Satellite altimetry: A revolution in understanding the wave climate

1. Satellite altimetry: A revolution in understanding the wave climate Peter Challenor, David Woolf, Christine Gommenginger, Meric SrokoszNational Oceanography Centre, SouthamptonDavid Cotton, David CarterSatellite Observing Systems Ltd

2. Summary • The early years • Calibration • Wave climate • Extremes • Beyond significant wave height • The future

3. If we didn’t have altimeters!

4. 30 years of altimeter waves • Starts with GEOS-3 • Then Seasat • For the first time we could see the world

5. Calibration • Altimeter wave heights are very accurate. • They are at least as good as wave buoys But the measurements differ from altimeter to altimeter. Use buoy networks to ensure consistency

7. Calibrations are not always stable

8. The wave climate • The biggest signal in the wave climate is of course seasonal.

9. Monthly mean wave height (90’s)

10. Inter-annual variability (90’s)

11. EOF 80’s-90’s

12. A statistical model for wave height • Build a model to predict wave climate from the NAO Index • For each 2° square regress monthly mean Hs on the NAO • (An alternative would be to regress on the EOF’s)

13. Validation at OWS Lima

14. Decadal Changes due to NAO (circa 1967 - circa 1991)

15. A Second Pair of Coupled Patterns NAO EA

16. Climate change 80’s and 90’s

18. Extremes • Many people are not interested in mean wave conditions but in the extremes • Usually these are expressed in terms of 50 or 100 year return values

19. Peaks over Thresholds (POT) The upper tails of statistical distributions tend to a limiting distribution - the Generalised Pareto Distribution (GPD) • Set a high threshold • Fit a GPD to the exceedences about that threshold Undersampling will lead to an underestimate of the extremes by about 10-15% (Robinson and Tawn)

20. An Example Fit

21. 50-year Return Value

22. If we add seasonal dependence and an NAO.seasonal interaction term we get a better fit These plots show a low (left) and high (right) NAO January H50 relative to average condtions

23. Beyond Significant Wave Height • S and C band • Wave Period • Skewness • Individual echoes • Phase

24. S Band / Ku Band Hs vs Buoy Data Variance of the S-band SWH is about 25 times that of the Ku-band. Factor of 20 times is accounted for by the reduced number of pulses averaged to get the S-band 1-Hz values.

25. S-band Hs much ‘noisier’, s0 shows better continuity+++ means rain flag is raised

26. Hurricane Katrina o Ku-band x S-Band

27. Gommenginger et al (2003) Tz v NDBC for Envisat

28. Wave Period and the NAO • In a similar way to wave height wave period relates to the NAO The above shows the first EOF of period. On the left we have a regression of Tm on the NAO

30. Location of ENVISAT RA2 Individual echo bursts Courtesy of J.Garlick, DeMontfortUniversity

31. Is it possible to estimate the wave spectrum from phase information?

32. Wave models • Wave models give an alternative source of information on the wave climate • Assimilation • Validation • Complementary

33. Conclusions • Radar altimeters are now the main way to gain information on the wave climate • Careful calibration is however still needed • Wave climate varies both seasonally and from year to year. • It is possible to obtain extremes from altimeter data • There are exciting prospects for the future