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GPS snow sensing: results from the EarthScope Plate Boundary Observatory Kristine Larson, Felipe Nievinski Department of Aerospace Engineering Sciences.

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Presentation on theme: "GPS snow sensing: results from the EarthScope Plate Boundary Observatory Kristine Larson, Felipe Nievinski Department of Aerospace Engineering Sciences."— Presentation transcript:

1 GPS snow sensing: results from the EarthScope Plate Boundary Observatory Kristine Larson, Felipe Nievinski Department of Aerospace Engineering Sciences University of Colorado at Boulder

2 Why this paper? Non-standard GPS application Multipath application Implications for – Expanded / novel applications of NGS CORS – “one NOAA” … snow and soil moisture = water cycle

3 Outline Multipath / reflections basics Specifics of snow sensing technique – How it’s done – Why it’s useful Paper overview Tie in to NOAA / NGS work that could be done with CORS

4 Definition of some terms Multipath: signal travels by multiple paths SNR: signal-to-noise ratio (one type of GPS observable, derived from phase tracking; sensitive to phase multipath) PBO: Plate Boundary Observatory (CORS-type network for plate tectonics) SNOTEL: network of in situ snow sensors (truth measure for snow) PRN: pseudorandom noise code (satellite ID number)

5 GPS Multipath Satellite signals become a plane wave by the time they reach Earth’s surface. Some ray paths are received by antenna on the ground: Direct path After bouncing off objects

6 GPS Multipath – SNR from simple ray tracing Slide modified from K Larson PdPd PrPr

7 GPS Multipath : remove direct + gain pattern Slide modified from K Larson Ignore the direct Analyze only the multipath part of SNR 10 deg Elevation Angle 30 deg

8 Each rising or setting GPS satellite samples a ground track  Interference pattern in SNR data Slide courtesy of K Larson

9 Snow Depth/Height estimate reflector height from frequency of interference pattern P101 (Utah) Slide courtesy of K Larson

10 Snow Depth/Height sensing footprint (Fresnel zone) also changes with snow height Fresnel zone = function of Height above surface Elevation angle of satellite

11 Result: 20 observations per day (maximum) Sampling area: ~1000 m 2 Slide modified from K Larson Specular reflection point (bounce point off of snow/ground)

12 Spatial resolution cm 3 soil moisture probes 1000 m 2 GPS 10-50 km Satellite missions 10 m 2 SNOTEL

13 Terrain Slope Effect Error in snow depth < 2-5 cm for ground slopes of 8 degrees or less

14 Why you can’t apply this technique everywhere DateSnow Depth d300/10No snow (bare ground) d330/10~ 50 cm snow d080/11~ 100 cm snow

15 P101 (works great) Local slope calculations show low-angle terrain to NW and due S Terrain is wide open, no trees.

16 P101 … most of the time Complex reflections to the NE, observed with PRN31. not all satellite tracks at a station will work equally well

17 P360 (also pretty good) Absence of data makes peak overly wide Site is very flat over the sensing zoneOpen scrubland

18 P711 (where are the wiggles?) … Lots of trees in the sensing footprint Overwhelms the relatively flat terrain… Sometimes a station is “great on paper” but other factors will override potential signal

19 P720 (nada) Trees, trees and more trees

20 How to make a snow depth time series 1.Pick satellite tracks (strong LSP peaks) 2.Analyze rising and setting separately (different hunks of ground) 3.Get ground height from summertime data 4.Every day 1.Peak LSP (LSP height minus ground height = snow depth) 2.Average all good satellites into site mean (scatter of satellites = error bar)

21 Snow Depth Over 2 Years Utah Idaho Wyoming 15 km apart

22 Snow Depth Over 2 Years Utah Idaho Wyoming

23 Cautionary Tale: SNR data quality Not all GPS receivers / data types provide good SNR! Breaking the multipath “rules” Poor resolution (firmware) P101 Trimble NetRS PRN25 vs time L2C, PRN25 L1C, PRN25 L2P, PRN18 L2C with 1.0 dB resolution

24 What about CORS?

25 CORS case study: MNDOT Network All Trimble receivers Operating at 5-sec rates Mix of NetRS NetR3 NetR5 NetR9 Stations often next to farm fields Asked VRS operator to turn on L2C in January 2011 COOP stations across state Home of SNOWDAS

26 ASHL Looking West

27 MRTA

28 To integrate CORS into PBO H2O Collect SNR data Evaluate SNR quality of non-Trimble receivers (Leica, Topcon, etc) – Varies by model, firmware release – Varies for phase observable (L1C, L2C, L2P, L5) Manually sort stations – Exclude 100% urban – Flat terrain – Find rural, or semi-rural (side of building next to field) Not Good Good

29 Adding CORS to PBO H2O would get our logos here

30

31 Signal-to-Noise Ratio (SNR) Measure of signal strength for each satellite Total SNR = direct plus reflected signal(s) – Direct amplitude = dominant trend – Multipath signal = superimposed on direct

32 Multipath: One man’s error is another man’s signal Multipath = direct + reflected signals Error: increases apparent distance from satellite Signal: amplitude and phase of GPS signal affected by reflecting surface

33 Multipath Geometry amplitudesangles A d direct signal amplitude A m multipath signal amplitude h reflector distance  angle of reflection  satellite elevation angle  path delay

34 Simplified Multipath Model and SNR Recorded SNR = direct + multipath signal Carrier phase error: Code (pseudorange) error (short delay) : multipath direct composite direct multipath  MP composite

35 What do these equations tell us? Oscillations in SNR, phase MP, and pseudorange MP all have common frequency MP frequency – Key to determining  – Function of Reflector distance h Reflection angle  GPS wavelength Fast MP = far away Slow MP = nearby For a fixed reflector, satellite motion generates time-varying signature

36 SNR, after removing direct signal Wiggles… Sinusoidal Result from interference of direct and reflected signals Repeat daily Have amplitude, frequency, and phase sensitive to soil moisture


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