MODELING ATMOSPHERIC SIGNALS IN SPACEBORNE INTERFEROMETRIC SAR DATA

Transcription

1 MODELING ATMOSPHERIC SIGNALS IN SPACEBORNE INTERFEROMETRIC SAR DATA F.J Meyer 1) 2), W. Gong 1), P. Webley 1) 3), D. Morton 4) 1)Earth & Planetary Remote Sensing, University of Alaska Fairbanks 2)Alaska Satellite Facility (ASF) 3)Alaska Volcano Observatory (AVO), University of Alaska Fairbanks 4)Arctic Region Supercomputer Center (ARSC), University of Alaska Fairbanks Collaborating Organizations:

2 Mitigation Strategies for Atmospheric Signals Calibration Methods using Ancillary Data: Integration of sparse GPS Points Combination with spaceborne multispectral scanners Adjustment Methods based on spatio-temporal stochastic property of APS: Conventional filtering methods applied in InSAR time series analysis Integration of Numerical Weather Prediction (NWP) models (e.g. PSU/NCAR Mesoscale Model (MM5); Weather Research & Forecasting Model (WRF)) 2 2

3 Study Regions with Diverse Climatic and Topographic Properties Netherlands East Mediterranean Okmok Volcano Unimak Island Hawaii Fringe 11, 12/15/2011 Frascati 3 F. Meyer et al. 3

4 WRF Model Settings Parameters Initial boundary condition Land Topography model Netherlands / Hawaii Unimak Okmok East Mediterranean ECMWF * ECMWF * ECMWF * ECMWF* SRTM USGS DEM USGS DEM/SRTM USGS DEM Land Use and Land MODIS MODIS MODIS MODIS Cover Data Vertical layers Lateral resolution[km] 27, 9, 3,1 27, 9, 3,1 18, 6,2,0.7 27, 9, 3,1 Spin-up time [hr] 24~30 15 ~ ~ 15 ECMWF, European Centre for Medium-Range Weather Forecasts 4

5 First Question to be Answered: What is the best Approach of WRF-based Atmospheric Phase (APS) Correction? A Deterministic Approach? 5 5

6 APS-Correction: Hawaii Example I InSAR (35-day) WRF InSAR - WRF Foster JGRL, vol. 33, 2006 mm 6

7 APS-Correction: Hawaii Example II InSAR (35-day) WRF InSAR - WRF 7

8 Netherland Case Study

9 Second Question to be Answered: Does Increased Spatial Resolution Improve WRF s Forecasting Quality and Reliability? 9 9

10 Atmospheric Delay on Mount Okmok, AK InSAR one way delay [mm] WRF Resolution Setting: 2km [mm] WRF Resolution Setting: 0.7 km [mm] Correlation = 0.2 Correlation =

11 Atmospheric Delay: East Mediterranean Case WRF run resolution setting: 3km [cm] WRF run resolution setting: 1km 11 11

12 East Mediterranean Case 00:30 UTC to 17:30 UTC, one every hour INSAR APS WRF run, 1km resolution -20 [mm] [mm] 10 No improvement for small scale turbulence-related signals -10 [mm]

13 Third Question to be Answered: Can WRF Predict Stochastic Properties of APS? 13 13

14 Predicting Atmospheric Variance with WRF Netherland Case WRF Predicated Atmospheric Variance[mm 2 ] InSAR Predicated Atmospheric Variance [mm 2 ] 14 14

15 Predicting Atmospheric Variance with WRF East Mediterranean Case WRF Predicated Atmospheric Variance[mm 2 ] InSAR Predicated Atmospheric Variance [mm 2 ] 15 15

16 Predicting Atmospheric Variance with WRF Unimak Island Case WRF Predicated Atmospheric Variance[mm 2 ] InSAR Predicated Atmospheric Variance [mm 2 ] 16 16

17 Outlier Analysis: Okmok Island Case INSAR APS WRF APS INSAR APS WRF APS Solution: increasing the window length of temporal filter in time domain for this one

18 Linear Regression Parameters of Atmospheric Variance WRF_Delay=a*INSAR_Delay + b a b Netherland Cases East Mediterranean case Unimak Island case

19 Conclusion and Future work Conclusion Limited reliability of WRF for phase screen correction Resolution increase useful for stratified delay prediction yet no improvement for turbulent delay WRF seems useful for predicting statistical properties of APS. WRF has potential in assisting time-series data processing, e.g. filter parameter setting and quality control. Future Work Using WRF predicted stochastic properties for identifying affected images. Using WRF predicted variance for APS filter kernel design