Retrieval of snow temperature and grain size Rune Solberg, NR

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Retrieval of snow temperature and grain
sizeRune Solberg, NR
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Background

• Parameters
• Surface Temperature of Snow (STS)
• Snow Grain Size (SGS)
• Definitions
• STS Snow skin surface temperature
• SGS Optically effective grain size
• State of the art
• STS can be measured in thermal infrared
  wavelengths with radiometers
• SGS can be measured in the combined visual and
  infrared region by broadband radiometers
  (relative reflectance) and in the near infrared
  by spectrometers (molecular absorption features)
• Spatial resolution
• STS 1 km
• SGS 250 m 1 km
• Motivation
• STS A parameter in hydrological models,
  climatologically important
• SGS Can be used to determine how old the snow is
  and snowmelt start
• Problems
• STS Removing the temperature contribution from
  the atmosphere
• SGS Reduce noise, effects generated by other
  mechanisms than grain size

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EnviSnow development STS

• Tailored algorithms developed for Sea Surface
  Temperature (SST) to snow applications
• Compared four algorithms to determine the best
  for snow temperature retrieval
• Validated and demonstrated the chosen algorithm

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STS algorithm

• Experiments determined Keys algorithm (split
  window view angle correction) to be overall
  best for snow monitoring
• The retrieval algorithm requires that the
  emissivity of the surface is known. Therefore, we
  restrict the use to snow-covered surfaces
• Atmospheric correction Done by measuring the
  atmospheric effect at two wavelengths and then
  correcting according to atmospheric path length
• Can be applied on both NOAA AVHRR and Terra/Aqua
  MODIS

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STS production line
MOD02 1km L4 dataHDF-format  
MOD35 1km L4 dataHDF-format  
Cloudmask
Snow mask
L11L12
view angle
Land mask  
Geometrical correction
Geometrical correction
geocorrected masks
geocorrected view angle
geo- corrected L11, L12
Retrieval algorithm for STS Key
surface temper. of snow (STS)
T11 T12
Generation of brightness temperatures
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Results

• Comparison with field measurements shows
  excellent results
• At 0C we found an accuracy of about 0.5C in our
  test site
• STS maps limited to areas of 100 SCA

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EnviSnow development SGS

• Compared a set of algorithms based on indices to
  obtain better understanding of their features
  when applied under conditions of variable terrain
  and snow metamorphosis
• Selected the overall best algorithm
• Validated and demonstrated the chosen algorithm

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SGS algorithm

• Snow undergoes continuous metamorphosis from
  complex crystals to solid ice
• Two main SGS retrieval approaches
• Measuring relative reflectance, VIS IR
• Measuring local spectral absorption
• Evaluated various indices based on an arithmetic
  combination of two or more bands in VIS and IR

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Field and satellite measurements of STS and SGS
Heimdalen-Valdresflya test site, 2003. HH
Heimdalshø, VF Valdresflya
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Results

• Snow metamorphosis (as observed in the field) can
  be accurately measured by remote sensing
• Rapid increase in effective grain size measured
  at snowmelt start
• Application limited to areas of 100 SCA

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Examples of SSW producs
White - dry, cold snow STS lt -2C.
Light/dark blue - dry/moist -2C lt
STS and -0.5C Yellow/orange - moist -0.5C
lt STS 0.5C. Red - wet 0.5C lt 1.0C
Unchanged SGS. Increasing SGS
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Conclusions

• Snow Temperature
• A sea surface temperature algorithm has been
  adapted to snow applications
• Validated and found very accurate
• Demonstrated in a semi-operational setting
• Results mature for operational use
• Snow Grain Size
• Various indices tested and evaluated
• Selected algorithm studied under varying terrain
  conditions for developing snow metamorphosis
• Appearance of liquid water in the snow pack
  clearly detectable in a time series of SGS data
• Results mature for operational use