Cloud parameters estimated by variational analysis of visible and infrared measurements from ATSR-2

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Nov 3, 2013 (3 years and 9 months ago)

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Caroline Poulsen

ATSR
-
2 Group

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

Space Science and Technology Department

Rutherford Appleton Laboratory

UK

1.
Now at EUMETSAT

2.
Oxford University



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

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

Comparing measurements with
calculations: Ice, water and mixed
phase

water

ice

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 T
-
matrix (ray tracing); method for small crystals.


Plates

Columns

Rosettes

Aggregates

Water clouds: spherical drops

Mie theory: solution of electromagnetic equations on dielectric sphere

Size distribution

10
m
m drop, 0.87
m
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


T
bc


T
ac
(e.g. MODTRAN)

Cloud + Atmosphere/surface



Separate solar and ‘thermal’ models


Both embed cloud with precalculated radiative
properties (LUTs) in clear atmosphere


t

r
e

p
c

(f)

Solar model

R
s





T
ac

From e.g. RTTOV


t

r
e

p
c

(f)

Thermal model

Transmitted

R
bc

Cloud emitted

B(T(p
c
))
e

Reflected

R
down

Atmosphere

emitted

R
up

Inversion: Optimal estimation

Guess

x
o

Calculate measurements

y(x
n
)

Adjust
(minimise J)

d
x =
-

J’/J’’
(Newton’s Method)

Stop!

d
J < 0.1 or n>10

Compare

J = [y
m
-
y(x
n
)] S
y
-
1
[y
m
-
y(x
n
)]
T

a priori

x
b


+ [x
n
-
x
b
] S
x
-
1
[x
n
-
x
b
]
T

= 1D
-
Variational analysis. Same principles > 3D, 4D Var (assimilation)

Cost Function

Compare

J = [y
m
-
y(x
n
)] S
y
-
1
[y
m
-
y(x
n
)]
T


+ [x
n
-
x
b
] S
x
-
1
[x
n
-
x
b
]
T

J = [y
m
-
y(x
n
)] S
y
-
1
[y
m
-
y(x
n
)]
T


Where y
m

are the radiances, S
y

the measurement error
covariance and y(x
n
) the cloud parameters modelled into
radiance space.


+ [x
n
-
x
b
] S
x
-
1
[x
n
-
x
b
]
T

Where X
b

is the apriori and S
x

the apriori covariance.


Inversion: Optimal estimation

Guess

x
o

Calculate measurements

y(x
n
)

Adjust
(minimise J)

d
x =
-

J’/J’’
(Newton’s Method)

Stop!

d
J < 0.1 or n>10

Compare

J = [y
m
-
y(x
n
)] S
y
-
1
[y
m
-
y(x
n
)]
T

a priori

x
b


+ [x
n
-
x
b
] S
x
-
1
[x
n
-
x
b
]
T

= 1D
-
Variational analysis. Same principles > 3D, 4D Var (assimilation)

Minimising J: optically thick
cloud

x
o

x
solution

-
No
a priori,

-
0.55, 1.6

m
m

channels

-

t
, R
e

only

Retrieved Cloud Parameters

Optical depth

Effective radius

Fraction

Cloud top pressure

False colour

Error Analysis and Quality Control

Cost

S
solution

= J’’
solution

= (S
x
-
1

+ K
T
.S
y
-
1
K)
-
1

Error Cloud top pressure

False colour

Validation Activities

R
e

validation against MRF FSSP probe

Optical depth (scaled to fit)

Effective radius

Hercules
-

ERS
-
2

Coincidence

FSSP

ATSR

Validation at SGP 20
th

Oct. 1997

AATSR overpass17:26

Microwave radiometer

SGP ARM data courtesy of Roger Marchand
.

Case study 20
th

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

LWP

Optical Depth

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 non
-
raining, 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 ATSR cloud and aerosol algorithm was developed
under funding from the following projects

The end

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!

R
e

validation against MRF FSSP probe

Optical depth (scaled to fit)

Effective radius

Hercules
-

ERS
-
2

Coincidence

FSSP

ATSR

Monthly Averaged Results

May 1999 log
10
Optical depth

May 1999 effective radius

Water clouds: spherical drops

Single particle

Mie theory: solution of electromagnetic equations on dielectric sphere

Size distribution

10
m
m drop, 0.87
m
m wavelength

Cloud top pressure