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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,

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Presentation on theme: "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,"— Presentation transcript:

1 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.

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

3 Where are we in the world ?

4 Waverider Buoy - Monitoring Program Scheme

5 Ship used to deploy instruments – Land-based Station

6 Details of Mooring System

7 Data made available to the public (for free) inreal time through the internet Sea state parameters: Hs, Tp and Dir.www.lahimar.ufsc.br

8 Sea Surface Temperature - Sense of Buoys Drift

9 Time Evolution of Frequency Spectra

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

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

12 MOORING FAILURES Buoy escaped from the mooring twice in 1.5 years First escape in December / 2002

13 Second escape in April/2003 during an extra-tropical cyclone which generated strong swell and shelf currents at Southern Brazilian Coast

14 TIME SERIES OF SEA STATE PARAMETERS: Hs = Significant Wave Height

15 TIME SERIES OF SEA STATE PARAMETERS: Tp = Peak Period

16 TIME SERIES OF SEA STATE PARAMETERS: Dir = Dominant Wave Direction (incoming)

17 TIME SERIES OF SEA STATE PARAMETERS: SST = Sea Surface Temperature

18 Mid-Shelf Current Directions ???... Possible to infer from buoys drift measurements !!!

19 CONCLUSIONS: Sea Monitoring Program has been going on for 1.5 years Amazingly 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 !

20 Wave Climate off the Southern Brazilian Coast: an Overview Eloi Melo Maritime Hydraulics Laboratory Federal University of Santa Catarina

21 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 !

22 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...

23 Ex. of time evolution of frequency Spectra... Notice second peak development...

24 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 UpperLower MULTIMODAL SPECTRA IDENTIFICATION

25 Expanded primary waves dataset (i.e. T p1, p1 and T p2, p2 ) considers the significant peaks encountered for each spectrum as individual primary wave fields.

26 MULTIMODAL SEA STATES SEASONAL DISTRIBUTION 23-26% of Bi-Modal Spectra were found by Guedes Soares & Nolasco (1992) for a coastal site off Portugal.

27 Tp x Dir – Scatter Plot for Significant Spectral Peaks Key parameters for Wave Climate analysis: Tp x Dir

28 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.

29 Mahalanobis Distance: Elementary Clusters x k ( p, T p ) 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

30 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 WARDs method (BIJNEN, E.J., 1973) optimum number of clusters (hierarchical tree division) CLUSTER ANALYSIS - 3

31 CLUSTER RESULTS: 6 Clusters (Wave Systems) identified) SEASON MEAN T p (s) 2*{T p STD} (s) ABCDEFABCDEF Spring *8.5* Summer … …. Autumn … …. Winter … …. Mean *8.5* Std … ….

32 SEASON MEAN p N (º) 2*{ p STD} N (º) ABCDEFABCDEF Spring *162* Summer … …. Autumn … …. Winter … …. Mean *162* Std … …. CLUSTER RESULTS: 6 Clusters (Wave Systems) identified)

33 CLUSTER ANALYSIS - Results !

34 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 !

35 CLUSTERS SEASONAL CONTRIBUTION CLUSTERSpringSummerAutumnWinter A B C D E F TOTAL100

36 # SPRING SUMMERAUTUMNWINTER 1 st 2 nd 3 rd 1 st 2 nd 3 rd 1 st 2 nd 3 rd 1 st 2 nd 3 rd A B C D E F ……………………… Occurrence of Wave Systems as 1 st 2 nd and 3 rd Peaks (%)

37 RelationsSPRING (%) SUMMER (%) AUTUMN (%) WINTER (%) A - B A - C A - D B - C B - D B - E B - F C - D COMBINED OCURRENCE OF WAVE SYSTEMS

38 WAVE SYSTEMS Temporal Evolution

39 CONCLUDING REMARKS Sea [8s E 1.25m] and Swell [12s S m] 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.

40 THANK YOU !!


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