Microwave, portable FMCW radar: a tool for measuring snow depth, stratigraphy, and snow water equivalent

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1 Microwave, portable FMCW radar: a tool for measuring snow depth, stratigraphy, and snow water equivalent Hans-Peter Marshall CGISS, Boise State University U.S. Army Cold Regions Research and Engineering Laboratory Snow Characterization Workshop, April 13-15,

NSF K-12 Engineering Outreach Fellowship NASA AMSR-Ice06 calibration/validation

2 Acknowledgements NASA Energy and Water Cycle Sponsored (NEWS) Research and Terrestrial Hydrology NASA Earth Systems Science Fellowship NSF Arctic Sciences NSF K-12 Engineering Outreach Fellowship NASA AMSR-Ice06 calibration/validation NASA Cold Lands Processes Experiment (CLPX) I/II ESA CryoSat calibration/validation 2

3 Outline Motivation - why microwave radar for snow? Brief FMCW radar overview Stationary FMCW measurements Spatial snow stratigraphy/depth measurements on alpine snow, polar firn, sea ice Snow depth measurements with highly portable system in 1st order basin Conclusions Future Work 3

4 Snow Water Equivalent (SWE) Sensitive climate change indicator Vital component of water cycle Effects global energy balance Estimates needed for water resource management, flood forecasting, prediction of available hydropower energy =? 4

5 But very difficult to estimate SWE at regional scales Can vary by more than 50% in 10 meters [Shook and Gray, 1996] Standard deviations of 65-80% of basin mean are typical [Marchand and Killingtveit, 2005] Manual measurements extremely time consuming (~1 hr for standard snowpit) 5

6 Less than 3 SNOTEL sites in most watersheds 6

7 Remote Sensing of Snow Daily to monthly repeat cycles Global coverage 7

over entire depth of snowpack Sensitivity to grain-size and stratigraphy Currently

8 But interpretation of remote sensing measurements is complex Large sub-footprint variability ground truth measurements difficult Narrowband measurements integrate over entire depth of snowpack Sensitivity to grain-size and stratigraphy Currently can map snowcover extent, but no operational SWE algorithms exist for active radar 8

Link manual point measurements Provide framework for

9 Motivation Ground truth for remote sensing Rapidly characterize spatial distribution and structure of snow Link manual point measurements Provide framework for accurate estimates of total SWE at basin scale and remote sensing footprints 9

annual accumulation link shallow cores/ neutron probe/snowpits

10 Glaciology High res. measurements in near-surface firn spatial variability of annual accumulation link shallow cores/ neutron probe/snowpits Remote sensing ground-truth (CryoSat) Scaled laboratory measurements to study meltwater pathways in glaciers 10

11 Snow avalanche layer thickness spatial variability of stratigraphy/storm snowfall geometry of a slab 11

12 Antarctic radar Spatial/temporal distribution of accumulation rate link to shallow (15m) and deep (800m+) cores ice depth, + GPS -> bed topography Disturbed layers infer past location of ice streams 12

GHz range Microwave radar studies in snow since 1980 s [e.g. Ellerbruch and Boyne, 1980; Gubler and Hiller, 1984; Fujino et al.

13 FMCW radar for snow studies Instead of 1-10 MHz for ice, GHz frequencies are optimal for near surface (1-3m) snow, for maximum vertical resolution (1 cm) Microwave (GHz) frequencies allow small, lightweight, directional antennas All active remote sensing measurements in GHz range Microwave radar studies in snow since 1980 s [e.g. Ellerbruch and Boyne, 1980; Gubler and Hiller, 1984; Fujino et al., 1985; Holmgren et al., 1998; Kanagaratnam et al., 2001; Gogineni et al, 2003; Yankielun et al., 2004] See [Marshall and Koh, CRST, 2008] for a review 13

14 Melt Pathways Albert, M.A., G. Koh and F. Perron, Radar investigations of melt pathways in a natural snowpack. Hydrological Processes (13):

15 Other Applications: Lake Ice Thickness 15

16 Other Applications: Landmine Detection 16

17 Other Applications: Landmine Detection 17

18 Linear frequency chirp transmitted (T) Received signal (R) also linear in frequency, offset by two-way travel time Frequency difference between T and R linearly related to the distance to target FMCW Theory 1) Multiply T*R 2) FFT -> find frequency differences df 3) df is proportional to travel time 18

19 High frequencies are more sensitive to subtle transitions: 19

20 High frequencies are more sensitive to subtle transitions: 20

21 High frequencies are more sensitive to subtle transitions: 21

22 But are rapidly attenuated if snowpack is wet 22

23 But are rapidly attenuated if snowpack is wet 23

24 But are rapidly attenuated if snowpack is wet 24

25 Resolution is limited by bandwidth c Δ z = 2B ε B = 1 GHz, Δz 12.5cm B = 4 GHz, Δz 3.1cm B = 8 GHz, Δz 1.5cm 25

Development of a Portable FMCW Radar Old radar ~350 lbs, 1 hr+ setup time Stationary, sweep ~2 m arc Extensive post-processing required, little information in field New radar <20 lbs, 10 min

26 Development of a Portable FMCW Radar Old radar ~350 lbs, 1 hr+ setup time Stationary, sweep ~2 m arc Extensive post-processing required, little information in field New radar <20 lbs, 10 min setup time Improved signal processing, sensitivity, resolution User-friendly MATLAB-based data acquisition software, real-time processing 20 complete radar profiles/sec Easily operated by 2 skiers 26

27 High Sensitivity (0-4 cm snow, cm SWE) 27

28 Low Variability 28

29 High Variability 29

30 Heavily Forested Site 30

31 Wind Effected Site 31

32 Spatial Variability Measurement is Repeatable 32

33 High resolution data on snow accumulation patterns is limited Standard methods are time consuming : ruler and density cutter New technology (LiDAR, radar) has recently resulted in snow depth/swe data, covering km scales, at orders of magnitude higher resolution (10 cm 1 m) 33

34 Quantifying Spatial Variability Compare values at different spacings, or lags Calculate average squared difference BUT: need ~150 pairs for accurate estimate [Webster and Oliver, 2001] 34

35 Quantifying Spatial Variability Generalized relative semi-variogram γ GR N 1 = 2N i= 1 ( u v ) i m 2 i 2 But plot: F = 100 2γ GR If F=50 at some separation, then on average, measurements separated by that distance vary by 50% 35

36 Theoretical Variogram Model 36

37 Effect of spatial structure NO SPATIAL STRUCTURE Constant mean, standard deviation 37

38 Effect of spatial structure MODERATE SPATIAL STRUCTURE Constant mean, standard deviation 38

39 Effect of spatial structure HIGH SPATIAL STRUCTURE Constant mean, standard deviation 39

$LiDAR snow depth fractal analysis results 1.$

40 LiDAR snow depth fractal analysis results 1.5 m resolution, 1km x 1km study areas Scale break in variogram meters, depending on environment [Deems et al, 2006] Scale break in power spectra 7-45m [Trullijo et al, 2007] Semivariance Buffalo Pass Walton Creek Alpine D = 2.48 D = 2.47 D = D = 2.91 D = 2.97 D = Distance (m)

41 Study sites 41

42 42

43 43

44 Barrow, Alaska 1) Variability controlled entirely by wind 2) Measurements made on 1 st year sea ice, so no small or large scale topography, no vegetation 44

45 Barrow, Alaska 1) Very similar range with 2 different instruments, good agreement with previous studies in alpine snow above treeline [Deems et al., 2006] 2) Magna probe shows more variability due to different support (1 cm vs 50 cm) 45

46 46

47 47

48 48

49 Devon Ice Cap, Canadian Arctic 1) Variability controlled by wind and smallscale topography (sastrugi) 2) Measurements made on ice cap summit; no large-scale topography or vegetation 49

50 Scales of spatial variability in radar signal 1 km 50

51 Scales of spatial variability in radar signal 50 meters 51

52 FMCW radar depths to Fall 04 surface 1) Shorter range compared to Barrow data, due to effect of small scale topography 2) Variability beyond range almost the same as Barrow data FMCW radar layer thickness 3) Note smooth transition in lower figure; lots of spatial structure within m

53 SMP surface hardness 1) Very similar range to hardness measurements in alpine snow [Kronholm and Birkland, 2005] 2) Much greater variability in hardness than layer thickness or depth 53

54 Monte-Carlo simulations along 1km profile (~10,000 total depths) 54

55 Monte-Carlo simulations along 1km profile (~10,000 total depths) 55

56 Monte-Carlo simulations along 1km profile (~10,000 total depths) 56

57 Monte-Carlo simulations along 1km profile (~10,000 total depths) 57

58 Annual accumulation in polar firn 58

59 Annual accumulation in polar firn 59

60 Annual accumulation in polar firn 60

61 Niwot Ridge, Colorado 1) Variability controlled by wind, small and large scale topography, and vegetation 2) Highly variable snow accumulation 61

62 Niwot Ridge, Colorado 1) Range agrees with previous studies below treeline [Deems et al., 2006] 2) Note that variability beyond range is more than 100% perpendicular to wind, and ~10% parallel to wind FMCW radar snow depths 62

63 Niwot Ridge, Colorado 1) Layer thickness variability shows more than 200% variability > 20 m for deepest layer, ~50% for midpack layer, and ~2 % for uppermost layer FMCW radar snow depths 2) Small scale topography combined with wind was major cause of variability; this effect is damped out for later snowfalls63

64 Senator Beck Basin, San Juan Mountains, Colorado 64

65 Above Treeline 65

66 Above Treeline 66

67 Below Treeline 67

68 Below Treeline 68

69 Conclusions Snow depth variability ranged from less than 5% to more than 200%, depending on environment Layer thickness variability ranged from less than 2% to more than 200% Variogram ranges agree well with previous studies Mechanical properties varied much more than layer thickness and snow depth Point measurements varied much more than radar measurements due to difference in support 69

70 Future Work quantifying spatial structure of snow Hierarchical geostatistics: [Barrash and Clemo, 2002, WRR] Subdivide basin into regions/zones with similar means/variances/spatial structure 70

71 Future Work estimating basin-wide SWE Ensemble stochastic simulation, optimizing solution for fit to both data and semi-variance [Johnson, Routh, Clemo, Barrash, Clement, 2007, WRR] χ = (SWE pred SWE obs ) 2 + β(γ pred γ obs ) 2 1) Determine uncertainty in basin-wide SWE estimates 2) Study optimal radar sampling patterns 3) Study minimal manual sampling patterns 4) Assimilate subsets of data into snow hydrology model - effect on accuracy of streamflow? 71

72 Future Work Use radar as tool to perform high resolution snow depth / SWE estimates for studying: Optimum snow radar sampling strategies Interpolation/extrapolation techniques in different environments Snow distribution models Scales from 0.01 m 10,000 m for remote sensing and snow hydrological modeling calibration / validation 72

HELICOPTER-BASED MICROWAVE RADAR MEASUREMENTS IN ALPINE TERRAIN

HELICOPTER-BASED MICROWAVE RADAR MEASUREMENTS IN ALPINE TERRAIN Hans-Peter Marshall, 1,2, * Karl Birkeland, 3,4 Kelly Elder, 5 Theo Meiners 6 1 Center for Geophysical Investigation of the Shallow Subsurface,