Transcript MSU Talk, 03/23/07: Microwave Radar for glaciology, snow

Evaluation of radar measurements
Hans-Peter Marshall, Boise State University and CRREL
Snow Characterization Workshop, April 13-15, 2009
1
Locate instrumentation-related signals…
And get rid of them!
Locate causes of major reflections
• Metal reflectors placed at known depths, to determine
cause of reflections in original signal
Metal reflector experiment
Metal reflector experiment
Metal reflector experiment
Metal reflector experiment
Accuracy of using mean dielectric
properties to estimate velocity: < 2%
Comparing FMCW signal to in-situ electrical
measurements
• radar => in-situ dielectric
properties (Finish snowfork)
[e.g. Harper and Bradford, 03]
• In-situ properties => physical
properties
(e.g. Sihvola et al, 1985;
Schneebeli et al, 1998;
Matzler, 1996)
In-situ Density and Wetness
In-situ Reflectivity
Rinsitu 
 2  1
 2  1
Radar Snow Water Equivalent Estimates
rmsSWE (TWT ,  )  2%
rmsSWE (TWT ,d)  9%
rmsSWE (TWT ,   250kg / m 3 )  10%
TWT  ( zs  z g )  / c
  1  2
SWE 
zs
  ( z )dz
zg
Comparison of radar with SMP at Swiss Federal
Institute for Snow and Avalanche Research
SnowMicroPenetrometer
=> Small diameter rod
driven through snow at
constant velocity, pressure
measured at tip
250 measurements/mm
Measures rupture force
of grain bonds
Snowpit comparison, SLF, Feb 19, 2004
Multi-Layer Model
 E(r, t )    E(r, t )
2
2
 2
2
2 



  Ey  0
 2
2

x

z


Ri ( ) 
ri 
EI (i ) ( w, t )
 i   ( i 1)
 i   ( i 1)
Ri ( ) 
(e.g. Ulaby et al, 1981)
ER (i ) ( w, t )
ri  R( i 1) ( )e
2 j  i di
1  ri R( i 1) ( )e
2 j  i di
3-layer model – complicated for thin layers
Depths of major reflections automatically picked
Comparison of FMCW radar and SnowMicroPen
Chuckchi Sea, Barrow
March, 2006
• 300 meter profile
on 1st year sea ice
• 601 MagnaProbe
measurements
• >3000 FMCW
radar snow
depths
Static comparison
1) Expected error =
velocity uncertainty (1.5
cm) + radar resolution
(1.5 cm) + difference in
horizontal support
(2cm) = 5cm
2) Mean values within
1.5 cm
Density/Velocity distribution from
SWE cores
+/- 5% uncertainty in depth estimate due to density variability
FMCW radar profile
Mean measured density used to estimate depth from radar TWT
FMCW radar / Magnaprobe comparison
1) Similar variability, good agreement
2) Differences mainly due to different support
and coregistering of measurements
Comparing point depths to radar measurements
1.7 km profile, Dx=10 cm, Dz=1.5 cm
1.7 km profile, Dx=10 cm, Dz=1.5 cm
1.7 km profile, Dx=10 cm, Dz=1.5 cm
Conclusions - limitations
• Signal attenuated in very wet snow
• Magnitude information from reflections
difficult to interpret for thin layers
• No mechanical / microstructural
information
Conclusions - advantages
• Rapid (50 Hz) estimates of snow depth, SWE,
major stratigraphic boundaries
• Basin-scale areas can be covered
• Slab geometry can be measured
• Simulate active microwave remote sensors