Snowpack properties and microwaves: Advances and Challenges

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Snowpack properties and microwaves Advances and
Challenges Amir E Azar, Al Powell, Narges
Shahroudi, Dugwon Seo, and Reza
Khanbilvardi NOAA-CREST, NOAA/NESDIS, The City
University of New York CREST Annual
Meeting Mayaguez, Puerto Rico February 2008
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• Snow Water Equivalent (SWE)
• Algorithm development and improvement of SWE
  estimated by satellite microwave data using
  winter NDVI data in Great Lakes area
• Snow Grain Size and Microwaves
• Analysis of snowpack behavior changes and their
  effect on microwave emissions using
• Satellite Brightness Temperature (Tb)
• Emissivity Data
• Analysis of snow pack profile and changes of its
  properties with respect to each other
• Development of a linear model to estimate snow
  grain size profile based on temperature

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Improvement of Microwave-based SWE Estimating
Algorithm over Great Lakes Area Using NDVI Data
SWE (A NDVI B)GTV SWECSSID GTV
(19v-37v), SSI (22v-85v) NDVI Winter time,
obtained from a 10-day composite image for
January 1994, A, B, C, and D entering the above
formula were found equal to 35, 2, 0.9, and -3
respectively.
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The Microwave data are underestimating over the
forested area (surrounding the lake) by combining
the microwave with NDVI
SSM/I Data, DMSP
NDVI derived from AVHRR
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Using snow emission model from Helsinki
University of Technology (HUT) the effect of snow
grain size on microwaves is quantified
Prigent, C. et al, 2006
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• Comparison and Evaluation of snowpack properties
  derived from SSM/I brightness temperature with
  NASA-GISS Emisivities using snow emission model
  (HUT)
• The results indicate a better performance of
  emissivity data in estimation of snow pack
  properties

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• Collected Data
• Snow Depth
• Snow Density
• Snow Temperature
• Snow Grain Size

Measurements in snow pits, (NSIDC) ,
www.nohrsc.nws.gov
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Meso-cell (MSA) ,Intensive Study Area (ISA),
and Snow pits

• Meso-cell Study Area
• (25 x 25 km), from NSIDC

Depth
Intensive Study Area (ISA,1km x 1km), from NSIDC
Temperature
Grain Size
Density
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Average of snow parameters (IOP 1 IOP2)
D2Depth in Feb. D3Depth in Mar. T2Snow
temperature in Feb. T3Snow temperature in
Mar. G2Grain size in Feb. G3Grain size in Mar.
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• Variation of snow
• Temperature,
• Grain size, and
• Density
• in the snow profile
• Fraser Alpine (pits 6 and 12)

• Snow grain size and snow temperature, and snow
  density are highly correlated within the profile
  of snow

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• Comparison of the correlation coefficients
• Grain size and Density
• Grain size and Temperature

• Snow grain size and snow temperature have high
  and positive correlations
• The correlation between snow density and snow
  grain size is not consistent

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Derivation of the Average Regression line
between snow grain size and snow temperature
The regression line slope and intercept changes
over various ISAs and MSAs
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Derived equations for estimating snow grain size
using snow temperature profile and their
performance
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• Error Increase over certain ISAs due to lack of
  grain size measurements

• Snow pit 08 in ISA, Walton Creek has only 1
  measurement for grain size below 80 cm depth

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(A) estimated grain size using measured
temperature
(B) estimated grain size using estimated
temperature
IOP1 (Feb 2002)
IOP3 (Feb 2003)
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• Winter NDVI data could successfully applied to
  improve the estimated SWE by microwave data
• Snow grain size has significant effects on
  microwave emissions and it is a challenge to
  account for those effects directly
• Snow emission models such as HUT can be used to
  evaluate the performance of Tb data vs.
  emissivity data in estimating snow pack
  properties
• The correlation between snow grain size and snow
  temperature is higher and more consistent as
  compared with snow grain size and temperature
• A regression line was derived based on the
  correlations between snow temperature and snow
  grain size
• The snow grain size profile was estimated using
  the temperature data

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• Future Works
• Setting up a station to measure
• Microwaves (ground-based) in 37 GHz and 19GHz in
  vertical and Horizontal polarizations
• Snowpack properties (depth, SWE, and grain size)
• Temperatures (air, and snow)
• Validation of different radiative transfer models
  including CRTM and MELMS using the obtained data
• Application of the results to improve existing
  satellite snow retrieval algorithms

PAMIR microwave radiometer