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Satellite Observations of Clouds and the Earth Radiation Budget over Snow: The Importance of Surface Roughness
Stephen R. Hudson
Other collaborators: Richard E. BrandtThomas C. Grenfell, Delphine Six (LGGE),and Seiji Kato (NASA-Langley)
Advisor: Stephen G. Warren
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Party for Steve Hudson
7 pm today
6847 36th Ave NE
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Satellite Observations of Clouds and the Earth Radiation Budget over Snow: The Importance of Surface Roughness
Stephen R. Hudson
Other collaborators: Richard E. BrandtThomas C. Grenfell, Delphine Six (LGGE),and Seiji Kato (NASA-Langley)
Advisor: Stephen G. Warren
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Outline
• Introduction– What is directional reflectance? Why is it important?
– Background about the East Antarctic Plateau and the measurements and models I have used
• How does snow-surface roughness affect the directional reflectance?
• What impact does this roughness effect have on cloud observations over snow?
• By accounting for the roughness effect, can we evaluate CERES observations and algorithms?
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What is directional reflectance?
• When the sun shines on a surface, the reflected radiance varies with direction.
Photo byJoseph Shaw, NOAA
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What is directional reflectance?
• When the sun shines on a surface, the reflected radiance varies with direction.
• This variation is less evident over snow than over many other surfaces, but it is still important.
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Measuring directional reflectance
• Anisotropic reflectance factor (R)– Average value is 1– An isotropic surface has R = 1 at all angles
• Bidirectional reflectance factor (BRF) – Average value is equal to the albedo– An isotropic surface has BRF = at all angles
r
r
F
IR
p=
o
r
F
IBRF
p= = R
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Okay, so what?
• Understanding the directional reflectance is important for interpreting satellite measurements.
• Satellites measure radiance coming from one angle; users must account for the anisotropy of the radiance field to determine flux or to estimate other properties.
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Okay, so what?
• Understanding the directional reflectance is important for interpreting satellite measurements.
• Satellites measure radiance coming from one angle; users must account for the anisotropy of the radiance field to determine flux or to estimate other properties.
Lookingstraightdown
Looking nearhorizon, awayfrom the sun
Looking nearhorizon, towards the sun
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Background — Observations
• We made spectral directional-reflectance observations of the snow at Dome C– 75°S, 123°E, 3250 m– 350—2400 nm– o 52—87°
• Representative of much of the East Antarctic Plateau
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Background — Observations
The observations were made with a 15° conical field of view from 32 m above the surface to capture the effects of the natural snow-surface roughness
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Background — Model
• The model results I will show come from the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model
Atmospheric Profile
T, P, H2O, O3
Clouds
Aerosols
LOWTRAN 7
Cloud Model
Aerosol Model
Surface Model
DISORT
Radiance andFlux at Sfcand TOA
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How does surface roughness affect R?
• Looking towards the sun you see shaded faces
• Looking away from the sun you see faces tilted towards the sun
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Roughness effect at Dome C
• Used SBDART to model the surface reflectance with a variety of phase functions (Mie, HG, Yang and Xie)
• Placed the snow under a clear, summertime-average, Dome-C atmosphere
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Roughness effect at Dome C
• Rough aggregate grains produce the best match between the model and observations, but the model produces significant error consistent with macro-scale roughness effects for all of the phase functions
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Roughness effect at Dome C
• The error increases with solar zenith angle
• The roughness has little effect on near-nadir intensity
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How do clouds affect R over snow?
From Welch and Wielicki 1989
• Early nadir-viewing satellite observations suggested clouds may reduce the reflectance over snow.
• This was unexpected since the smaller particles in clouds should raise the albedo.
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How do clouds affect R over snow?
From Wilson and Di Girolamo 2004Multiangle Imaging SpectroRadiometer
• Later observations showed clouds do raise the TOA albedo over snow, but also enhance the anisotropy over snow.
• This was unexpected since the smaller particles in clouds should be more isotropic scatterers than snow grains.
Nad
ir
Vie
wFo
rwar
d V
iew
Cloud
Cloud
Clear
Clear
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Effect of clouds on R over snow
• We believe much of this effect is caused by clouds hiding the surface roughness, not by differences in the single-scattering properties of snow and cloud particles
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Effect of clouds on R over snow
• The key is that the height variations at the cloud top are very small compared to those on the snow surface, in units of optical depth.
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Effect of clouds at Dome C
• Nights with shallow fog allowed us to observe the reflectance of a cloud over the snow surface
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Observation of fog at Dome C
• The difference caused by fog at Dome C is similar to the error in the plane-parallel modeling results
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Roughness effect at Dome C
• The error increases with solar zenith angle
• The roughness has little effect on near-nadir intensity
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Modeling fog at Dome C
• Using SBDART to model the upwelling intensity above a thin cloud over a surface with the observed BRDF gives results very similar to the foggy observation
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Observed effect requires rough surface
• When the same cloud is placed over a modeled (flat) snow surface it does not produce the correct effect
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Comparison with MISR
• Modeled TOA 866-nm radiances above our parameterized surface agree reasonably well with MISR observations of clear and cloudy scenes
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Comparison with MISR
• Modeled TOA 866-nm radiances above our parameterized surface agree reasonably well with MISR observations of clear and cloudy scenes
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Summary—So Far
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Summary—So Far
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A little about CERES
• Clouds and the Earth’s Radiant Energy System; follow-on to ERBE
• Instruments measure broadband-solar, longwave-window, and total radiances at TOA; algorithms estimate other quantities.
• Meant to improve on ERBE accuracy, providing better than 1% SW calibration
• Two instruments fly on each of two satellites that see Dome C about twice each day, giving many observations of the area.
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Can we assess CERES SW calibration?
• Use the parameterized surface in spectral runs with SBDART to compare modeled and CERES solar TOA radiances
• CERES data from 4 clear days in January 2004 and 2005; about 20,000 observations
• Use CERES radiance data that include all reflected solar energy at all wavelengths, and no emitted energy
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Can we assess CERES SW calibration?
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Which is right?
• We would like to know if the model is overestimating the radiance or if CERES is underestimating it.
• Comparisons of the modeled radiances with MISR observations suggest the model is accurately calculating the radiance, or is slightly underestimating it.
• Some work by people on the CERES team also suggests the difference could be due to a bias in CERES data (Charlock; Kato).
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Can we assess CERES ADMs?
• To convert the radiance measurements to flux estimates, the CERES team uses Angular Distribution Models.
• These ADMs provide the average R pattern at the TOA for each scene type and solar zenith angle.
• The R patterns can be compared with model results to evaluate the algorithms separately from the calibration issue.
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Can we assess CERES ADMs?
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Conclusions
• Studies involving the directional reflectance over snow must consider surface roughness.– Observations must be made with a footprint
that is large enough to accurately capture the effect of the roughness.
– Models of radiative transfer over snow-covered regions should not treat the snow as a plane-parallel surface.
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Conclusions
• The enhanced anisotropy caused by clouds in the reflected radiance field above polar snow surfaces can be explained by accounting for the surface roughness in the clear-sky model.
• The clouds hide the rough surface with a surface that is very smooth in units of optical depth.
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Conclusions
• The parameterization developed from our surface reflectance observations can be used to assess satellite observations and products.
• Doing this for CERES suggests a negative bias in the instruments’ shortwave channels, but indicates that the method used to convert radiance observations to fluxes works well.
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Future Work
• Work with Seiji Kato to further validate the CERES algorithms for converting radiance to flux.
• Examine the importance of atmospheric aerosols or other constituents on R at the TOA.
• Look at CERES ADMs for clouds over permanent snow.
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Acknowledgements
• Steve Warren
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Acknowledgements
• Steve Warren
• Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick
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Acknowledgements
• Steve Warren
• Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick
• Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny
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Acknowledgements
• Steve Warren
• Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick
• Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny
• Mike Wallace
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Acknowledgements
• Steve Warren
• Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick
• Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny
• Mike Wallace
• Seattle friends
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Acknowledgements
• Steve Warren
• Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick
• Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny
• Mike Wallace
• Seattle friends
• Family
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Can we assess CERES SW calibration?
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Which is right?
• Comparisons of the modeled radiances with MISR observation suggest the model gives accurate or low estimates of radiance.
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Ice clouds have similar qualitative effect
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Roughness effect on broadband albedo