1 / 15

A multi platform OI scheme for SST based on spatial-temporal covariance models

A multi platform OI scheme for SST based on spatial-temporal covariance models. J. L. Høyer & J. She Danish Meteorological Institute. The work is carried out under the ODON project, funded by EU. Objective Validate medium to high resolution satellite SST products

nansen
Download Presentation

A multi platform OI scheme for SST based on spatial-temporal covariance models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A multi platform OI scheme for SST based on spatial-temporal covariance models J. L. Høyer & J. She Danish Meteorological Institute The work is carried out under the ODON project, funded by EU

  2. Objective • Validate medium to high resolution satellite SST products • Generate and validate an OI SST field to use for: NWP (HIRLAM), In combination with a simplified Kalman filter and for operational daily/weekly high resolution SSTs. • Use correlations for rational sampling design of observational networks Outline • Satellite and in situ SST observations and coverage • Satellite error statistics • Optimal Interpolation • Covariance models • Validation of OI products • Error covariances and elliptical covariances • Conclusions

  3. Satellite data Satellite products: • O&SI SAF (NOAA 14+16), resolution 2 km • BSH SST (NOAA 12) • Baltic Sea, resolution 1.2 km • North Sea, resolution 1.5 km Only nighttime observations. Day-night difference up to 1.5 deg in the mean and annual variation

  4. Satellite coverage Averaged in 20 km and 3 days Nov 1- June 1 June 1-Nov 1

  5. In situ SST observations Averaged in 20 km and 3 days • Data from GTS, Bouys, Ferrybox, Research cruises, etc. • ~200000 SST observations for 2001 • Several quality control procedures Green: Bouys Blue: Surface Red: Profiles

  6. Satellite error statistics • O&SI SAF have better accuracy than BSH product • Adjusted BSH observations to SAF SSTs • Individual satellite bias used in skin-bulk SST correction Mean difference Accuracy of corrected SST Standard deviations (Høyer & She, 2004)

  7. Filling the gaps: Optimal Interpolation • Uses steady state statistics • Minimizes error, provides error estimate for all predictions • x, y and t covariances calculated from satellite observations • Spatially varying covariance models are fitted to data • Same covariance model applied for satellite and in situ observations • Multiplatform, individual noise on observations • OI is performed on anomalies (annual and semiannual variations subtracted) • 10 and 2 km OI products have been produced

  8. Covariance models Model determined from mean correlations In x and y r(x) = exp(ax0.5) In time r(t) = exp(at0.85)

  9. Spatial variation of parameters • Regional differences in the fitted x, y and t correlation model parameters, a x-direction y-direction Temporal

  10. OI snapshot from Aug 29, 2001 • 10 by 10 km, grid every 12 hours in 2001 • Used 8 or 4 observations in prediction • Search radius 3 days

  11. OI validation 1) Mean theoretical OI error Several OI products have been tested, some results: • Including daytime satellite SSTs gave poorer the results and introduced annual variation • Better to use only SAF SST than including BSH SSTs • Using only every 4th satellite observation gave accuracies of 0.84 (compared to 0.8)

  12. OI validation 2) Predictions compared against independent satellite and in situ observations: Satellite validation RMS for hourly 2 km OI SST product for May: 1.05 for 6742 obs In situ validation

  13. Satellite and in situ correlations Results from the grid points where both satellite and in situ observations are available Differences between curves indicate if in situ or satellite noise is correlated: -Not in time -Maybe spatial satellite error covariance

  14. Elliptical covariances -Spatial correlations -+50 km in x and y, averaged for 100x100 km bins -Contour intervals 0.1 Spatial correlations 0 +50 km -50 km Lag in x

  15. Conclusions • SAF and BSH satellite products have been validated with in situ observations • A skin—bulk correction has been applied to the satellite SSTs. The accuracy is 0.6oC. • Significant spatial structure of the correlations • Optimal interpolation in time and space has been used to fill the gaps where no in situ or satellite observation exist • The accuracy of the 12 hourly 6nm OI product is max 0.8oC • Satellite error covariances may be important in space, not in time. • Elliptical covariances may be important in some areas in the region Future work: • More investigations on satellite and in situ error covariances • Incorporate more operational satellite products (AATSR if available) and in situ observations • Operationalize OI scheme to produce daily high resolution (2-5 km) SST maps

More Related