Richard Kelly Department of Geography University of Waterloo Ontario, Canada
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Transcript of Richard Kelly Department of Geography University of Waterloo Ontario, Canada
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
The impact of physical temperature on brightness
temperature observations over snow for NASA’s AMSR-E
Richard KellyRichard Kelly
Department of Geography Department of Geography University of WaterlooUniversity of Waterloo
Ontario, CanadaOntario, Canada
Marco TedescoMarco TedescoCity College of New York - CUNYCity College of New York - CUNY
New York, USANew York, USA
Thorsten Markus & James FosterThorsten Markus & James FosterNASA/GSFC, USANASA/GSFC, USA
AMSR-E St. Louis Creek, CO
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Bri
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tness T
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-36V
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xv36
xv18xv18-xv36
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
57.07N, 86.22E
ObservationThere are high temporal frequency variations in the brightness temperatures (and therefore retrievals) at 36, 18 and 10 GHz.
QuestionWhat controls/causes high frequency (day to a few days) changes?
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Outline
• Simple theory
• Met station measurements
• AMSR-E observations
• Summary & further work
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
What controls the brightness temperature (Tb) variation from a snow-covered scene as observed by a spaceborne microwave radiometer?
(1)
• Tbs is snow Brightness Temperature
• Tbv is vegetation (tree canopy) Brightness Temperature
is a atmospheric transmissivity
• Tbatm atmospheric brightness temp (up & down) (assume negligible in this case)
• NB Tb responses are frequency dependent.
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Tbscene = (Tbs + Tbv )Γτ + Tbatm
Simple theoretical standpoint
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Tbv
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Tbs
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Tbv
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Tbground = ffTbv + (1− ff )Tbs
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
What controls the brightness temperature (Tb) variation from a snow-covered scene as observed by a spaceborne microwave radiometer?
Deconstructing previous expression:
(2)
• Ts is snow physical temperature:– Air temperature is the driver here and changes through time: the
snowpack thermal gradient is constantly adjusting.– Sub-nivean temperature probably stable
• es is snow emissivity and related to bulk snow properties: • grain size, snow crystal packing, number of scatters in the path
length [SWE], water content [free or bounding] • probably (?) buried vegetation effects too
• Tv is vegetation physical temperature
• ev is vegetation emissivity
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Tbscene = (esTs + evTv )
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Tbscene = (Tbs + Tbv )
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
What is the role of Tv or Ts ?
• In the models, Tv and Ts are often equated or combined as the effective temperature, T0, where:
(3)
• T0 is also computed through (e.g.)
(4)
where Tair is the air temperature and Ts is the snow temperature.
But, are there overlooked implications to these assumptions ?
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Tbscene = T0(es + ev )
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T0 =Tair + Ts
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AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
What do physical temperature measurements suggest?
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Tsskin
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Tv
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Tspack
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Tsoil
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
CLPX Experiment Data
Colorado: 19-24 Feb. 2003• 3 MSAs (25x25km)• Each MSA had 3 ISAs
(1x1km):– Fraser ISA: moderate
snow accumulations & denser forest fraction
– Rabbit Ears: deep snow accumulations & less dense forest fraction
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Fraser Experimental Catchment MSA
• St. Louis Creek ISAs (forest and moderate snow) and LSOS site.
SWEmean 189mm
SWE55 mm
Depthmean 80 cm
Depth 20 cm
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Rabbit Ears MSA
• Walton Creek ISA (moderate forest and deep snow)
SWEmean 580 mm
SWE115 mm
Depthmean 189 cm
Depth 55 cm
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
In situ measurements: dense pine at CLPX LSOS
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
In situ measurements: Rabbit Earsless dense forest
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Summary of in situ measurements• Scene Tb’s are sensitive to (constituent surface) physical
temperature.
• (Tv) Vegetation canopy temperature is likely affected by air temperature– overall large fluctuations
• (Ts) Snow temperature at the near air-snow interface varies more than at near basal snow temperature.– overall small fluctuations
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Tbscene = (esTs + evTv )
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
How might Tphys affect PM SWE retrievals?
AMSR-E Observations
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Retrieval approaches based on R-T theory (Chang et al., 1987 & 1996):
where a is a calibration coefficient and ff the forest fraction. If this is deconstructed further:
where es18 and es36 are snow emissivities at 18 and 36 GHz respectively.
Is SWE a function of To / Tv / Ts ?
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SWE = a(Tbscene18 −Tbscene36) /(1− ff ) [mm]
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SWE = a[(es18Ts + ev18Tv ) − (es36Ts + ev37Tv )]/(1− ff )
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
AMSR-E Tbs
AMSR-E St. Louis Creek, CO
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xv36xv18xv18-xv36
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
AMSR-E Tbs for Fool Creek, CO
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AMSR-E St. Louis Creek, CO
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Tbs at adjacent CLPX ISA sites (separated by ~8km)
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Variations of surface temperature (Tair) and Tbs at 18V & 36V
Fraser: St. Louis data
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Date
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Tphys
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
SWE = a(Tbscene18 −Tbscene36) [mm]
Variations of surface temperature (Tair) & Tb18V-Tb36V [K]
But which of these channels contributes most to the variations?
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Variations of Tair match Tb variations (somewhat) at low frequencies but less at 36 GHz ……
Fraser (St. Louis Creek), Colorado - dense tree cover.
10V GHz 18V GHz 36V GHz
R2 = 0.4775
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R2 = 0.352
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AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Again, variations of Tair match well variations at low frequencies and to some extent the 36 GHz ……
Rabbit Ears, (Walton Creek), Colorado - dense tree cover.
R2 = 0.3115
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AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
SummaryWhat causes apparent fluctuations in the SWE estimates
or Tb18-Tb36?•Contribution of Tair to Tbs at lower frequencies is greater than higher frequencies;•‘Surface’ temperature-related effects (driven by air temps) are a likely cause of Tb fluctuations;•Vegetation temperatures are likely to change with air temperature;•Vegetation emissivity changes are small (excepting snow in the canopy);
•Snowpack temperature variations Ts are not a likely cause;
•Ground temperature/emissivity variations are not a likely cause;•Snow emissivity changes in response to punctuated snowfall events and seasonal snowpack evolution but not at the time scale under consideration.
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SWE = a[(es18Ts + ev18Tv ) − (es36Ts + ev37Tv )]/(1− ff )
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Conclusions & Further Work•We are looking at correcting for Ts & Tv in the retrievals.
•Can we estimate Tair from AMSR-E? (synergy w/ John Kimball). If achievable, Tair could be used to help drive a snowpack stratigraphy model (information needed in retrieval parameterization).•Other sites under test (Canada: tundra and Boreal forest; Russia).•A simple fix could be to ratio Tb18/Tb36 rather than subtract Tb18-Tb36•Validation of current version is in progress for Sept 2008 - refinement activity will follow.
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
Rationale• Retrieval approach is often snapshot in scope
• Algorithms generate coarse-resolution SWE estimates at ~25 x 25 km
• Uncertainties in the estimates are related to algorithms and spatial resolution
Monthly average
AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (
In situ measurements: open pine at CLPX LSOS