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Sea state bias estimation for ENVISAT S. Labroue

1 - Data quality for cycles 10 and 11 2 - Estimation of ENVISAT Ku band SSB at crossovers 3 - Estimation of ENVISAT Ku band SSB with SSH-MSS measurements 4 - Comparison of ERS 2 and ENVISAT range. Sea state bias estimation for ENVISAT S. Labroue. 1 - Data quality for cycles 10 and 11.

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Sea state bias estimation for ENVISAT S. Labroue

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  1. 1 - Data quality for cycles 10 and 11 2 - Estimation of ENVISAT Ku band SSBat crossovers 3 - Estimation of ENVISAT Ku band SSBwith SSH-MSS measurements 4 - Comparison of ERS 2 and ENVISAT range Sea state bias estimation for ENVISATS. Labroue

  2. 1 - Data quality for cycles 10 and 11 Two different approaches are described : crossover dataset SSH measurements relative to the MSS CLS 2001 => cross calibration of the estimates obtained with the 2 methods The sea state bias is expressed as a function of SWH and wind speed (U) The atmospheric attenuation in Ku band is often set to the climatological value (bug in the processing) => wrong sigma 0 => wrong wind speed (Modified Chelton and Wentz algorithm)

  3. SWH, Cycle 10, ocean valid data

  4. Wind speed, Cycle 10, ocean valid data

  5. (Sigma0,U) relation of the Chelton Wentz algorithm

  6. (U,SWH) distribution, Cycle 10 and 11, ocean valid data

  7. 1 - Data quality for cycles 10 and 11 Geophysical corrections applied to the SSH measurements : ocean tide correction from the GDR product (GOT99 model) earth tide correction from the GDR product polar tide correction from the GDR product wet tropospheric correction : test between radiometer correction and ECMWF model, because the radiometer wet tropospheric correction provides too low corrections. The algorithm needs a bias of 1 dB on the 0 to work properly. ionospheric correction problem of dynamics for the Doris correction dual frequency is not corrected for the difference of sea state bias between Ku and S band => use of Bent model or GIM correction (routine provided by R. Scharoo)

  8. 2 - Crossover estimation Methodology difference of SSH at crossovers measurements where SSH’ = H - R - COR COR are the geophysical corrections except the SSB editing of SSH differences greater than 50 cm editing of SWH < 10 cm and U < 10 cm/s working with difference of SSH => crossovers with a 10 days window to reduce oceanic variability => use of larger smoothing window in the NP processing as we have only two cycles => an estimation of the SSB for each cycle (between 7000 and 10000 measurements) => mean of the estimates Analysis wet tropospheric correction ionospheric correction selection of crossovers with latitude

  9. SSB, mean of cycles 10 and 11Bent ionospheric correction, radiometer wet tropospheric correction

  10. The area of high SWH and high wind speed is correlated with dry atmosphere which present the larger differences between radiometer and ECMWF correction => up to 3 cm difference on the SSB SSB, mean of cycles 10 and 11Bent ionospheric correction, ECMWF wet tropospheric correction

  11. The Bent and GIM ionospheric correction give only small differences on the SSB estimation at crossovers (less than 1 cm of difference). Even if the Bent model does not retrieve all the dynamics of the ionosphere near the equator, most of the crossover data are located at high latitudes where the ionosphere is much smoother => Bent model works not too bad for these latitudes SSB, mean of cycles 10 and 11GIM ionospheric correction, ECMWF wet tropospheric correction

  12. Impact of a selection Abs(Lat) < 66°Difference of SSB(sel)-SSB corrected with Bent ionospheric correction The selection of data with latitude has an impact on the SSB estimation. The differences up to 2 cm are clearly correlated with SWH and U. High latitudes have been removed as models are known to be less accurate in these regions and cycles 10 and 11 present no ice in the north hemisphere. A lot of crossover measurements are of bad accuracy in these regions.

  13. Impact of a selection Abs(Lat) < 66°Difference of SSB(sel)-SSB corrected with GIM ionospheric correction The same comparison when correcting with GIM give a different structure with much more magnitude (up to 4 cm for high SWH). The differences are mainly correlated with SWH with a gradient decreasing with SWH. The larger difference comes fromthe accuracy of the GIM correction which has more weight when removing high latitudes points.

  14. SSB, individual estimates for cycles 10 and 11Bent ionospheric correction, ECMWF wet tropospheric correction SWH=2m, U=7m/s SSB = -12cm SWH=2m, U=7m/s SSB = -8cm

  15. 3 - Direct estimation Methodology use of direct SSH at 1Hz (Vandemark et al 2002) where SSH’ = H - R - MSS - COR COR are the geophysical corrections except the SSB MSS is the CLS 2001 (Hernandez et al.) editing of SWH < 10 cm and U < 10 cm/s Bent or GIM ionospheric correction ECMWF wet tropospheric correction working with direct SSH measurements => one estimation of the SSB using all the data set one year of data is needed to take into account seasonal variations but the two cycles 10 and 11 give a first estimate of the sea state bias

  16. Mean per bins of (U,SWH)Bent ionospheric correction, ECMWF wet tropospheric correction

  17. Mean per bins of (Sigma0,SWH)Bent ionospheric correction, ECMWF wet tropospheric correction

  18. Map of the measurements SWH<1.5m and 12dB< Sigma0< 15dB

  19. SSB estimateBent ionospheric correction, ECMWF wet tropospheric correction The estimate has been shifted of the value obtained at (U=0,SWH=0) => 1.176m NP estimate => we still have the discontinuity at SWH=1m Too much gradient for SWH<1m Unexpected peak in the SSB near SWH=7m

  20. SSB estimateGIM ionospheric correction, ECMWF wet tropospheric correction The estimate has been shifted of the value obtained at (U=0,SWH=0) => 1.211m More regular structure for SWH<2m but the discontinuity is still there! Same behaviour for SWH near 7m

  21. Difference of SSB direct estimateBent ionospheric correction- GIM ionospheric correction

  22. Difference of SSB estimate corrected with GIM ionospheric correctiondirect - crossover The difference between direct and crossover estimate presents a structure well correlated with SWH and U. It is close to the one obtained when comparing crossover estimates with a selection on latitude.

  23. Difference of SSB estimate corrected with GIM ionospheric correctiondirect - crossover with selection Abs(Lat)<66° There is still a 2cm gradient with (SWH,U) but it is less important than on the previous plot. The main differences are for high SWH/low winds which come from the different amount of data available in this part of the (U,SWH) distribution

  24. Crossover and direct estimates Crossover estimate with GIM correction and selection of Abs(Lat)<66° (U=7m/s,SWH=2m) SSB=-12cm Direct estimate with GIM correction (U=7m/s,SWH=2m) SSB=-10cm

  25. Parametric models BM3 model in GDR products (derived from ERS2) BM4 model fitted on ENVISAT, cycle 10

  26. Results on crossover standard deviation SSB estimates are applied to the same crossover data set (corrected with Bent, ECMWF corrections) with a selection of 50 cm on crossover differences All NP crossover estimates perform better than the BM3 model provided in the products NP crossover estimates give the same results with GIM or Bent ionospheric correction Variability between individual estimates and the mean of the estimates The selection with latitude for the crossover estimate provides an SSB which reduces more the standard deviation NP crossover estimate behaves as a BM4 model tuned on ENVISAT data. More cycles are needed to get a better NP estimate and measure its quality

  27. Results on crossover standard deviation NP direct estimate performs better with GIM ionospheric correction rather than Bent => ionospheric correction needs to be more accurate with direct measurements NP crossover SSB seems to give better results than the direct estimate

  28. Comparison with JASON 1 SSB JASON 1 crossover estimate (U=7m/s,SWH=2m) SSB=-13cm ENVISAT crossover estimate (U=7m/s,SWH=2m) SSB=-12cm

  29. 4 - Comparison of ENVISAT and ERS2 range

  30. The SSB estimates presented are preliminary results with only 2 cycles available: 70 days for the direct method using SSH-MSS 2 crossover data sets of 8000 measurements with a 10 days selection => not enough The quality of the wet tropospheric correction has an influence over the SSB estimation with up to 3cm difference when comparing to the SSB derived with model correction A well tuned ionospheric correction is needed and the GIM correction seems to be accurate enough to provide an independent ionospheric correction for the SSB estimation More work is needed to understand the influence of the data at high latitudes on the crossover SSB estimate. These results have to be confirmed with more cycles. All the results presented have been obtained with a 10 days selection for the crossovers. The analysis of the differences at 35 days has to be performed to measure the impact of the oceanic variability on the SSB estimation. Conclusions (1/2)

  31. The crossover data set has to be analysed more in depth to take into account the uncertainty of the Ku band range when computing the SSH differences. The two approaches for computing the SSB (crossovers and direct method) are in good agreement with differences between 1cm and 2cm. The area with low wind and high wave does not behave the same between the two data sets. The first SSB estimates for ENVISAT give a correction of the order of the one observed for ERS2 and JASON 1, roughly 5% of SWH. The S band has not been analysed yet since there are “S band anomalies” on cycle 10 and using only cycle 11 is not enough to derive a valid SSB estimation. Furthermore, the methodology has to be well analysed in Ku band before combining both bands. Conclusions (2/2)

  32. The SSB estimation needs to be performed on “good” data as it is impacted by the quality of all the geophysical corrections. => The radiometer wet tropospheric correction algorithm has to be modified to take into accounts the sigma 0 bias to work properly => Same evolution required on the algorithm of the atmospheric attenuation A new wind speed algorithm using sigma 0 and SWH as it is done for JASON 1 (neural network method tuned on collocated ENVISAT/QuickScat data) ? => To improve the wind speed histogram More data is needed to compute an accurate SSB estimate in Ku band and S band. Recommendations

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