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CARIBBEAN METOCEAN CLIMATE STATISTICS (CARIMOS)

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Presentation on theme: "CARIBBEAN METOCEAN CLIMATE STATISTICS (CARIMOS)"— Presentation transcript:

1 CARIBBEAN METOCEAN CLIMATE STATISTICS (CARIMOS)
Specification of the extreme and normal wind and wave climate and basin scale storm surge and 2-D currents by a hindcast approach Vincent J. Cardone, President Oceanweather Inc. Cos Cob, CT, USA

2 CARIMOS Hindcast Approach
Hindcast 109 tropical cyclones 1921 – 1999 Hindcast 15-continuous years 1981 – 1995 Winds and waves: triple nested approach to account for North Atlantic swell input, complex basin morphology, shallow water and island sheltering: 1.25°/.25°/.065° Basin storm surge with 2D hydro model: .25°

3 NOAA National Hurricane Center HURDAT Tape. Tropical Cyclone Deck
Tropical Cyclones NOAA National Hurricane Center HURDAT Tape. Tropical Cyclone Deck Aircraft Reconnaissance Data. Annual Tropical Storm Reports and Summaries. Published Accounts of Caribbean Storms. Continuous Wind Fields Northern Hemisphere Surface Analysis Final Analysis Series NOAA Tropical Strip surface analyses NOAA NCDC TDF-11 format ship report files Climatological summaries Gridded pressure fields (NCDC Global Historical Fields CD) Global so-called WMO 1000-mb wind fields Gridded digital products of the new NOAA NCEP Global Reanalysis Project, specifically the 6-hourly 1000 mb and 10m surface wind fields.

4 Hindcast Model Attributes
Swell Grid GRID SWELL

5 Coarse Grid GRID COARSE

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7 Fine Grid GRID FINE

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9 Tracks of Tropical Cyclones

10 Tropical Wind Model (TC96)
Diagnostic PE PBL Model Wind field “snapshots” Buoy/Rig comparisons show accuracy of +/- 20º in wind direction and +/- 2 m/s in wind speed Applied in worldwide tropical basins

11 Tropical Model Snapshot Inputs
Speed and direction of vortex motion Equivalent geostrophic flow of the ambient PBL pressure field in which the vortex propagates Central Pressure Scale radius of exponential radial pressure profile Holland’s profile peakedness parameter

12 Snapshot Solutions

13 Wind WorkStation (WWS)
Graphical analysis tool for the analysis of marine surface wind fields Blending tropical wind field into synoptic scale Direct analysis of tropical winds

14 Wind Fields in Tropical Cyclones

15 Tropical Wind Field Methodology
Model Inputs Tropical Model WWS Wave Model

16 Aircraft Reconnaissance
North Atlantic 1944 – present Western North Pacific Image Courtesy of TPC

17 Pressure Profile Fit to Aircraft Data

18 35/50 Knot Radii Image Courtesy of JTWC

19 Tropical Cyclone Snap Database
Input Output Dp: 2 to 120 mb every 2 mb Rp 5 to 120 Nmi every 4 Nmi B 1 to 2.5 every .1 Maximum Surface Wind Radius of Maximum Wind Azimuthally Averaged Wind Speed in 5 Nmi Bins Radius of 35/50 Knots

20 QUIKSCAT Scatterometer Winds

21 Possible Model Solutions for Dp=90, B=1.00

22 Sample QUIKSCAT Fit

23 Wind Analysis Distributed Application (HWind)

24 HWind vs. Tropical Model Winds
TC96 Winds on Sept 15th, GMT HWind: 37.3 m/s NHC: 45.4 m/s TC96/QuikScat: 43.3 m/s

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26 Sample Workstation Rp Fit in CARIMOS Hurricane

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30 Sample CARIMOS Hurricane Wind Hindcast

31 Sample CARIMOS Wave Hindcast

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37 Continuous Wind Fields
NOAA NCEP Reanalysis 10-m winds First iteration revealed biases Interactive Objective Kinematic Analysis (IOKA)

38 Validation Tropical Cyclones – rely on extensive prior validation in tropical regimes such as Gulf of Mexico, South China Sea… Continuous – utilize buoy and satellite altimeter measured winds and sea states

39 Model Validation - Buoy Data

40 Hindcast vs Buoy 41018

41 Wind and Wave Statistics for CARIMOS Operational Hindcast vs
Wind and Wave Statistics for CARIMOS Operational Hindcast vs. Buoy 41018

42 Model Distributional Validation vs. Buoy

43 Model Validation - Altimeter Data

44 Model Distributional Validation - Altimeter

45 Wind and Wave Statistics for CARIMOS Operational Hindcast vs
Wind and Wave Statistics for CARIMOS Operational Hindcast vs. Altimeter Data

46 Model Validation Altimeter

47 CARIMOS Derivative Products
Continuous Hindcast - site specific Time series 15 years, 3-hourly Bivariate distributions (HS-TP, HS-VMD, WS-WD) Duration/persistence distributions Extremes Time series in storms, hourly Return period extremes of WS, HS, HM, HC, TP, Va, Sh 1 year to 100 years

48 CARIMOS Wind and Wave Fields Definitions
Description Date Julian format (where Jan = 1, Jan = 2, etc.) Wind Direction From which the wind is blowing, clockwise from true north in degrees (meteorological convention). Wind Speed 1-hour average of the effective neutral wind at a height of 10 meters, units in meters/second.

49 Total Spectrum Wave Fields:
Total Variance The sum of the variance components of the hindcast spectrum, over the 552 bins of the wave model, in meters squared. Significant Wave Height 4.000 times the square root of the total variance, in meters. Peak Spectral Period Peak period is the reciprocal of peak frequency, in seconds. Peak frequency is computed by taking the spectral density in each frequency bin, and fitting a parabola to the highest density and one neighbor on each side. If highest density is in the Hz bin, the peak period reported is the peak period of a Pierson-Moskowitz spectrum having the same total variance as the hindcast spectrum. Vector Mean Direction To which waves are traveling, clockwise from north in degrees (oceanographic convention).

50 Second Spectral Moment
First Spectral Moment Following Haring and Heideman (OTC 3280, 1978) the first and second moments contain powers of  = 2f; thus: where dS is a variance component and the double sum extend over 552 bins. Second Spectral Moment where dS is a variance component and the double sum extend over 552 bins. Dominant Direction Following Haring and Heideman, the dominant direction  is the solution of the equations The angle  is determined only to within 180 degrees. Haring and Heideman choose from the pair (, +180) the value closer to the peak direction.

51 In-Line Variance Ratio
Angular Spreading The angular spreading function (Gumbel, Greenwood & Durand) is the mean value, over the 552 bins, of cos( -VMD), weighted by the variance component in each bin. If the angular spectrum is uniformly distributed over 360 degrees, this statistic is zero; if uniformly distributed over 180 degrees, 2/; if all variance is concentrated at the VMD, 1. For the use of this statistic in fitting an exponential distribution to the angular spectrum, see Pearson & Hartley, Biometrika Tables for statisticians, 2:123 ff. Angular spreading (ANGSPR) is related to cosn spreading as follows: n = (2*ANGSPR)/(1-ANGSPR) In-Line Variance Ratio Directional spreading by Haring and Heideman, p Computed as: If spectral variance is uniformly distributed over the entire compass, or over a semicircle, Rat = 0.5; if variance is confined to one angular band, or to two band 180 degrees apart, Rat = According to Haring and Heideman, cos^2 spreading corresponds to Rat = 0.75.

52 Wave Partition Fields (Sea/Swell):
Total Variance of “Sea” Partition Peak Spectral Period of “Sea” Partition Vector Mean Direction of “Sea” Partition Total Variance of “Swell” Partition Peak Spectral Period of “Swell” Partition Vector Mean Direction of “Swell” Partition Explanation of sea/swell computation: The sum of the variance components of the hindcast spectrum, over the 552 bins of the wave model, in meters squared. To partition sea (primary) and swell (secondary) we compute a P-M (Pierson-Moskowitz) spectrum, with a cos3 spreading, from the adopted wind speed and direction. For each of the 552 bins, the lesser of the hindcast variance component and P-M variance component is thrown into the sea partition; the excess, if any, of hindcast over P-M is thrown into the swell partition.

53 Sample Storm Peak Table
Storm Peaks 23 Peaks found > 4 within a window of 72 hours. CS Gpt 3106, Lat 15.0n, Long 75.0w, Depth m Defined Period: Storms Date Ranges: 9/8/ :00 to 10/2/ :00 CCYYMM DDHHmm WD WS ETOT TP VMD MO1 MO2 HS DMDIR ANGSPR INLINE HSUR CS CD MaxWAVE CREST W/HS C/HS deg m/s m^2 sec deg m^2/s m2/s m deg cm cm/s deg m m

54 Computation of Maximum Individual Wave Height in a Storm
The program evaluates Borgman's (1973) integral: where H is the largest wave height; a2 is the mean square height taken as a function of time, t; ta and tb are the beginning and end of the storm; and T(t) is the wave period, taken here is the significant wave period. Maximum Individual Wave Height (Forristall, 1978): T = M0/M1

55 Computation of Maximum Individual Crest Height in a Storm
Maximum Crest Height (Haring and Heideman, 1978): where h is elevation and d is water depth T = .74 TP TP is the reciprocal of fpeak, found by solving by inverse interpolation The median of the resulting distribution was taken as the maximum expected single peak in the storm.

56 Extremal Analysis Storm Peak Table Issues
Peaks over threshold vs annual maximum Selection of distribution Selection of fitting threshold Site averaging

57 Algorithm - Return-Period Extremes Calculation of Return-Period Extremes
The distributional assumptions used are: Gumbel distribution of extremes: Borgman distributions of extremes, i.e., Gumbel distribution of squared extremes: 3. Galton distribution of height, i.e. normal distribution of log heights: 4. Weibull distribution described in next section The fitting procedure of Gumbel (1958, pp ) was followed for Gumbel, Borgman and Galton, with plotting positions based in i/(n+1), often called Weibull plotting position. , where

58 Climate Variability - Impact on Design Wave Criteria
Cold Years 100-year Hs 7.4m (Gumbel) 100-year Hs 7.9m (Weibull) Warm Years 100-year Hs 9.9m (Gumbel) 100-year Hs 10.3m (Weibull) Overall 100-year Hs 9.0m (Gumbel) 100-year Hs 9.2m (Weibull)

59 (From Goldberg et al., Science, 293, 20 July, 2001)
Tropical Cyclone Variability (From Goldberg et al., Science, 293, 20 July, 2001)

60 Significant Wave Height Interannual Variability of Exceedence Value

61 Significant Wave Height Interannual Variability of Exceedence Value

62 Conclusions Demonstration that CARIMOS provides a very skillful model for normal conditions Importance of due consideration to decadal climatic trends, 25% difference in extreme wave heights Importance of due consideration in inter- annual variability upto 100% difference in values of 90% and 99% exceedences

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