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The Accuracy of Retrieved Cloud Properties Impacted by Systematic Error By Leandra Merola Mentor – Odele Coddington
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 variability, is needed for understanding and quantifying the role of clouds in climate variability and for modeling clouds and their effects in climate and weather models.” (Vukicevic, et al. 2010)
Most importantly clouds effect our climate.
Satellite
DownwellingRadiance
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.)
radiative
transfer model
Cloud: Mie theory (, g, )
ri, i
Absorbing Gases:
O3, O2, H2O
surface spectral albedo
Top of atmosphere
irradianceatmospheric 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 μmDashed lines = effective radii of 30 μmOptical thickness increases from blue to red
Wavelength (nm)
0.0
0
.2
0.4
0.6
0
.8
1.0
Alb
edo
400 600 800 1000 1200 1400 16006
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
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 ThicknessSolid Line
Increased Effective RadiiDashed Line
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.
Shannon Information Content
• A formal mathematical theory to quantify the information gained by making a measurement.
• Maximum information content is dependent on resolution of the look up table.
• It’s a scalar.
0
1
2
3
4
5
6
7
0 500 1000 1500 2000
Wavelength (nm)
Shannon Information Content forOptical Thickness
Albedo
Scaled Albedo
0
1
2
3
4
5
6
7
0 500 1000 1500 2000
Wavelength (nm)
Shannon Information Content forEffective 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.
GENRA Output for Case with No Systematic ErrorTrue(tau,re) = (40,15 micron)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100
Optical Depth
515 nm
745 nm
1015 nm
1240 nm
1625 nm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30
Effective Radius
515 nm
745 nm
1015 nm
1240 nm
1625 nmMax Likelihood40
Max Likelihood15
GENRA Output for Case with Systematic ErrorTrue(tau,re) = (40,15 micron)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100
Optical Depth
515 nm
745 nm
1015 nm
1240 nm
1625 nm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30
Effective Radius
515 nm
745 nm
1015 nm
1240 nm
1625 nm
Max Likelihood40
Max Likelihood15
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100
Optical Depth
515 nm
745 nm
1015 nm
1240 nm
1625 nm
Max Likelihood37
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30
Effective Radius
515 nm
745 nm
1015 nm
1240 nm
1625 nm
Max Likelihood15
Cloud Only Cloud with Overlying Aerosol Layer
GENRA Output for Case with Systematic Error Using Different Retrieval Wavelengths
True(tau,re) = (40,15 micron)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100
Optical Depth
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30
Effective Radius
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100
Optical Depth
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30
Effective Radius
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100
Optical Depth
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30
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)
Characterizing the Entire Look up Table
3000 (tau,re) pairs were characterized in 4 hours on the Cynewulf cluster using GENRA.
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.
