outline why use atsr? why variational analysis? forward model examples validation level 3 products
DESCRIPTION
Cloud parameters estimated by variational analysis of visible and infrared measurements from ATSR-2. Caroline Poulsen, Richard Siddans, Barry Latter and Brian Kerridge, Chris Mutlow, Sam Dean 2 , Don Grainger 2 , Gareth Thomas 2 , Graham Ewen 2 and Phil Watts 1 - PowerPoint PPT PresentationTRANSCRIPT
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Caroline PoulsenATSR-2 Group
Cloud parameters estimated by variational analysis of visible and
infrared measurements from ATSR-2Caroline Poulsen, Richard Siddans, Barry Latter and
Brian Kerridge, Chris Mutlow, Sam Dean2, Don Grainger2, Gareth Thomas2, Graham Ewen2 and
Phil Watts1
Space Science and Technology DepartmentRutherford Appleton Laboratory
UK1. Now at EUMETSAT2. Oxford University
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OutlineWhy use ATSR?
Why Variational Analysis?Forward Model
ExamplesValidation
Level 3 productsFuture
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ATSR Channels
ATSR2/AATSR • 0.55um• 0.67um• 0.87um• 1.6um• 3.7um• 11um• 12um
Cloud Parameters Retrieved• Cloud top pressure/height• Cloud fraction• Cloud optical depth• Cloud effective radius• Cloud phase
Auxillary information• ECMWF T and q profiles• MODIS surface albedo
Aerosol Parameters Retrieved• Aerosol optical depth• Aerosol effective radius
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Comparing measurements with calculations: Ice, water and mixed
phase
waterice
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Why use Optimal Estimation?
• Basic principle is to maximise the accuracy the retrieved cloud parameters based on the measurements and any ‘apriori’
• Allows us to characterise the error in each cloud parameter under the assumption of a reasonably plane parallel cloud model
• It’s a very flexible approach that enables us to utilise any prior information, for example on cloud fraction. All the clear sky atmospheric effects can be derived from NWP profiles.
• Allows us to utilise ALL the information in the measurements for each channel contributes to a greater or lesser extent to the retrieval of individual cloud parameters.
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Forward Model
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Ice clouds: complex particlesCurrently uses a combination of geometric optics (ray tracing); for large ice crystals and a T-matrix (ray tracing); method for small crystals.
Plates
Columns
Rosettes
Aggregates
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Water clouds: spherical drops
Mie theory: solution of electromagnetic equations on dielectric sphere
Size distribution
10 m drop, 0.87 m wavelength
Since real time calculations of cloud radiative properties are too slow calculations are made once DISORT (plane-parallel) model and incorporating rayleigh scattering and stored in easily accessible Look up Tables.
Look up Tables
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Tbc
Tac(e.g. MODTRAN)
Cloud + Atmosphere/surface
• Separate solar and ‘thermal’ models• Both embed cloud with precalculated radiative
properties (LUTs) in clear atmosphere
re pc (f)Solar model
Rs
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TacFrom e.g. RTTOV
re pc (f)Thermal model
Transmitted
Rbc
Cloud emitted
B(T(pc))
Reflected
Rdown
Atmosphereemitted
Rup
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Inversion: Optimal estimation
Guess xo
Calculate measurements y(xn)
Adjust (minimise J) x = - J’/J’’ (Newton’s Method)
Stop! J < 0.1 or n>10
Compare J = [ym-y(xn)] Sy-1 [ym-y(xn)]T
a priori xb
+ [xn-xb] Sx-1 [xn-xb]T
= 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)
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Cost Function
Compare J = [ym-y(xn)] Sy-1 [ym-y(xn)]T
+ [xn-xb] Sx-1 [xn-xb]T
J = [ym-y(xn)] Sy-1 [ym-y(xn)]T
Where ym are the radiances, Sy the measurement error covariance and y(xn) the cloud parameters modelled into radiance space. + [xn-xb] Sx
-1 [xn-xb]T
Where Xb is the apriori and Sx the apriori covariance.
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Inversion: Optimal estimation
Guess xo
Calculate measurements y(xn)
Adjust (minimise J) x = - J’/J’’ (Newton’s Method)
Stop! J < 0.1 or n>10
Compare J = [ym-y(xn)] Sy-1 [ym-y(xn)]T
a priori xb
+ [xn-xb] Sx-1 [xn-xb]T
= 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)
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Minimising J: optically thick cloud
xoxsolution
-No a priori,-0.55, 1.6 m channels
- , Re only
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Retrieved Cloud Parameters
Optical depth
Effective radiusFraction
Cloud top pressure
False colour
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Error Analysis and Quality Control
Cost
Ssolution = J’’ solution = (Sx-1 + KT.Sy
-1K)-1
Error Cloud top pressure
False colour
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Validation Activities
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Re validation against MRF FSSP probe
Optical depth (scaled to fit)
Effective radius
Hercules - ERS-2Coincidence
FSSP
ATSR
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Validation at SGP 20th Oct. 1997AATSR overpass17:26Microwave radiometer
SGP ARM data courtesy of Roger Marchand.
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Case study 20th October 1997
Parameter ATSR-2 SGP
Optical depth 37.3 35.8
Effective radius 8.8 8.9
Liquid water path 244.0 209.8
Effective radius LWPOptical Depth
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SGP validation
Mean: -0.08Stdev: 1.21
Liquid water path is calculated using the technique of Frisch et al, J. Atmos Sci. 1995, the technique is only valid for non-raining, water clouds.
Optical depth calculated using Han et alJ. Atmos Sci.,1995. Errors shown are the standard deviation of the matches used.
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Validation of CTH
Chilbolton 94GHz Galileo Radar
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Comparison with ISCCP data
ATSR-2 May 1999 Optical depth ISCCP Optical depth May 1999
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Level 3 products
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Cloud top pressure
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Cloud optical depth
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Cloud effective radius
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Cloud fraction
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Summary and plans
• 6 years of ATSR-2 data processed at 3x3km resolution and a variety of level 3 products
• Version 2 to begin soon with many improvements
• Potential is there to use information from other satellites
• Dual view tomographic cloud retrieval
• Extension to AATSR- long time series
• More validation, comparison with met. Office models
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The ATSR cloud and aerosol algorithm was developed under funding from the following projects
The end
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QC: Summary
• Model adequate (J<1)– Expected errors, S
• parameter dependent• state dependent• Information for
assimilation
• (Discussed today• Not discussed)
• Model inadequate (J>1)– A priori out of range
• rogue values– Measurements out of range
• calibration errors• rogue values
– Model out of range• multi-layer cloud• shadows• incorrect ice crystals• incorrect surface reflectance• incorrect statistical
constraints
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Retrieval (inversion): required steps
• “Forward modelling”:
– Optical properties of average particle in ‘single scattering’ event
– Optical properties of a cloud of particles: multiple scattering
– Interaction of cloud radiative processes with atmosphere and surface
– y = y(x)
• “Inverse modelling”:– x = ? (y)
– Guess cloud conditions (x)
– Calculate radiances y(x)
– Compare to measurements
– Change cloud conditionsStop!
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Re validation against MRF FSSP probe
Optical depth (scaled to fit)
Effective radius
Hercules - ERS-2Coincidence
FSSP
ATSR
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Monthly Averaged Results
May 1999 log10Optical depth May 1999 effective radius
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Water clouds: spherical drops
Single particle
Mie theory: solution of electromagnetic equations on dielectric sphere
Size distribution
10 m drop, 0.87 m wavelength
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Cloud top pressure