2. REQUIREMENTS:
• High quality data (mean / extremes)
• Long time series
• High temporal resolution
• Good spatial distribution (global / regional / local)
• Local characterization (from offshore to nearshore)
Engineering applications Management
Design, maintenance, problems
Risk assessment
Climate analysis coastal impact
Variability, forecast, projections
3. 1. Wind data SeaWind (SW)
DATABASES 2. Offshore wave data Global Ocean Waves (GOW)
3. Nearshore wave data Downscaling of Ocean Waves (DOW)
4. Surge data (sea level) Global Ocean Surge (GOS)
Research Interests of the Climate IH‐team:
• Dataset required for estimation of ocean climate
• Calibration / validation techniques and methods
• Downscaling (from global to regional to local)
• Climate variability (at different temporal/spatial scales)
• Risk analysis (impact due to climate change)
8. SW
SeaWind reanalysis
Spatial domain: Regional
With the best physics
configuration, we test the
performance of wind simulations
on a small domain at higher
resolution (<2 km).
16. GOW
Global Ocean Waves reanalysis
Global
Global Ice
Winds
Generation of
Waves
Preliminary
Validation
Buoy data Calibration Satellite data
Validation of
results
Apps
17. GOW
Global Ocean Waves reanalysis
Spatial domain:
Global
[Reguero et al. 2011 a,b]
18. GOW
Global Ocean Waves reanalysis
Dataset Validation
Validation of numerical modeling with dataset from satellite and buoy measurements
[Reguero et al. 2011 a]
22. GOW
Global Ocean Waves reanalysis
Dataset Validation
GOW‐SW‐NCEP
GOW‐SW‐EraInt
23. GOW
Global Ocean Waves reanalysis
Identification of outliers
(hurricanes and tropical
cyclones)
[Minguez et al. 2011a]
Calibration
(correction with altimeter data)
[Minguez et al. 2011b]
biR i
H C
s i a i i H
R
R
s i
24. GOW
Global Ocean Waves reanalysis
Spatial domain: Local
Temporal series of different wave climate parameters
25. GOW
Global Ocean Waves reanalysis
GOW outputs:
27. DOW
Downscaled Ocean Waves
•Inputs: energy spectra (offshore) Historical
•Downscaling with hybrid method data bases
•Reconstruction & validation nearshore
Selection
K-means
Propagation
SWAN
Reconstruction
RBF
Validation
Wave climate
28. DOW
Downscaled Ocean Waves
GOW – Spain.
•Inputs: energy spectra (offshore)
•Downscaling with hybrid method
•Reconstruction & validation nearshore
Historical
SWAN data bases
Selection
K-means
Propagation
SWAN
[Camus et al., 2011]
Reconstruction
N-Dim. Interp.
Validation
Wave climate
29. DOW
Downscaled Ocean Waves
Nearshore processes:
•Refraction
•Shoaling
•Wave breaking
•Diffraction
Spatial scale of
interest=100 m
30. DOW
Downscaled Ocean Waves
X i H i ,Ti , i ,Wi , i ; i 1,..., N
Historical
data bases
Selection
Propagation
Reconstruction
Validation
Wave climate
31. DOW
Downscaled Ocean Waves
Historical
data bases
Selection
Propagation
Reconstruction
Validation
Local wave propagations: SWAN Wave climate
32. DOW
Downscaled Ocean Waves
Historical
data bases
Selection
Propagation
Reconstruction
Radial Basis Functions
M
RBF X i p X i a j X i D j Validation
j 1
p X i b0 b1 H i b2Ti b3i b4Wi b5i
Wave climate
2
Xi Dj Xi Dj
exp
2c 2
36. DOW
Downscaled Ocean Waves
Spatial domain:
local
37. DOW
Downscaled Ocean Waves
Name Forcing Meshes Spatial Resolution
DOW-NCEP NCEP-NCAR Atlantic coast of Spain from ~500m to ~100m
DOW-NCEP NCEP-NCAR Canarias Islands from ~275m to ~45m
DOW-NCEP NCEP-NCAR Brasil Coast ~1Km
DOW-SWEraInt SW-EraInt Mediterranean coast of Spain from ~280m to ~55m
DOW-SWNCEP SW-NCEP Mediterranean coast of Spain from ~280m to ~55m
DOW-SWNCEP SW-NCEP Atlantic cost of Spain from ~500m to ~90m
DOW-SWNCEP SW-NCEP Canarias Islands from ~275m to ~45m
39. GOS
Global Ocean Surges
Spatial domain: regional
GOS domain
Grid Grid
Initial Final Initial Final points points
∆x (º)
Lon Lon. Lat. Lat. number number
(X) (Y)
-20 W 37 E 25 N 46.034 N 421 487 1/8º
40. GOS
Global Ocean Surges
Dataset Validation
Santander
Málaga
