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The role of numerical weather models (NWM) in mitigation of tropospheric delay for SAR Interferometry July 27, 2011 Shizhuo Liu 1 , Agnes Mika 2 , Wenyu Gong 3 , Franz Meyer 3 , Ramon Hanssen 1 , Don Morton 3  and Peter Webley 3 1  Delft institute of Earth Observation and Space Systems (DEOS), the Netherlands 2  BMT AGROSS, the Netherlands 3  University of Alaska Fairbanks, United States Department of Earth Observation and Space Systems (DEOS), Aerospace engineering InSAR WRF
Delay observed by repeat-pass SAR Interferometry 07/27/11 ,[object Object],[object Object],p q t1 t2 observed delay: (spatio-temporal  difference)
Spatial characteristics of delay in InSAR 07/27/11 8 ERS1/2 tandem interferograms over Groningen, the Netherlands a b c d e f g h trend: c, e, h local anomaly: a, g trend+anomaly: b, d, f  Trend  +  Variation (water vapor) mm
Delay in mountainous regions 07/27/11 =  Trend   +  Variation (random) p q Atmospheric-only interferogram Hawaii topography h trend  +  variation +  vertical stratification m mm
Studies of regions with different climates 07/27/11 Netherlands Hawaii Mexico City Lake Moore, WA
Forecasting setup ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],07/27/11
Hawaii (case No.1) InSAR (35-day) 07/27/11 WRF Foster JGRL, vol. 33, 2006  mm InSAR - WRF InSAR WRF InSAR - WRF
Hawaii (case No.2) 07/27/11 InSAR (35-day) WRF InSAR - WRF InSAR WRF InSAR - WRF
Topography of Mexico City 07/27/11 m
Mexico City (case No.1) 07/27/11 InSAR (35-day) WRF InSAR-WRF InSAR WRF InSAR-WRF mm
Mexico City (case No.2) 07/27/11 InSAR (35-day) WRF InSAR - WRF InSAR WRF InSAR - WRF
Inconsistency (case No.3) 07/27/11 InSAR (35-day) WRF InSAR - WRF
Cross-validation with MERIS 07/27/11 WRF InSAR MERIS mm
Flat regions 07/27/11
Netherlands (9 cases) 07/27/11 InSAR (35-day) WRF InSAR-WRF No.1 mm No.2 No.3
Netherlands 07/27/11 InSAR (35-day) WRF InSAR-WRF No.4 No.5 No.6
Netherlands 07/27/11 InSAR (35-day) WRF InSAR-WRF No.7 No.8 No.9
Southwest Australia (5 cases) 07/27/11 InSAR (35-day) WRF InSAR-WRF No.1 No.2 No.3
Southwest Australia 07/27/11 InSAR (35-day) WRF InSAR-WRF No.4 No.5
Variograms of delay 07/27/11 Netherlands Australia Distance [km] InSAR WRF
Results review ,[object Object],[object Object],[object Object],07/27/11
Model tuning ,[object Object],[object Object],[object Object],07/27/11
ECMWF versus FNL 07/27/11 Mexico City (case No.3) same model settings
Netherlands (case No.2) 07/27/11 InSAR ECMWF(WRF) InSAR-ECMWF FNL(WRF) InSAR-FNL
Netherlands (case No.9) 07/27/11 InSAR ECMWF(WRF) InSAR-ECMWF FNL(WRF) InSAR-FNL
Longer spin-up time and more vertical levels 07/27/11 Hawaii Mexico City Netherlands Australia InSAR WRF tuned WRF original
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Conclusions 07/27/11
[object Object],07/27/11
Is the weather model generally bad for delay prediction ? 07/27/11 MERIS WRF Absolute delay:
Mean delay 07/27/11
Recommendations ,[object Object],[object Object],07/27/11
Tropospheric delay experienced by MW 07/27/11 hydrostatic (gas components) wet (water vapor) cloud droplets absolute delay due to troposphere: hydrostatic: long wavelength spatial gradient(pressure, temperature), i.e., trend wet/cloud: significant spatial variation, i.e., local variation
Numerical forecasting for delay mitigation 07/27/11 Earth’s surface NWM prediction dh z(h) (T, e, P) P:  total air pressure e :  water vapour pressure T:  air temperature x y C onstants  (Davis et al., 1985)  Refractivity is obtained by taking temporal and spatial difference in sequence
Hawaii (case No.3) 07/27/11 InSAR (35-day) WRF InSAR - WRF InSAR WRF InSAR - WRF
Hawaii (case No.4) 07/27/11 InSAR (35-day) WRF InSAR-WRF InSAR WRF InSAR-WRF

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The role of weather models in mitigation of tropspheric delay for SAR Interferometry.ppt

  • 1. The role of numerical weather models (NWM) in mitigation of tropospheric delay for SAR Interferometry July 27, 2011 Shizhuo Liu 1 , Agnes Mika 2 , Wenyu Gong 3 , Franz Meyer 3 , Ramon Hanssen 1 , Don Morton 3 and Peter Webley 3 1 Delft institute of Earth Observation and Space Systems (DEOS), the Netherlands 2 BMT AGROSS, the Netherlands 3 University of Alaska Fairbanks, United States Department of Earth Observation and Space Systems (DEOS), Aerospace engineering InSAR WRF
  • 2.
  • 3. Spatial characteristics of delay in InSAR 07/27/11 8 ERS1/2 tandem interferograms over Groningen, the Netherlands a b c d e f g h trend: c, e, h local anomaly: a, g trend+anomaly: b, d, f Trend + Variation (water vapor) mm
  • 4. Delay in mountainous regions 07/27/11 = Trend + Variation (random) p q Atmospheric-only interferogram Hawaii topography h trend + variation + vertical stratification m mm
  • 5. Studies of regions with different climates 07/27/11 Netherlands Hawaii Mexico City Lake Moore, WA
  • 6.
  • 7. Hawaii (case No.1) InSAR (35-day) 07/27/11 WRF Foster JGRL, vol. 33, 2006 mm InSAR - WRF InSAR WRF InSAR - WRF
  • 8. Hawaii (case No.2) 07/27/11 InSAR (35-day) WRF InSAR - WRF InSAR WRF InSAR - WRF
  • 9. Topography of Mexico City 07/27/11 m
  • 10. Mexico City (case No.1) 07/27/11 InSAR (35-day) WRF InSAR-WRF InSAR WRF InSAR-WRF mm
  • 11. Mexico City (case No.2) 07/27/11 InSAR (35-day) WRF InSAR - WRF InSAR WRF InSAR - WRF
  • 12. Inconsistency (case No.3) 07/27/11 InSAR (35-day) WRF InSAR - WRF
  • 13. Cross-validation with MERIS 07/27/11 WRF InSAR MERIS mm
  • 15. Netherlands (9 cases) 07/27/11 InSAR (35-day) WRF InSAR-WRF No.1 mm No.2 No.3
  • 16. Netherlands 07/27/11 InSAR (35-day) WRF InSAR-WRF No.4 No.5 No.6
  • 17. Netherlands 07/27/11 InSAR (35-day) WRF InSAR-WRF No.7 No.8 No.9
  • 18. Southwest Australia (5 cases) 07/27/11 InSAR (35-day) WRF InSAR-WRF No.1 No.2 No.3
  • 19. Southwest Australia 07/27/11 InSAR (35-day) WRF InSAR-WRF No.4 No.5
  • 20. Variograms of delay 07/27/11 Netherlands Australia Distance [km] InSAR WRF
  • 21.
  • 22.
  • 23. ECMWF versus FNL 07/27/11 Mexico City (case No.3) same model settings
  • 24. Netherlands (case No.2) 07/27/11 InSAR ECMWF(WRF) InSAR-ECMWF FNL(WRF) InSAR-FNL
  • 25. Netherlands (case No.9) 07/27/11 InSAR ECMWF(WRF) InSAR-ECMWF FNL(WRF) InSAR-FNL
  • 26. Longer spin-up time and more vertical levels 07/27/11 Hawaii Mexico City Netherlands Australia InSAR WRF tuned WRF original
  • 27.
  • 28.
  • 29. Is the weather model generally bad for delay prediction ? 07/27/11 MERIS WRF Absolute delay:
  • 31.
  • 32. Tropospheric delay experienced by MW 07/27/11 hydrostatic (gas components) wet (water vapor) cloud droplets absolute delay due to troposphere: hydrostatic: long wavelength spatial gradient(pressure, temperature), i.e., trend wet/cloud: significant spatial variation, i.e., local variation
  • 33. Numerical forecasting for delay mitigation 07/27/11 Earth’s surface NWM prediction dh z(h) (T, e, P) P: total air pressure e : water vapour pressure T: air temperature x y C onstants (Davis et al., 1985) Refractivity is obtained by taking temporal and spatial difference in sequence
  • 34. Hawaii (case No.3) 07/27/11 InSAR (35-day) WRF InSAR - WRF InSAR WRF InSAR - WRF
  • 35. Hawaii (case No.4) 07/27/11 InSAR (35-day) WRF InSAR-WRF InSAR WRF InSAR-WRF

Editor's Notes

  1. FNL: final analysis (google) Microphyiscs: Morrison 2-moment is a good scheme for high-resolution forecasting (WRF manual)