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Why Study Clouds?
- They are pretty to look at!
- “Knowledge of cloud properties, including
The Accuracy of Retrieved Cloud Properties Impacted by Systematic - - PowerPoint PPT Presentation
The Accuracy of Retrieved Cloud Properties Impacted by Systematic - - PowerPoint PPT Presentation
The Accuracy of Retrieved Cloud Properties Impacted by Systematic Error By Leandra Merola Mentor Odele Coddington Why Study Clouds? They are pretty to look at! Knowledge of cloud properties, including their spatial and temporal
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Most importantly clouds effect our climate.
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Satellite Downwelling Radiance Upwelling Radiance Aerosols SSFR (Solar Spectral Flux Radiometer) (shortwave range 300 – 1700 nm, spectral resolution 8 – 12 nm) Irradiance – is the amount of radiation emitted from an object integrated over the hemisphere. It is measured in W m-2 nm-1. Albedo – a measure of the reflectivity of an object. (upwelling irradiance/downwelling irradiance.)
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radiative transfer model Cloud: Mie theory (, g, ) ri, i Absorbing Gases: O3, O2, H2O surface spectral albedo Top of atmosphere irradiance atmospheric conditions P, T, RH
Inputs for Forward Model
5
tables of spectral irradiance for ri, i pairs
Modeled albedo – Solid lines = effective radii of 1 μm Dashed lines = effective radii of 30 μm Optical thickness increases from blue to red
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Wavelength (nm)
0.0 0.2 0.4 0.6 0.8 1.0
Albedo
400 600 800 1000 1200 1400 1600 6
radiative transfer model SSFR
(Solar Spectral Flux Radiometer)
measured spectral irradiance best fit ri, i
Retrieval Method
Modeled Data Measured Data tables of spectral irradiance
This method is know as inverse problem solving. 5 retrieval wavelengths
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Bias – The Ultimate Enemy
- Overlying absorbing aerosols reduce albedo.
- These aerosols bias the cloud retrieval giving us inaccurate
information about the cloud, which could be confused with the indirect aerosol effect.
- Aerosol 1st Indirect Effect – when aerosols physically change
cloud microphysical properties and therefore change its albedo.
Increased Optical Thickness Solid Line Increased Effective Radii Dashed Line
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GENRA
(Generalized Nonlinear Retrieval Analysis)
- GENRA is a statistical program that lets us study
cloud retrievals from many cloud types with and without systematic error in an efficient way.
- GENRA
– Makes use of the pre-existing look up table. – Defines pdfs of measured and modeled albedo. – Aerosol impact is treated as a systematic error (shift) in the model pdf. – Solution pdf is the expected behavior in retrieved cloud properties.
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Shannon Information Content
- A formal mathematical theory to quantify the
information gained by making a measurement.
- Maximum information content is dependent
- n resolution of the look up table.
- It’s a scalar.
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1 2 3 4 5 6 7 500 1000 1500 2000 Wavelength (nm)
Shannon Information Content for Optical Thickness
Albedo Scaled Albedo 1 2 3 4 5 6 7 500 1000 1500 2000 Wavelength (nm)
Shannon Information Content for Effective Radii
Albedo Scaled Albedo
Max Info
To Scale or Not to Scale?
Max Info
- Scaling comes from the chi square statistic formula
that determines the best fit, which is the minimum residual.
- Part one of the formula the absolute difference
between measured and modeled albedo, weighted towards shorter wavelengths where there is more information about optical depth.
- The second is the absolute difference between
scaled measured and modeled albedo, weighted towards higher wavelengths where there is more information about effective radii.
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GENRA Output for Case with No Systematic Error True(tau,re) = (40,15 micron)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20 40 60 80 100 pdf
Optical Depth
515 nm 745 nm 1015 nm 1240 nm 1625 nm 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 pdf
Effective Radius
515 nm 745 nm 1015 nm 1240 nm 1625 nm
Max Likelihood 40 Max Likelihood 15
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GENRA Output for Case with Systematic Error True(tau,re) = (40,15 micron)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20 40 60 80 100 pdf
Optical Depth
515 nm 745 nm 1015 nm 1240 nm 1625 nm 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 pdf
Effective Radius
515 nm 745 nm 1015 nm 1240 nm 1625 nm
Max Likelihood 40 Max Likelihood 15
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20 40 60 80 100 pdf
Optical Depth
515 nm 745 nm 1015 nm 1240 nm 1625 nm
Max Likelihood 37
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 pdf
Effective Radius
515 nm 745 nm 1015 nm 1240 nm 1625 nm
Max Likelihood 15 Cloud Only Cloud with Overlying Aerosol Layer
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GENRA Output for Case with Systematic Error Using Different Retrieval Wavelengths True(tau,re) = (40,15 micron)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 50 100 pdf
Optical Depth
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 pdf
Effective Radius
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 50 100 pdf
Optical Depth
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 pdf
Effective Radius
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20 40 60 80 100 pdf
Optical Depth
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 pdf
Effective Radius
35 15 15 38 37 15 2-wavelength retrieval (e.g. satellite) 5-wavelength retrieval (e.g. SSFR) 24-wavelength retrieval (e.g. future retrievals)
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Characterizing the Entire Look up Table
3000 (tau,re) pairs were characterized in 4 hours on the Cynewulf cluster using GENRA.
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Future Research
- Look at other factors that may cause bias in
cloud retrievals such as:
– Absorbing aerosol mixed within a cloud – 3D cloud effects
- GENRA can be used for characterizing any
retrieval statistic and to compare retrievals from different instruments.
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