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Retrieval of Snow Water Equivalent Using Passive Microwave Brightness Temperature Data

Retrieval of Snow Water Equivalent Using Passive Microwave Brightness Temperature Data. By: Purushottam Raj Singh & Thian Yew Gan Dept. of Civil & Environmental Engineering University of Alberta, Canada. RESEARCH OBJECTIVE.

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Retrieval of Snow Water Equivalent Using Passive Microwave Brightness Temperature Data

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  1. Retrieval of Snow Water Equivalent Using Passive Microwave Brightness Temperature Data By: Purushottam Raj Singh & Thian Yew Gan Dept. of Civil & Environmental Engineering University of Alberta, Canada

  2. RESEARCH OBJECTIVE To Develop new SWE retrieval algorithms using Passive Microwave Brightness Temperature Data of SSM/I Sensor for a prairie like environment of North America *SWE = Snow Water Equivalent (cm)

  3. INTRODUCTION • Snow: Dominant source of Water Supply, contributes up to 70% in many parts of Canada • Seasonal Variation of SWE: Critical to an effective management of Water Resources • Snow course & snow gauge data: Point measurements & Limited • Airborne Datafor SWE: Expensive

  4. PASSIVE MICROWAVE RADIOMETRY • Passive Microwave (PM): can penetrate clouds & provide information during night • Daily PM data available on a global basis • Satellite Microwave data: To retrieve SWE Chang et al.,1976; Goodison et al.,1986; etc. • Basis of microwave detection of snow: Redistribution of upwelling radiation (RTM, SM)

  5. STUDY SITE • Red River Basin (120,000 Km^2) • Elevation Range: 237-552m Figure 1. The Red River basin study area of eastern North Dakota and northwestern Minnesota.

  6. DATA • Airborne SWE Data(88, 89 & 97)->NWS • SSM/I Brightness Temperature Year SSM/I Ascending/Descend Source 1988: DMSP(F8) 6:13 18:13 -> NSIDC 1989: DMSP(F8) 6:13 18:13 -> NSIDC 1997: DMSP(F10) 22:24 10:24 -> MSFC 1997: DMSP(F13) 17:46 5:46 -> MSFC • Other Data Land Use/Cover & DEM(30 arc”) -> USGS Temperature & Precipitation -> HPCC Total Precipitable Water(1 deg.) -> TOVS

  7. Airborne SWE Data: ->NWS Year 1988 1989 1997 # of Airborne Data: 65 241 192 # of Gridded Data: 52 175 197 # of Dry Snow Cases: 16 121 119 Mean SWE(cm) 3.43 9.25 13.55 • Cumulative Snowfall 1997 Cumulative snowfall in cm. 1989 1988

  8. Selection Criteria for Dry Snow Cases • V37<250°K; V19-V37=>9 °K ! Goodison et al.,’86 • V37-H37 => 10 °K ! Walker & Goodison’93 • P_factor > 0.026 • V37 > 225 °K (DMSP-F8) • P_factor < 0.041 (F10/F13) Where, V37: 37GHz Vertical Polarization Brightness Temperature(°K) P_factor or polarization factor = (V37-H37)/(V37+H37) ! From Present Study

  9. RETRIEVAL ALGORITHMS Goodison et al.,’94:SWE=K1+K2(V19-V37) ..(1) Chang et al.,’96: SWE=K3+K4(H19-H37)(1-AF) ..(2) Proposed: (a) Conventional Regression (b) PPR (a) SWE = K5(V19-H37) + K6(AMSL) + K7(1-AF) + K8(1-AW)TA + K9 (TPW) ..(3)

  10. Projection Pursuit Regression (PPR): Figure 2.Calibration Results: Fraction of unexplained variance (U) versus the number of terms (Mo) for the PPR model using selected dry snow cases, ascending set of SSM/I data of 1989.

  11. DISCUSSION OF RESULT Figure 3. Scatterplots of SWE from Airborne Gamma Ray Vs. SWE Retrieved from SSM/I using Existing (Eq. 1) and Proposed (Eq. 3: Multi-variate Regression) Algorithms.

  12. DISCUSSION OF RESULT (Contd.) Figure 4. Scatterplots of SWE from Airborne Gamma Ray Vs. SWE Retrieved from SSM/I using Existing (Eq. 2) and Proposed (Eq. 4: Projection Pursuit Regression) Algorithms.

  13. Necessity to Add Shift-Parameter(or “offset’) • Shift-Parameter (SP) required at validation stage. • Existing retrieval algorithms: show some improvement with SP. • SP depends on the overall SWE of each year. • Example: (Number encircled are SP for Calibration Year) • Year: 1988 1989 1997 • Mean SWE(cm) 3.43 9.25 13.55 • Shift-Para(1) -5.00 0.00+ 4.00 • Shift-Para(2) 0.00+5.00 + 9.00

  14. DISCUSSION OF RESULT (Contd.) Figure 5. Distinct patterns of inter-annual SWE retrieved from exist- ing algorithms (Eqs. 1 & 2) when plotted against one of the proposed algorithm (Eq.3). Marked improvement with Shift Parameter (SP).

  15. Reason behind Shift-Parameter • Snowfall, temperature gradient & snow metamorphism process vary from year to year • Scatter-induced darkening is not a function of Scattering albedo alone. It is also a function of Snow-Depth (England, 1975). • Also Retrieval algorithms of statistical nature are biased towards the mean. • * Scattering of TB by snow grains within the dielectric layer gives rise to Scattering albedo

  16. Reason behind Shift-Parameter (contd.) • Figure 6. Scatter-induced darkening (TBo) versus scattering albedo (o) for various thickness (D) of dry fresh snowpack at 273 K, a case of free space microwave wave-length (o) of 10 cm (adapted from England, 1975).

  17. CONCLUSIONS • Reasonably accurate SWE retrieved from SSM/I data from different satellites using Proposed algorithms and calibration techniques like Projection Pursuit Regression (PPR) & multi-variate regression. • Introduce a Shift-parameter (SP) to retrieval algorithms. Magnitude of SP depends on the overall SWE difference between calibration & validation years. • Introduce new criteria for selecting dry snow cases that are affected by depth-hoar, and/or large water bodies.

  18. FOR FURTHER DETAILS ON THIS POSTER PRESENTATION • Singh, P. R., and Gan, T. Y. (2000), Retrieval of snow water equivalent using passive microwave brightness temperature data. Remote Sensing of Environment. 74(2):275-286. Thank You

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