A REAL TIME, ON-LINE COASTAL INFORMATION PROGRAM IN BRAZIL

by

Eloi Melo & LAHIMAR crew:

F.M. Pimenta, D.A.R. Mendes, G.R. Hammes, C.E.S. Araujo, D. Franco, J.H.G.M. Alves, R.C. Barletta,

A.M. Souto, G. Castelão, N.C. Pereira, F.V. Branco

LAHIMAR - Maritime Hydraulics Laboratory Federal University of Santa Catarina

Florianopolis, BRAZIL.

<www.lahimar.ufsc.br>

TALK OUTLINE:

• Description of Monitoring System• Data made available to the Public• Wave Forecasting System• Mooring Failures• Time Series of Sea State Parameters• Conclusions

Where are we in the world ?

Waverider Buoy - Monitoring Program Scheme

Ship used to deploy instruments – Land-based Station

Details of Mooring System

Data made available to the public (for free) in “real time” through the internet www.lahimar.ufsc.br

Sea state parameters: Hs, Tp and Dir.

Sea Surface Temperature - Sense of Buoy’s Drift

Time Evolution of Frequency Spectra

WAVE FORECASTING SYSTEM (operational) : WW3 forced with NCEP/NOAA wind fields

Example of Regional Wave Forecasting System Hs output for Florianópolis - April / 2003

MOORING FAILURESBuoy escaped from the mooring twice in 1.5 years

First escape in December / 2002

Second escape in April/2003 during an extra-tropical cyclone which generated strong swell

and shelf currents at Southern Brazilian Coast

TIME SERIES OF SEA STATE PARAMETERS:

Hs = Significant Wave Height

TIME SERIES OF SEA STATE PARAMETERS:

Tp = Peak Period

TIME SERIES OF SEA STATE PARAMETERS:

Dir = Dominant Wave Direction (incoming)

TIME SERIES OF SEA STATE PARAMETERS:

SST = Sea Surface Temperature

Mid-Shelf Current Directions ???... Possible to infer from buoy’s drift measurements !!!

CONCLUSIONS:

Sea Monitoring Program has been going on for

1.5 yearsAmazingly Successful : Web-site had 150,000 visits

so far !Project allowed LAHIMAR to gather extremely

valuable data AND, at the same time, provide a

useful public service !Data collected boosted wave research at UFSC:

please check companion paper later on at this Conference !

Wave Climate off the Southern Brazilian Coast: an Overview

Eloi Melo

Maritime Hydraulics LaboratoryFederal University of Santa Catarina

Perform WAVE CLIMATE STUDY

→ 2 YEARS (2002, 2003) OF DATA !(... Wave Monitoring Program continues ...)

IDEA: ASK THE DATA ABOUT THE WAVE CLIMATE...

HOW ? ... CLUSTER ANALYSIS TECHNIQUE !

04 mHs

SEA STATE CHARACTERIZATION

→ in terms of 3 basic parameters:

1- Significant Wave Height (Hs):

2- Peak Period (Tp):

3 - Dominant Direction (Dir):

→ Careful !! Bi-Modal Spectra are common in the area ...

Ex. of time evolution of frequency Spectra

... Notice second peak development ...

Methodology proposed by Rodriguez and Guedes Soares (1999)

Capture the concomitant occurrence of sea-swell conditions in each measured spectrum

Logarithmic transformation of the spectral density estimator provides frequency independecy for the confidence band interval

2

2/1,

22/,

22/,

22/1,

lnln (f)Slnln (f)Sln

Upper Lower

MULTIMODAL SPECTRA IDENTIFICATION

MULTIMODAL SPECTRA IDENTIFICATION

Expanded “primary waves” dataset (i.e. Tp1, p1 and Tp2, p2) considers

the significant peaks encountered for each spectrum as individual

“primary” wave fields.

SEASON (counts) 1 PEAK % (counts) 2 PEAKS % (counts) 3 PEAKS % (counts) SPRING (1635) 65.32 (1068) 32.42 (530) 2.26 (37) SUMMER (1695) 60.23 (1021) 37.88 (642) 1.89 (32) AUTUMN (2010) 66.77 (1342) 31.59 (635) 1.64 (33) WINTER (2043) 75.43 (1541) 23.74 (485) 0.83 (17) TOTAL (7383) 67.34 (4972) 31.05 (2292) 1.61 (119)

MULTIMODAL SEA STATES SEASONAL DISTRIBUTION

23-26% of Bi-Modal Spectra were found by Guedes Soares &

Nolasco (1992) for a coastal site off Portugal.

Tp x Dir – Scatter Plot for Significant Spectral Peaks

Key parameters for Wave Climate analysis: Tp x Dir

CLUSTER ANALYSIS - 1

I

II

RESULT : Wave “Clusters” = Wave Systems composed of

waves with similar characteristics.

METHODOLOGY

Finding similarities between every pair of objects in the data set by a prescribed distance measure;

Linking objects by well defined rules into a binary hierarchical cluster tree;

Determining a cut off hypothesis for the hierarchical tree to find the final clusters.

)()'( 12babaab xxSxxD Mahalanobis Distance:

Elementary Clusters

xk (p, Tp)

kn

i

ki

kk x

nx

1

1kx ki ,

Mahalanobis Distance best for wave parameters because it automatically accounts for the coordinate axis scaling, corrects for correlation between

the different parameters and can provide curved decision boundaries.

CLUSTER ANALYSIS - 2

Minimizes the sum of the squared within-group distance about the group mean of each parameter for all parameters and all groups

simultaneously at each iteration

WARD’s method (BIJNEN, E.J., 1973)

optimum number of clusters(hierarchical tree division)

20

3

1

1 1

c

c

i

c

iii WW

CLUSTER ANALYSIS - 3

CLUSTER RESULTS: 6 Clusters (Wave Systems) identified)

SEASON

MEAN Tp (s) 2*{Tp STD} (s)

A B C D E F A B C D E F

Spring 14.9 11.7 8.1 4.8 5.1* 8.5* 1.8 2.4 1.4 2 2.1 1.6

Summer 14 11.5 7.5 4.3 7.5 …. 3 2 2.5 1.3 3.7 ….

Autumn 14.6 11.8 9.7 5.9 7.4 …. 3 2 2.8 3 3 ….

Winter 13.2 10.5 8.8 4.0 5.4 …. 2.8 2.8 2.4 1 2.4 ….

Mean 14.2 11.4 8.5 4.7 6.4* 8.5* 2.65 2.3 2.27 1.82 2.8 1.6

Std. 0.75 0.6 0.95 0.83 1.28 …. 0.57 0.38 0.61 0.89 0.71 ….

SEASONMEAN p N (º) 2*{p STD} N (º)

A B C D E F A B C D E F

Spring 139 147 86 36 196* 162* 32 44 38 48 22 40

Summer 151 167 85 25 177 …. 25 24 40 42 32 ….

Autumn 146 169 118 48 180 …. 34 22 38 60 30 ….

Winter 149 164 79 0 201 …. 34 40 23 36 16 ….

Mean 146 162 92 27 188* 162* 31 32 35 47 25 40

Std. 5.2 10 17.6 20.4 11.8 …. 4.3 11.1 7.9 10.2 7.4 ….

CLUSTER RESULTS: 6 Clusters (Wave Systems) identified)

CLUSTER ANALYSIS - Results !

A : longer period distant swells generated in higher latitudes of the

South Atlantic Ocean.

B : swells generated off the coasts of Rio Grande do Sul and Uruguay by

Southerly wind events.

C : stable wave system associated with the fairly persistent NE winds of the semi-permanent high-pressure of the South Atlantic Ocean

D : short seas generated by shorter duration (N-NW) winds that usually blow just before the arrival of a cold front

E : relates to winds from S-SW that usually blow right behind the cold front.

CLUSTERS – Physical Interpretation !

CLUSTERS SEASONAL CONTRIBUTION

CLUSTER Spring Summer Autumn Winter

A 7.7 20.9 18.6 31.6

B 35.5 33.9 37.2 34.7

C 32.1 32.7 22.9 24

D 7.5 5.4 13.1 7.4

E 4.1 7.1 8.2 2.3

F 13.1 ... ... ...

TOTAL 100 100 100 100

#

SPRING SUMMER AUTUMN WINTER

1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd

A 36.6 57 6.4 41.4 54.4 4.2 46.8 47.8 5.4 69.4 29.4 1.2

B 59 39.1 1.9 73.2 26.3 0.5 80.8 19.1 0.1 94.3 5.7 0

C 90.2 9.7 0.1 92 7.8 0.2 88.9 10.9 0.2 94 5.8 0.2

D 58.9 38.7 2.4 17.1 80.6 2.3 55.2 43.9 0.9 12.7 84.1 3.2

E 76.1 17.4 6.5 86 12.8 1.2 95 4.5 0.5 69 31 0

F 97.6 2.4 0 … … … … … … … … …

Occurrence of Wave Systems as 1st 2nd and 3rd Peaks (%)

Relations SPRING (%)

SUMMER (%)

AUTUMN (%)

WINTER (%)

A - B 1.1 7.3 4.9 19

A - C 14.9 29.1 15.7 29.5

A - D 3.2 1.6 11 10.1

B - C 35.8 30.7 8.5 4.1

B - D 15.7 11.1 22.5 20

B - E 4.2 5.5 11.8 3.1

B - F 11.7 ... ... ...

C - D 4.2 4.4 10.6 2.7

COMBINED OCURRENCE OF WAVE SYSTEMS

WAVE SYSTEMS Temporal Evolution

CONCLUDING REMARKS

• Sea [8s E 1.25m] and Swell [12s S 1.25-2.5m] are well defined in the Southern Brazilian wave regime. Hs > 4m may occur in all seasons (although not frequently).

• The “primary” wave fields dataset (obtained by the multimodal spectra peak identification methodology) reveals that about one third of the sea-swell structure, actually refers to the simultaneous presence of both Sea and Swell.

• Cluster Analysis Technique allowed the identification of five different wave systems and indicated also how these systems may combine to form multimodal spectra.

• Bimodal Seas are mainly represented by the superposition of the stable NE wind sea with one of the Swell systems or by the co-occurrence of the near field Swell and the two stage wave systems related to the cold front passage.

• The Clustering Methodology used in this work appears to be a useful and promising tool. The use of the primary waves energy content and the directional spreading as clustering parameters is likely to improve wave systems definition.

THANK YOU !!