Earth Observation in coastal zone MetOcean design criteria

Transcription

1 ESA Oil & Gas Workshop 2010 Earth Observation in coastal zone MetOcean design criteria Cees de Valk BMT ARGOSS

2 Wind, wave and current design criteria geophysical process uncertainty modelling assumptions approximation errors (weather, wave) sampling bias hindcast data (weather, wave) sampling variability tail distribution assumptions model unc. & estimator measurement errors sampling variability model uncertainty observations (weather, waves) wave database sampling variability metocean conditions over time interval climate uncertainty climate (ensemble) F Fˆ

3 Exploiting developments in measuring, predicting and reconstructing weather and ocean Global satellite measurement of weather and sea surface wind scatterometer, radar altimeter, SAR Computer modelling Numerical weather prediction Sea surface wave prediction GCM s (Global Circulation Models: Ocean + atmosphere), tidal modelling Data assimilation (merging measurement data into model predictions) to analyse (hindcast) the past To forecast the future Verification Using satellite data, prediction can be verified globally Simultaneous data from independent sources allow assessment of uncertainty

4 Global Tidal Elevation and Tidal Current Data Characteristics: Based on integration of tide gauge data from stations and over 20 years of satellite measurements with numerical modelling Global coverage, varying resolution Information at resolutions of up to 1 minute readily available near coast Even finer resolutions possible through dedicated modelling

5 High Resolution Weather Prediction Zooming in step by step

6 Nearshore and shallow-water wave hindcasting Delaware Bay nearshore wave transformation test: bathymetry and gridpoints of global hindcast model

7 Nearshore and shallow-water wave hindcasting Example: spectral wave ray tracing model, rays to boundary

8 Nearshore and shallow-water wave hindcasting Probability distribution of Hs per month: hindcast (left) and buoy (right)

9 Data quality control and data uncertainty assessment Precision measurement using coinciding data from 3 sources

10 Satellite wind and wave data in quality control and data uncertainty assessment

11 Data quality control and data uncertainty assessment Precision measurement using coinciding data from 3 sources

12 Wind, wave and current design criteria

13 Data quality control and data uncertainty assessment Data quality information made accessible from everywhere over the Web

14 Data quality control and data uncertainty assessment Data quality information made accessible from everywhere over the Web

15 Data quality control and data uncertainty assessment Data quality information made accessible from everywhere over the Web

16 Data quality control and data uncertainty assessment Relative root-mean-square error of altimeter minus original/calibrated model wave height. Data quality information made accessible from everywhere over the Web

17 Data quality control and data uncertainty assessment Data quality information made accessible from everywhere over the Web

18 Data quality control and data uncertainty assessment Bias of altimeter minus original /calibrated model wave height Data quality information made accessible from everywhere over the Web

19 Data quality control and data uncertainty assessment Standard deviation of altimeter minus original /calibrated model wave height Data quality information made accessible from everywhere over the Web

20 Data quality control and data uncertainty assessment The main limitation of altimeter data for wave hindcast calibration is data volume: the Hs range where storm hindcasts get biased is not sufficiently covered

21 SAR Imagery: coastal wind fields

22 SAR Imagery: coastal wind fields

23 SAR Imagery: coastal wind fields

24 SAR Imagery: internal waves

25 Imagery: coastal sea-surface waves Hi Res optical image of SW coast of Bali (Digital Globe)

26 Imagery: coastal sea-surface waves Crude wave phase picture from Bali image

27 Thank You