1. 1 (1) ISEIS, Chinese University of Hong Kong, NT, Shatin, Hong Kong (2) DEI, Politecnico di Milano, Milan, Italy (3) DIEI, Università la Sapienza, Rome, Italy (4) CETEMPS, University of L’Aquila, Italy (5) DIIAR, Politecnico di Milano, Milan, Italy (6) ESA, ESTEC Noordwijk, The Netherlands Vancouver, 28 th July 2011 Mitigation of atmospheric delay in InSAR: the ESA METAWAVE project Daniele Perissin (1), Fabio Rocca （ 2 ）, Mauro Pierdicca （ 3 ）, Emanuela Pichelli （ 4 ）, Domenico Cimini （ 4 ）, Giovanna Venuti （ 5 ）, Bjorn Rommen （ 6 ）

2. 2 Mitigation of atmospheric delay in InSAR: the ESA METAWAVE project Table of Contents 1. decomposition of atmospheric signal 2. connection between APS and IWV 3. experiments and performances (GPS, Meris, NWP) 4. PS precision assessment 5. Conclusions

3. 3 Decomposition of atmospheric signal (from InSAR point of view) Atmospheric components… - stratification (correlated with topography) - turbulence - spatially linear component - stationary part - variational part …which can be divided into Points to keep in mind - The APS contains only the variational part of the atmosphere - The APS gathers spatially correlated noise components (also orbital artifacts)  spatially linear trends must be removed!

4. 4 Connection between APS and IWV - in APS domain: differential way (multi-master) 2 different strategies for comparison/correction of APS - in IWV domain: stationary term + spatial linear terms must be provided by external data To be able to extract Water Vapor from the APS - in APS domain: pseudo-absolute way (single Master)  the Master delay is estimated and removed, so the atmospheric delay can be compared day by day

5. 5 Experiments and performances Table of Contents NWP data in Rome vs APS Meris data in Rome vs APS GPS data in Como vs APS

6. 6 InSAR vs NWP T351, desce, 10UTC 30 images 10 std IWV maps T172, asce, 21UTC 41 images 20 std IWV maps Rome Envisat datasets

7. 7 InSAR vs NWP NWP domain and topography

8. 8 InSAR vs NWP “differential” comparison IWVAPSAPS-IWV Scatter plot APS vs IWV APS-IWV vs SRTM

9. 9 InSAR vs NWP “differential” comparison IWV stdAPS stdAPS-IWV std IWV stratif.APS stratif.APS-IWV stratif.

10. 10 Estimated Master APS Average IWV InSAR vs NWP “pseudo-absolute” comparison

11. 11 IWVAPSAPS-IWV Scatter plot APS vs IWV APS-IWV vs SRTM InSAR vs NWP “pseudo-absolute” comparison

12. 12 Scatter plot disp: 0.7 mm/km vs InSAR disp: 1.3 mm/km InSAR vs NWP Variational stratification MM5 can help reducing the stratification component

13. 13 IWVAPSAPS-IWV Scatter plot APS vs IWV APS-IWV vs SRTM InSAR vs NWP Comparison of turbulent terms

14. 14 Spatial cross-correlation Standard deviations [mm] InSAR vs NWP Comparison of turbulent terms

15. 15 Cumulative distribution functions Test statistics Kolmogorov-Smirnov test InSAR vs NWP Comparison of turbulent terms MM5 turbulent term has very low correlation with the APS one

16. 16 InSAR vs NWP IWV evolution in time

17. 17 Standard deviation vs delay InSAR vs NWP NWP-APS synchronization NO significant improvement

18. 18 The average map has been subtracted 00 06 11 InSAR vs NWP Impact of starting time 06 11 3 October 08, residuals after subtraction of stationary term Strong random component in MM5 simulations!!

19. 19 InSAR vs MERIS T351, desce, 10UTC 30 images 26 Meris image T172, asce, 21UTC 41 images The Rome dataset No use for night passes Meris can be used only for day time passes

20. 20 Examples in the Rome dataset Rome T351, morning passes 40% of loss InSAR vs MERIS Meris needs clear sky conditions

21. 21 InSAR vs MERIS Spectral analysis Meris has spectral content closer to the APS one

22. 22 Scatter plots and correlation Meris IWV [mm] InSAR IWV [mm] InSAR vs MERIS In our experiment the Meris success rate is quite low

23. 23 InSAR vs GPS The Como test-site 480 descending, 10am (28 images) 487 ascending, 9pm (38 images) 5 GPS stations, 5 overlapping days

24. 24 descending track Different ways for estimating the GPS stationary term InSAR vs GPS ascending track

25. 25 AscendingDescending GPS std InSAR std diff std corr coeff GPS std InSAR std diff std corr coeff 3.693.373.430.533.431.722.850.56 2.344.27 0.282.735.263.520.79 3.615.001.980.950.633.353.030.59 2.943.361.060.955.366.072.790.89 2.181.151.270.891.671.832.050.32 Correlation and deviation reduction InSAR vs GPS GPS has a 50% success rate

26. 26 D =1000 4.6mm*sqrt(0.036)=4.6*0.19=0.9mm. InSAR alone 1mm path delay error if we interpolate PS’s distributed along a circle with 10km diameter APS interpolation in presence of PS’s By F. Rocca

27. 27 Experiments and performances Conclusions at this time Meris: spectral content closer to APS however usable only in clear sky stratification not very robust NWP:powerful tools in space and time strong random component useful for long spatial wavelengths and stratification GPS:highest accuracy reliability depends on density of ground stations PS:where PS’s are present, no better way to estimate the APS