IDENTIFICATION OF WIND SEA AND SWELL EVENTS AND SWELL EVENTS PARAMETERIZATION OFF WEST AFRICA. K. Agbéko KPOGO-NUWOKLO

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1 Workshop: Statistical models of the metocean environment for engineering uses IDENTIFICATION OF WIND SEA AND SWELL EVENTS AND SWELL EVENTS PARAMETERIZATION OFF WEST AFRICA K. Agbéko KPOGO-NUWOKLO IFREMER- Laboratoire Comportement des Structure en Mer (CSM) Brest, September 30 to October 1st

2 PLAN 1- General context 2- A proposed method for wind sea and swell events identification 3- Swell events parameterization 4- Conclusions and Perspectives 2

3 I - General context Sea wave long-term statistics are important in many ocean engineering fields : design against fatigue wave energy harvesting coastal erosion Akpo platform (Nigeria) 3

4 I - General context West Africa sea state conditions 4

5 I - General context West Africa sea state conditions Sea state spectra in West africa often exhibit many peaks due to the presence of multiple wave systems (wind sea and swells). Typical West Africa spectrum 5

6 I - General context The main goal of the thesis is the assessment of the wave climate off West Africa by using a new approach. This approach is to rely on a partition of time-sequences of metocean parameters with respect to the meteorological events that are the sources of the phenomena. The objective is to provide a structure with physical meaning and temporal coherence for the data occurrence joint probabilities. Using this approach need the following steps: 1- Identification of coherent time-sequences of wave parameters (swell and wind sea events); 2- Parameterization of swell events and wind sea events; 3- Modeling the occurrence process scheme of the wave system events. 6

7 II- Swell events and wind sea events identification 1- Existing methods 2- A proposed method 7

8 II- Swell events and wind sea events identification 1- Existing methods : partitioning + wave systems parameters tracking Partitioning (H s1, T P1, θ p1 ) (H s3, T P3, θ p3 ) (H s2, T P2, θ 2 ) Sea state = f each time step. H s1, T P1, θ p1,, H s2, T P2, θ 2,, H s3, T P3, θ p3,, at 8

9 II- Swell events and wind sea events identification 1- Existing methods : partitioning + wave systems parameters tracking Wave systems parameters tracking Wave systems parameters tracking (SPOP) The tracking is based on some empiral criteria and it is difficult to find a good adjustments in oder to have coherent time-sequences of parameters that can be modeled consistenly. 9

10 II- Swell events and wind sea events identification 2- A proposed method 10

11 II- Swell events and wind sea events identification 1- A proposed method One sided spectrum Time-history of spectra The wave systems events can be observed on the figure and the objective is to implement an automatic method for their extraction. 11

12 II- Swell events and wind sea events identification 1- A proposed method Step 1: spectral estimation Smoothing time history of log-raw spectra both in frequency and time domain. One sided spectrum Time-history of spectra 12

13 II- Swell events and wind sea events identification 1- A proposed method Step 2: watershed algorithm A time history of spectra may be seen as a topographic map, where the blue level of a pixel is interpreted as its altitude in the landscape. A drop of water falling on a topographic relief flows along a path to finally reach a local minimum. 13

14 II- Swell events and wind sea events identification 1- A proposed method Step 3: partitions grouping 14

15 II- Swell events and wind sea events identification 1- A proposed method Time-history of identified parameters 15

16 II- Swell events and wind sea events identification Comparaison Partitionning + tracking approach A proposed method 16

17 Summary A good wave systems events identification can be made with our method. However, in the case where two systems from different directions are very close in frequency, the method still fails in their identification. Using some criteria, the identified events can be classified into swell events and wind sea events. The next stage of our work is the parameterization of a swell event and a wind sea event. 17

18 III- SWELL EVENT PARAMETERIZATION 18

19 III- SWELL EVENT PARAMETERIZATION Swell events selection Are selected as swell events: events in which peak frequency at each time step is greater than 0.13Hz (7,5 s), and in which peak frequency temporal evolution is almost linear with positif slope. Time-history of identified swell parametres 19

20 III- SWELL EVENT PARAMETERIZATION A proposed swell event model The model is made of wave parameters (hs, fp and θp) time evolution modelling. Set for each event the time origin t0 at the observation of maximum Hs Normalization is carried out by the maximum Hs of the event to bring all histories to 1 at their time origin. Plot of normalized events: (a) all retained events, (b) median 20

21 III- SWELL EVENT PARAMETERIZATION A proposed swell event model Six parameters are required: maximum Hs values, Hs growth slope, Hs decay slope, fp value at time maximum Hs, fp slope, direction An event and it model (M. Olagnon, K.A. Kpogo-Nuwoklo and Z. Guédé) 21

22 CONCLUSIONS A wave systems events identification method is proposed with satisfactory wave systems identification. However, in the case where two systems from different directions are very close in frequency, the method still failes in their identification. This problem can be resolved by making segmentation in 3D (time-history of directional spectra). We develop a model for swell events at west Africa locations. It provides parametric shapes for joint time-histories of significant wave height, dominant wave period and direction at the location of interest. The model can be improved in particular for the direction. It needs a wind sea event model. Perspectives The occurrence process scheme of wave system events is also needed. 22

23 Thank you! 23