Transcript pres_meris_aatsr2005_v2

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 Dean2, Don
Grainger2, Gareth Thomas2, Graham Ewen2 and
Phil Watts1
Space Science and Technology Department
Rutherford Appleton Laboratory
UK
1. Now at EUMETSAT
2. Oxford University
Caroline Poulsen
ATSR-2 Group
Outline
Why use ATSR?
Why Variational Analysis?
Forward Model
Examples
Validation
Level 3 products
Future
ATSR Channels
ATSR2/AATSR
• 0.55um
• 0.67um
• 0.87um
• 1.6um
• 3.7um
• 11um
• 12um
Auxillary information
• ECMWF T and q profiles
• MODIS surface albedo
Cloud Parameters Retrieved
• Cloud top pressure/height
• Cloud fraction
• Cloud optical depth
• Cloud effective radius
• Cloud phase
Aerosol Parameters Retrieved
• Aerosol optical depth
• Aerosol effective radius
Comparing measurements with
calculations: Ice, water and mixed
phase
ice
water
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.
Forward Model
Ice clouds: complex particles
Currently uses a combination of geometric optics (ray tracing); for large ice crystals and a Tmatrix (ray tracing); method for small crystals.
Plates
Columns
Rosettes
Aggregates
Water clouds: spherical drops
Mie theory: solution of electromagnetic equations on dielectric sphere
10 mm drop, 0.87 mm wavelength
Size distribution
Look up Tables
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.
Cloud + Atmosphere/surface
• Separate solar and ‘thermal’ models
• Both embed cloud with precalculated radiative
properties (LUTs) in clear atmosphere
Tac(e.g. MODTRAN)
t re pc (f)
Solar model
Rs
Tbc
Cloud emitted
From e.g. RTTOV
Transmitted
Reflected
Atmosphere
emitted
Rup
Tac
Rdown
B(T(pc))e
t re pc (f)
Thermal model
Rbc
Inversion: Optimal estimation
Guess
a priori
Calculate measurements
Compare
xo
xb
y(xn)
J = [ym-y(xn)] Sy-1 [ym-y(xn)]T
+ [xn-xb] Sx-1 [xn-xb]T
Adjust (minimise J)
dx = - J’/J’’
Stop!
dJ < 0.1 or
(Newton’s Method)
n>10
= 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)
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.
Inversion: Optimal estimation
Guess
a priori
Calculate measurements
Compare
xo
xb
y(xn)
J = [ym-y(xn)] Sy-1 [ym-y(xn)]T
+ [xn-xb] Sx-1 [xn-xb]T
Adjust (minimise J)
dx = - J’/J’’
Stop!
dJ < 0.1 or
(Newton’s Method)
n>10
= 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)
Minimising J: optically thick
cloud
-No a priori,
-0.55, 1.6 mm channels
- t, Re only
xo
xsolution
Retrieved Cloud Parameters
Effective radius
Fraction
False colour
Cloud top pressure
Optical depth
Error Analysis and Quality Control
False colour
Error Cloud top pressure
Cost
Ssolution = J’’ solution = (Sx-1 + KT.Sy-1K)-1
Validation Activities
Re validation against MRF FSSP probe
Optical depth (scaled to fit)
Effective radius
ATSR
FSSP
Hercules - ERS-2
Coincidence
Validation at SGP 20th Oct. 1997
Microwave radiometer
SGP ARM data courtesy of Roger Marchand.
AATSR overpass17:26
Case study 20th October 1997
Effective radius
LWP
Optical Depth
Parameter
ATSR-2
SGP
Optical depth
37.3
35.8
Effective radius
8.8
8.9
Liquid water path
244.0
209.8
SGP validation
Mean: -0.08
Stdev: 1.21
Liquid water path is calculated using the
technique of Frisch et al, J. Atmos Sci.
1995, the technique is only valid for nonraining, water clouds.
Optical depth calculated using Han et al
J. Atmos Sci.,1995. Errors shown are the
standard deviation of the matches used.
Validation of CTH
Chilbolton
94GHz
Galileo Radar
Comparison with ISCCP data
ATSR-2 May 1999 Optical depth
ISCCP Optical depth May 1999
Level 3 products
Cloud top pressure
Cloud optical depth
Cloud effective radius
Cloud fraction
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
The end
The ATSR cloud and aerosol algorithm was developed
under funding from the following projects
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
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 conditions
Stop!
Re validation against MRF FSSP probe
Optical depth (scaled to fit)
Effective radius
ATSR
FSSP
Hercules - ERS-2
Coincidence
Monthly Averaged Results
May 1999 log10Optical depth
May 1999 effective radius
Water clouds: spherical drops
Mie theory: solution of electromagnetic equations on dielectric sphere
10 mm drop, 0.87 mm wavelength
Single particle
Size distribution
Cloud top pressure