tides North Shore, Oahu Historical Context For Understanding Wave Run-up Photo: Dolan Eversole, DLNR Jan 30, 2007

  41. Wave Runup Issues: Safety!

  42. Property Protection December 1-4, 1969 Back-to-back giant surf episodes ($1500K 1970 dollars) Neap tides!

  43. Data Waves: 51001, Waimea Tides: Haleiwa and Kaneohe Overview: Methodology 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.

  44. 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.

  45. Semi-diurnal mixed tide Caldwell et. al. 2009, J.Coas.Res.

  46. 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.

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

  48. 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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