1. Operational Wave Model Validation SWAN

2. Objective
Skill assesment of Operational Wave Model (CSM - North Sea)
Method
A hindcast, for year 2009
Comparison of wave parameters betweeen Operational Model result and observed (wave bouy) wave parameters
In addition to bulk-wave parameter, wind sea and swell wave properties will also be examined (spectral partitioning algorithm is used)
Model : SWAN
Observed data : Wave spectrum data from RWS (thanks to Ivo)

3. SWAN model set up Observed data The data consist of : Forcing :
Komen et al
The data consist of :
Energy spectrum
Wave direction and spreading
Forcing :
Hirlam Wind (per 3 hour)
Output of WW3 Global as swell boundary
Three buoys are used for validation
Eierlandse Gat
Europlatform
K13 platform

4. Building ‘2D wave spectrum’ from RWS field data
(Quasi) 2D wave spectra can be reconstructed from field spectral data from RWS, using “spreading function”.
Parameter given by the field data :
Energy as function of frequency  E(f)
Mean Wave Direction as function of frequency  θ0
Wave spreading as function of frequency (for NDF  standard deviation)
(Quasi) 2D wave spectrum can be constructed using following expression :
Since :
Therefore :
Normal distribution spreading function :
A routine is built to construct ‘quasi’ 2D spectra out of RWS field data
(based on Alfons Smalle’s routine)

5. Spectral Partitioning Literatures
Apply Spectral Partitioning
Spectral Partitioning Literatures
Tracy, B. A., E.-M. Devaliere, et al. (2007)
Hanson, J. L., B. A. Tracy, et al. (2009)
Portilla et al 2008
Hanson and Phillips 2001
etc
An algorithm is built to differentiate between wind sea and swell – wave separation
Directional wave spectrum is partitioned using so called “watershed algorithm” (Matlab has ‘watershad.m’ function in Image Processing Toolbox)
Partitions then identified (as wind sea) using Wave Age criteria (Komen et.al 1984)
The rest of partitions belongs to Swell
Wind Seas are calssified based on Wave Age criteria :
Angle between Wind and Wind Sea direction
Wind speed

6. An example of wave separation result
Station : K13APFM
Period : 20 June – 30 June

7. Hindcast for year 2009 SWAN model is run for 1 year period (yr 2009)
SWAN output (2D wave spectrum) are stored
Spectral partitioning is applied for both model output and observed spectra
Wave parameters (wave height, peak period, and mean direction) are produced for both wind sea and swell
Comparison of these parameters for every month
Model skill is represented by statistical measures (Bias, RMS error)

8. 2009 hindcast Result Monthly Wind Sea and Swell heigth error measures
RMS error
Normalized RMS error

9. 2009 hindcast Result Monthly Wind Sea and Swell heigth error measures
Bias
For wave heigth and wave direction, model performs reasonably good
For Peak period, buoy Eierlandse Gat shows distinct different tendency, especially for month of May to August.
Slight tendency of underestimation for June, while overestimation for April

10. 2009 hindcast Result Problem with field data
RMS error
Energy spectrum data fro buoy Eierlandse Gat, gives unreasonbly high number (not 999 nor other easily recognized numbers)

11. K13APFM April

12. K13APFM April, 02-13
Wind waves are slightly overestimated for waves greater than 2 m (arround 9th of April), which corresponds to slight overestimation in the wind speed during this time
Swells are predicted with good skill

13. K13APFM June

14. K13APFM June, 01-15
Computed wind waves are in good agreement with observation, with slight tendency of underestimation
Swells are predicted well with tendency of underestimation
Swells and wind waves existance are almost equally dominant

15. K13APFM November

16. K13APFM November, 02-14
Computed wind waves are in good agreement with observation
Swells heigth are predicted well with tendency of slight underestimation
Wind sea energy is dominant

17. Wave separation algorithm has been created (developing..)
Summary
Wave separation algorithm has been created (developing..)
An extensive wave model validation has been carried out for Operational CSM model
Model performs very good reproducing Wind Sea
Model performs very good reproducing swell
The forcing, HiRLAM wind fields are in good agreement with observations
Consistent tendency of slight underestimation for swell
Next ...
To add more materials/analysis to the curent result, so it can be decent enough for a publication
More details on the wave separation algorithm
More details on the analysis of the validation