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(1) (1) ISEIS, Chinese University of Hong Kong, NT, Shatin, Hong KongISEIS, Chinese University of Hong Kong, NT, Shatin, Hong Kong(2) (2) DEI, Politecnico di Milano, Milan, ItalyDEI, Politecnico di Milano, Milan, Italy(3) (3) DIEI, Università la Sapienza, Rome, ItalyDIEI, Università la Sapienza, Rome, Italy(4) (4) CETEMPS, University of L’Aquila, ItalyCETEMPS, University of L’Aquila, Italy(5) (5) DIIAR, Politecnico di Milano, Milan, ItalyDIIAR, Politecnico di Milano, Milan, Italy(6) (6) ESA,ESA, ESTEC ESTEC Noordwijk, The NetherlandsNoordwijk, The Netherlands
Vancouver, 28Vancouver, 28thth July 2011 July 2011
Mitigation of atmospheric delay in InSAR: the ESA Mitigation of atmospheric delay in InSAR: the ESA METAWAVE projectMETAWAVE project
Daniele Perissin Daniele Perissin (1)(1),,Fabio RoccaFabio Rocca (( 22 )) , Mauro Pierdicca, Mauro Pierdicca (( 33 )) , Emanuela Pichelli, Emanuela Pichelli (( 44 )) , , Domenico CiminiDomenico Cimini (( 44 )) , Giovanna Venuti, Giovanna Venuti (( 55 )) , Bjorn Rommen, Bjorn Rommen (( 66 ))
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Mitigation of atmospheric delay in InSAR: Mitigation of atmospheric delay in InSAR: the ESA METAWAVE projectthe 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
Decomposition of atmospheric signal Decomposition of atmospheric signal (from InSAR point of view)(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
Connection between APS and IWVConnection 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
Experiments and performancesExperiments and performances
Table of Contents
NWP data in Rome vs APS
Meris data in Rome vs APS
GPS data in Como vs APS
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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
InSAR vs NWP NWP domain and topography
8
InSAR vs NWP “differential” comparisonIWV APS APS-IWV
Scatter plotAPS vs IWV
APS-IWV APS-IWVvs SRTM
9
InSAR vs NWP “differential” comparisonIWV std APS std APS-IWV std
IWV stratif. APS stratif. APS-IWV stratif.
10
Estimated Master APSAverage IWV
qk iii += εα ii kkk δ+= 0
InSAR vs NWP “pseudo-absolute” comparison
11
IWV APS APS-IWV
Scatter plotAPS vs IWV
APS-IWV APS-IWVvs SRTM
InSAR vs NWP “pseudo-absolute” comparison
12
Scatter plot disp: 0.7 mm/kmvs InSAR disp:
1.3 mm/km
InSAR vs NWP Variational stratification
MM5 can help reducing thestratification component
13
IWV APS APS-IWV
Scatter plotAPS vs IWV
APS-IWV APS-IWVvs SRTM
InSAR vs NWP Comparison of turbulent terms
14
19940131 19940203 19940212 19940215 19940227 19940305 19940308
APS 0.51 0.64 0.33 0.26 0.45 0.85 0.33APS-IWV 0.62 0.58 0.5 0.42 0.64 0.8 0.51
Spatial cross-correlation
Standard deviations [mm]
InSAR vs NWP Comparison of turbulent terms
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19940131 19940203 19940212 19940215 19940227 19940305 19940308
KS 0.12 0.24 0.26 0.23 0.11 0.27 0.1
Cumulative distribution functions
Test statistics
Kolmogorov-Smirnov test
InSAR vs NWP Comparison of turbulent terms
MM5 turbulent termhas very low correlation
with the APS one
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InSAR vs NWP IWV evolution in time
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Standard deviation vs delay
InSAR vs NWP NWP-APS synchronization
NO significant improvement
18The 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 componentin MM5 simulations!!
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 onlyfor day time passes
20
Examples in the Rome dataset
Rome T351, morning passes
40% of loss
InSAR vs MERIS
Meris needs clear sky conditions
21
InSAR vs MERIS Spectral analysis
Meris has spectral contentcloser to the APS one
22
Scatter plots and correlation
Meris IWV [mm]
InSAR IWV [mm]
InSAR vs MERIS
In our experiment theMeris success rate is quite low
23
InSAR vs GPS The Como test-site
480 descending, 10am (28 images) 487 ascending, 9pm (38 images)
5 GPS stations,5 overlapping days
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descending track
Different ways for estimating the GPS stationary term
InSAR vs GPS
ascending track
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0.322.051.831.670.891.271.152.18 0.892.796.075.360.951.063.362.94 0.593.033.350.630.951.985.003.61 0.793.525.262.730.284.274.272.34 0.562.851.723.430.533.433.373.69
corr coeff
diff std
InSAR std
GPS std
corr coeff
diff std
InSAR std
GPS std
Descending Ascending
Correlation and deviation reductionInSAR vs GPS
GPS has a 50% success rate
26
D =10004.6mm*sqrt(0.036)=4.6*0.19=0.9mm.
1000036.0
Dq =
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
Experiments and performancesExperiments and performances
Conclusions at this time
Meris: spectral content closer to APShowever usable only in clear skystratification not very robust
NWP: powerful tools in space and timestrong random componentuseful for long spatial wavelengths and stratification
GPS: highest accuracyreliability depends on density of ground stations
PS: where PS’s are present, no better way to estimate the APS