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 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.

Upwelling Radiance Satellite Aerosols Downwelling SSFR (Solar Spectral Flux Radiometer) (shortwave range Radiance 300 – 1700 nm, spectral resolution 8 – 12 nm) Irradiance – is the amount of radiation Albedo – a measure of the emitted from an object integrated over reflectivity of an object. (upwelling the hemisphere. It is measured in irradiance/downwelling irradiance.) W m -2 nm -1 .

Inputs for Top of atmosphere atmospheric conditions irradiance P, T, RH Forward Model surface spectral albedo Cloud: radiative Mie theory (  , g,  ) transfer model r i ,  i Modeled albedo – Solid lines = effective radii of 1 μ m Absorbing Gases: Dashed lines = effective radii of 30 tables of spectral O 3 , O 2 , H 2 O μ m irradiance for Optical thickness increases from r i ,  i pairs blue to red 5

Retrieval Method This method is know as inverse problem solving. Modeled Data Measured Data SSFR radiative (Solar Spectral transfer model 5 retrieval wavelengths Flux Radiometer) tables of spectral measured spectral 0.0 0.2 0.4 0.6 0.8 1.0 irradiance irradiance Albedo best fit r i ,  i 400 600 800 1000 1200 1400 1600 6 Wavelength (nm)

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 1 st Indirect Effect – when aerosols physically change cloud microphysical properties and therefore change its albedo. Increased Effective Radii Dashed Line Increased Optical Thickness Solid 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.

To Scale or Not to Scale? • 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. Shannon Information Content for Shannon Information Content for Optical Thickness Effective Radii 7 7 Max Info 6 6 5 5 Max Info 4 4 Albedo 3 3 Albedo Scaled Albedo 2 2 Scaled Albedo 1 1 0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 Wavelength (nm) Wavelength (nm)

GENRA Output for Case with No Systematic Error True(tau,re) = (40,15 micron) 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 515 nm 515 nm 0.5 0.5 pdf pdf 745 nm 745 nm Max Likelihood 1015 nm 1015 nm 0.4 0.4 15 1240 nm 1240 nm Max Likelihood 1625 nm 1625 nm 0.3 0.3 40 0.2 0.2 0.1 0.1 0 0 0 20 40 60 80 100 0 10 20 30 Optical Depth Effective Radius

GENRA Output for Case with Systematic Error True(tau,re) = (40,15 micron) 1 1 Cloud Only Cloud with Overlying Aerosol Layer 0.9 0.9 0.8 0.8 0.7 0.7 Max Likelihood Max Likelihood 0.6 0.6 515 nm 515 nm 0.5 0.5 pdf 40 pdf 37 745 nm 745 nm 0.4 0.4 1015 nm 1015 nm 0.3 0.3 0.2 0.2 1240 nm 1240 nm 0.1 0.1 1625 nm 1625 nm 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Optical Depth Optical Depth 1 1 0.9 0.9 0.8 0.8 Max Likelihood Max Likelihood 0.7 0.7 0.6 0.6 15 515 nm 15 515 nm 0.5 pdf pdf 0.5 745 nm 745 nm 0.4 0.4 1015 nm 1015 nm 0.3 0.3 0.2 0.2 1240 nm 1240 nm 0.1 0.1 1625 nm 1625 nm 0 0 0 10 20 30 0 10 20 30 Effective Radius Effective Radius

GENRA Output for Case with Systematic Error Using Different Retrieval Wavelengths True(tau,re) = (40,15 micron) 1 1 1 2-wavelength retrieval 5-wavelength retrieval 24-wavelength retrieval 0.9 0.9 0.9 (e.g. satellite) (e.g. SSFR) 0.8 0.8 0.8 (e.g. future retrievals) 0.7 0.7 0.7 0.6 0.6 0.6 0.5 0.5 0.5 pdf pdf pdf 38 0.4 0.4 0.4 0.3 0.3 0.3 37 35 0.2 0.2 0.2 0.1 0.1 0.1 0 0 0 0 50 100 0 50 100 0 20 40 60 80 100 Optical Depth Optical Depth Optical Depth 1 1 1 0.9 0.9 0.9 0.8 0.8 0.8 15 0.7 0.7 0.7 0.6 0.6 0.6 0.5 0.5 pdf pdf pdf 0.5 0.4 0.4 0.4 15 15 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0 0 0 0 10 20 30 0 10 20 30 0 10 20 30 Effective Radius Effective Radius Effective Radius

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.

Questions?