Transcript Document

Grand Unified Algorithms
How to retrieve radiatively consistent profiles
of clouds, precipitation and aerosol from radar,
lidar and radiometers
…and evaluating models
Robin Hogan, University of Reading
Thanks to Julien Delanoe, Nicola Pounder, Nicky Chalmers,
Howard Barker
Spaceborne radar, lidar and radiometers
EarthCare
The A-Train
– NASA
– 700-km orbit
– CloudSat 94-GHz radar (launch 2006)
– Calipso 532/1064-nm depol. lidar
– MODIS multi-wavelength radiometer
– CERES broad-band radiometer
– AMSR-E microwave radiometer
2013
2019
2018
2017
2016
2015
EarthCARE: launch 2012
2014
– ESA+JAXA
– 400-km orbit: more sensitive
– 94-GHz Doppler radar
– 355-nm HSRL/depol. lidar
– Multispectral imager
– Broad-band radiometer
– Heart-warming name
What do CloudSat and Calipso see?
Cloudsat radar
CALIPSO lidar
•
Radar: ~D6,
detects whole
profile, surface
echo provides
integral constraint
•
Lidar: ~D2, more
sensitive to thin
cirrus and liquid
but attenuated
Radar-lidar ratio
provides size D
•
Target classification
Insects
Aerosol
Rain
Supercooled liquid cloud
Warm liquid cloud
Ice and supercooled liquid
Ice
Clear
No ice/rain but possibly liquid
Ground
Delanoe and Hogan (2008, 2010)
How do we evaluate models?
• Traditional approach
– Compare retrieved cloud
products to model variables
– But these are the variables we
want to know!
– Simple forward modeling can
get right answer for wrong
reasons, e.g. right Z with wrong
IWC and re
– Can extract “hidden” info, e.g.
re from radar-lidar synergy
– Can provide radiative
verification of each retrieved
profile
– But certainly more difficult!
• New approach
– Forward-model observations
and evaluate in obs. space
– IceSat lidar: Chepfer et al.
(2007), Wilkinson et al. (2008)
– CloudSat: Bodas et al. (2008)
– CloudSat & Calipso: IPCC/COSP
– Much easier!
– Avoids contamination by apriori information
– Avoids competition between
retrievals
Overview
• Justification for and design of unified algorithm
• Ice clouds
– Evaluation against CERES
– Evaluation of ECMWF and Met Office models
• Liquid clouds
– Fast multiple scattering model for exploitation of lidar signal
– Extinction profile from multiple field-of-view lidar
– Can we estimate liquid cloud base from the Calipso lidar?
• First results from unified algorithm applied to A-Train
– Simultaneous ice, liquid and rain retrievals
• Outlook
– 3D radiatively consistent scene retrieval
“Grand Unified Algorithm”
• Combine all measurements available (radar, lidar, radiometers)
– Forms the observation vector y
• Retrieve cloud, precipitation and aerosol properties simultaneously
– Ensures integral measurements can be used when affected by more
than one species (e.g. radiances affected by ice and liquid clouds)
– Forms the state vector x
• Variational approach
– This is the proper way to do it!
– Use forward model H(x) to predict observations from state vector
– Report solution error covariance matrix & averaging kernel
• Completely flexible
– Applicable to ground-based, airborne and space-borne platforms
• Behaviour should tend towards existing two-instrument synergy algos
– Radar+lidar for ice clouds: Donovan et al. (2001), Tinel et al. (2005)
– CloudSat+MODIS for liquid clouds: Austin & Stephens (2001)
– Calipso+MODIS for aerosol: Kaufman et al. (2003)
– CloudSat surface return for rainfall: L’Ecuyer & Stephens (2002)
The cost function
• The essence of the method is to find the state vector x that
minimizes a cost function:
Some elements of x
are constrained by a
prior estimate
The forward model H(x)
predicts the observations
from the state vector x
1 m  yi  H i (x) 1 n xi  bi  1 n 1
2


J 



x

2
x

x

 i i 1 i i1
2 i 1
 yi2
2 i 1  bi2
2 i 2
2
1 T 1
 δy R δy
2
2
1 T 1
1 T
 δx B δx  x Tx
2
2
Each observation yi is
weighted by the inverse of
its error variance
This term can be used
to penalize curvature in
the retrieved profile
1. New ray of data: define state vector x
Use classification to specify variables describing each species at each gate
Ice: extinction coefficient , N0’, lidar extinction-to-backscatter ratio
Liquid: extinction coefficient and number concentration
Rain: rain rate, drop diameter and melting ice
Aerosol: extinction coefficient, particle size and lidar ratio
2. Forward model
2a. Radar model
Including surface return
and multiple scattering
2b. Lidar model
Including HSRL channels
and multiple scattering
3. Compare to observations
Check for convergence
2c. Radiance model
Solar and IR channels
Unified
retrieval
Ingredients developed
Work in progress
4. Iteration method
Derive a new state vector
Adjoint of full forward model
Quasi-Newton or GaussNewton scheme
Not converged
Converged
5. Calculate retrieval error
Error covariances and averaging kernel
Proceed to next ray of data
Unified retrieval: Forward model
• From state vector x to forward modelled observations H(x)...
Ice & snow
Liquid cloud
Rain
Aerosol
x
Gradient of cost function (vector)
xJ=HTR-1[y–H(x)]
Lookup tables to obtain profiles of extinction, scattering
& backscatter coefficients, asymmetry factor
Ice/radar
Ice/lidar
Ice/radiometer
Liquid/radar
Liquid/lidar
Liquid/radiometer
Rain/radar
Rain/lidar
Rain/radiometer
Aerosol/lidar
Aerosol/radiometer
Vector-matrix multiplications: around
the same cost as the original forward
operations
Sum the contributions from each constituent
Radar scattering
profile
Lidar scattering
profile
Radiative transfer models
Radar forward
modelled obs
Lidar forward
modelled obs
Radiometer
scattering profile
H(x)
Radiometer fwd
modelled obs
Adjoint of radar
model (vector)
Adjoint of lidar
model (vector)
Adjoint of radiometer
model
Adjoint of radiative transfer models
yJ=R-1[y–H(x)]
Ice cloud: non-variational retrieval
Observations
State
variables
Aircraftsimulated
profiles with
noise (from
Hogan et al.
2006)
Retrieval is
accurate
but not
perfectly
stable
where lidar
loses signal
Derived
variables
• Donovan et al. (2000) algorithm can only be applied where both lidar
and radar have signal
Delanoe and Hogan (2008)
Variational radar/lidar retrieval
Observations
Lidar noise
matched by
retrieval
State
variables
Derived
variables
Noise
feeds
through to
other
variables
• Noise in lidar backscatter feeds through to retrieved extinction
Delanoe and Hogan (2008)
…add smoothness constraint
Observations
State
variables
Derived
variables
Retrieval
reverts to
a-priori N0
Extinction
and IWC
too low in
radar-only
region
• Smoothness constraint: add a term to cost function to penalize
curvature in the solution (J’ =  Si d2ai/dz2)
Delanoe and Hogan (2008)
…add a-priori error correlation
Observations
State
variables
Derived
variables
Vertical
correlation
of error in
N0
Extinction
and IWC
now more
accurate
• Use B (the a priori error covariance matrix) to smooth the N0
information in the vertical
Delanoe and Hogan (2008)
Lidar observations
Example ice
cloud retrievals
Delanoe and Hogan (2010)
Lidar forward model
Visible extinction
Radar observations
Ice water content
Radar forward model
Effective radius
Evaluation using CERES TOA fluxes
• Radar-lidar retrieved profiles containing only ice used
with Edwards-Slingo radiation code to predict CERES fluxes
– Note: MODIS IR radiances can be used but aren’t used here
• Small biases but large random shortwave error: 3D effects?
Shortwave
Bias 4 W m-2, RMSE 71 W m-2
Longwave
Bias 0.3 W m-2, RMSE 14 W m-2
Chalmers (2011)
CERES versus a radar-only retrieval
• How does this compare with radar-only empirical IWC(Z, T)
retrieval of Hogan et al. (2006) using effective radius
parameterization from Kristjansson et al. (1999)?
Shortwave
Bias 48 W m-2, RMSE 110 W m-2
Longwave
Bias –10 W m-2, RMSE 47 W m-2
Bias 10 W m-2
RMS 47 W m-2
Chalmers (2011)
How important is lidar?
• Remove lidar-only pixels from radar-lidar retrieval
• Change to fluxes is only ~5 W m-2 but lidar still acts to
improve retrieval in radar-lidar region of the cloud
Shortwave
Bias –5 W m-2, RMSE 17 W m-2
Longwave
Bias 4 W m-2, RMSE 9 W m-2
Chalmers (2011)
TOA fluxes don’t tell the whole story...
• In terms of net atmospheric heating rate in the tropics, cloud
radiative effect is underestimated by a factor of two above 12 km if
clouds detected by lidar alone are ignored
A-Train
versus
models
• Ice water
content
• 14 July 2006
• Half an orbit
• 150°
longitude at
equator
Delanoe et al.
(2011)
Evaluation of gridbox-mean ice water content
In-cloud mean ice water content
• Both models lack high thin cirrus
• ECMWF lacks high IWC values; using this work, ECMWF have
developed a new prognostic snow scheme that performs better
• Met Office has too narrow a distribution of in-cloud IWC
Why you must compare the full PDF
• Consider Cloudnet evaluation of IWC in models
– DWD model drastically underestimates mean IWC
Illingworth et al. (2007)
3-7 km
– But PDF is within observational range in all but the highest bin!
– 10% of cloud volume contains 75% of the ice in observations
– But all parts of PDF can be radiatively significant
– Moreover, top 10% of IWC retrievals are the least reliable
Liquid clouds
• Stratocumulus clouds are tricky!
– Lidar beam is rapidly attenuated
– Radar return usually dominated by drizzle (Fox & Illingworth 1997)
• Can we piece together their properties by forward modeling the
following?
– Multiply scattered lidar signal: information on optical depth and
extinction profile
– Surface radar return: path integrated attenuation proportional to liquid
water path (Hawkness-Smith 2010)
– Solar radiances: optical depth and droplet size / number concentration
• We need:
– Fast lidar forward model incorporating multiple scattering
– Ability to use additional constraints, such as tendency for liquid water
content profile to be adiabatic, particularly near cloud base
CALIPSO
CloudSat
Examples of multiple scattering
LITE lidar (<r, footprint~1 km)
Stratocumulus
Apparent echo from
below the surface!
Surface echo
Intense thunderstorm
CloudSat radar (>r)
Time-dependent 2-stream approx.
•
Describe diffuse flux in terms of outgoing stream I+ and incoming stream I–, and
numerically integrate the following coupled PDEs:
Time derivative
Remove this and
we have the timeindependent twostream
approximation

1 I
I

 a 1 I    2 I   S 
1c t
r


1 I  I 

 a 1 I    2 I   S 
1c t
r
Spatial derivative
Transport of
radiation from
upstream
•



Loss by absorption
or scattering
Some of lost radiation
will enter the other
stream
Source
Scattering from
the quasi-direct
beam into each of
the streams
Gain by scattering
Radiation scattered
from the other
stream
These can be discretized quite simply in time and space (no implicit methods or
matrix inversion required)
Hogan and Battaglia (2008)
Fast multiple scattering forward model
Hogan and Battaglia (2008)
• New method uses the timedependent two-stream
approximation
• Agrees with Monte Carlo but
~107 times faster (~3 ms)
CloudSat-like example
CALIPSO-like example
Multiple field-of-view lidar retrieval
• To test multiple scattering model in a
retrieval, and its adjoint, consider a
multiple field-of-view lidar observing
a liquid cloud
• Wide fields of view provide
information deeper into the cloud
• The NASA airborne “THOR” lidar is an
example with 8 fields of view
• Simple retrieval implemented with
state vector consisting of profile of
extinction coefficient
Cloud top
lidar
10 m
100 m
600 m
Results for a sine profile
•
•
•
•
•
•
Simulated test
with 200-m
sinusoidal
structure in
extinction
With 1FOV, only
retrieve first 2
optical depths
With 3FOVs,
retrieve structure
down to 6 optical
depths
Beyond that the
information is
smeared out
Averaging kernel area: what fraction of retrieval from obs rather than prior?
Averaging kernel width: how much has true profile been smeared out?
Pounder et al. (2011)
Calipso?
• Can we use this approach
with a lidar with only one
field of view?
• Calipso has 90-m footprint
• Simulations indicate that
there is measurable
difference in apparent
backscatter profile up to
20-30 optical depths
(would be more for a
larger field-of-view)
• Perhaps can’t retrieve full
extinction profile, but at
least the optical depth and
the cloud boundaries?
Simulated profile
• Adiabatic cloud retrieved by lidar
using smoothness constraint
• Optical depth around 20
• Because lidar ratio is well
constrained in liquid clouds,
backscatter provides quite
accurate extinction (and hence
LWC) at cloud top
• Wide-angle multiple scattering
provides some optical depth
information
• But retrieved shape is wrong
• Cloud base too low
One-sided gradient constraint
Slingo et al. (1982)
• We have a good constraint on the gradient of LWC with height in
stratocumulus: adiabatic profile, particularly near cloud base
• Add an extra term to the cost function to penalize deviations from
gradient c:
2
 dLWC 
J grad    
 c
dz

i 
• This term is only used when the LWC gradient is greater than c, so
sub-adiabatic clouds can be retrieved
• Test with simulated lidar-only retrieval of liquid water cloud using
unified algorithm, and including simulated instrument noise
Gradient constraint
• With one-sided gradient
constraint observed by
backscatter-only lidar, much
better retrieved shape
• Cloud base about right
Clipped profile
• Multiply scattered signal plus
gradient constraint enables more
structured profiles to still be
retrieved reasonably well
• Still have the problem of multiple
liquid layers if the lower ones are
undetected by the lidar
Unphysical profile
• Gradient constraint ensures no
super-adiabatic profiles are
retrieved.
Optical depth from multiple scattering lidar
• Total optical depth
can be retrieved to
~30 optical depths
with 3 fields of view
• Limit is closer to 3
for one narrow
field-of-view lidar
• Useful optical depth
information from
one 100-mfootprint lidar (e.g.
Calipso)!
• Why not launch
multiple FOV lidar
in space?
Pounder et al. (2011)
Unified algorithm: progress
• Bringing the aspects of this talk together…
• Done:
– Functioning algorithm framework exists
– C++: object orientation allows code to be completely flexible:
observations can be added and removed without needing to keep track
of indices to matrices, so same code can be applied to different
observing systems
– Preliminary retrieval of ice, liquid, rain and aerosol
– Adjoint of radar and lidar forward models with multiple scattering and
HSRL/Raman support
– Interface to L-BFGS quasi-Newton algorithm in GNU Scientific Library
• In progress / future work:
– Estimate and report error in solution and averaging kernel
– Interface to radiance models
– Test on a range of ground-based, airborne and spaceborne instruments
– Will produce the standard EarthCARE cloud & precip synergy products
Observations vs forward models
• Lidar backscatter
– Radar and lidar backscatter are
successfully forward modelled (at
final iteration) in most situations
– Can also forward model Doppler
velocity (what EarthCARE would
see)
• Radar reflectivity factor
Three retrieved components
• Liquid water content
• Ice extinction coefficient
• Rain rate
Extension to three dimensions
• Synergistic retrievals under radar and lidar can be extended laterally
using imager, then evaluated radiatively using broadband fluxes
• Part of proposed product chain for EarthCARE satellite
Barker et al. (2011)
A: aerosol not included
B: surface temperature error
Outlook
• Evaluation of climate models in model space has distinct advantages
over comparisons in observation space
– Radiatively validated and consistent estimates of atmospheric state
– Can say not only in what way model clouds are wrong but what the
radiative consequence is
– Forward-model errors affect both approaches
• A “Grand Unified Algorithm” enables all measurements to be
combined to provide the optimum estimate of the atmospheric state
– Difficult and plenty remains to be done (e.g. precipitation – to be done
with Pavlos Kolias)
– Important to report errors (including those due to forward model
errors) and averaging kernel information
– Hope to have a fully flexible and freely available code that can be
applied to many different platforms and accommodate new observations
Three years of CloudSat and Calipso ice retrievals:
http://www.icare.univ-lille1.fr/projects/dardar/ (Google “dardar icare”)
Clouds in climate models
Vertically integrated cloud water (kg m-2)
• Via their interaction with solar and terrestrial radiation, clouds are
one of the greatest sources of uncertainty in climate forecasts
• But cloud water content in models varies by a factor of 10
• Need instrument with high vertical resolution…
But all models
tuned to give
about the
same top-ofatmosphere
radiation
14 global
models
(AMIP)
0.25
0.20
0.15
0.10
0.05
90N
80
60
40
20
0
Latitude
-20
-40
-60
-80 90S
The properties
of ice clouds
are particularly
uncertain
Stephens et al. (2002)
Vertical structure of liquid water content
•
•
Cloudnet: several years of retrievals from 3 European ground-based sites
Observations in grey (with range indicating uncertainty)
0-3 km
– Supercooled liquid water
content from seven forecast
models spans a factor of 20
•
– ECMWF has far too great an
occurrence of low LWC values
How do these models perform globally?
Illingworth, Hogan et al. (2007)
CloudSat and Calipso sensitivity
• In July 2006, cloud occurrence in the subzero
troposphere was 13.3%
• The fraction observed by radar was 65.9%
• The fraction observed by lidar was 65.0%
• The fraction observed by both was 31.0%
Minimization methods - in 1D
Quasi-Newton method (e.g. L-BFGS)
J
J/x
Gauss-Newton method
J
2J/x2
x1
x1
x2
x4 x3
x5
x
xx8 7 6
J/x
x
• Rolling a ball down a hill
– Intelligent choice of direction in
multi-dimensions helps
convergence
• Requires the gradient J/x
– A vector (efficient to store)
– Efficient to calculate using
adjoint method
• Used in data assimilation
x5
x4
x3
x2
x
• Requires the curvature 2J/x2
– A matrix
– More expensive to calculate
• Faster convergence
– Assume J is quadratic and
jump to the minimum
• Limited to smaller retrieval
problems
Minimizing the cost function
1
1
T
T
1
J  y  H (x) R y  H (x)  x  a  B 1 x  a 
2
2
Gradient of cost function (a vector)
x J  HT R 1y  H (x)  B1 x  a
Gauss-Newton method

x i 1  x i   J
–
–
–
–
2
x

1
x J
Rapid convergence (instant for linear
problems)
Get solution error covariance “for
free” at the end
Levenberg-Marquardt is a small
modification to ensure convergence
Need the Jacobian matrix H of every
forward model: can be expensive for
larger problems as forward model may
need to be rerun with each element of
the state vector perturbed
and 2nd derivative (the Hessian matrix):
2x J  HT R 1H  B1
Gradient Descent methods
xi 1  xi  Ax J
– Fast adjoint method to calculate xJ
means don’t need to calculate Jacobian
– Disadvantage: more iterations needed
since we don’t know curvature of J(x)
– Quasi-Newton method to get the search
direction (e.g. L-BFGS used by ECMWF):
builds up an approximate inverse Hessian
A for improved convergence
– Scales well for large x
– Poorer estimate of the error at the end
EarthCARE
• The ESA/JAXA “EarthCARE”
satellite is designed with
synergy in mind
• We are currently
developing synergy
algorithms for its
instrument specification
EarthCARE lidar
• High Spectral Resolution
capability enables direct
retrieval of extinction profile
Scattering models
• First part of a forward model is the scattering and fall-speed model
– Same methods typically used for all radiometer and lidar channels
– Radar and Doppler model uses another set of methods
Particle type
Aerosol
Radar (3.2 mm)
Aerosol not
detected by radar
Liquid droplets
Mie theory
Rain drops
T-matrix: Brandes
et al. (2002)
shapes
Ice cloud
T-matrix (Hogan et
particles
al. 2010)
Graupel and hail Mie theory
Melting ice
Wu & Wang
(1991)
Radar Doppler
Aerosol not
detected by radar
Beard (1976)
Beard (1976)
Thermal IR, Solar, UV
Mie theory, Highwood
refractive index
Mie theory
Mie theory
Westbrook &
Heymsfield
TBD
TBD
Baran (2004)
Mie theory
Mie theory
Unified algorithm: state variables
• Proposed list of retrieved variables held in the state vector x
State variable
Representation with height / constraint
A-priori
Ice clouds and snow
Visible extinction coefficient
One variable per pixel with smoothness constraint
None
Number conc. parameter
Cubic spline basis functions with vertical correlation
Temperature dependent
Lidar extinction-to-backscatter ratio
Cubic spline basis functions
20 sr
Riming factor
Likely a single value per profile
1
Liquid clouds
Liquid water content
One variable per pixel but with gradient constraint
None
Droplet number concentration
One value per liquid layer
Temperature dependent
Rain rate
Cubic spline basis functions with flatness constraint
None
Normalized number conc. Nw
One value per profile
Dependent on whether from
melting ice or coallescence
Melting-layer thickness scaling factor
One value per profile
1
Rain
Aerosols
Extinction coefficient
One variable per pixel with smoothness constraint
None
Lidar extinction-to-backscatter ratio
One value per aerosol layer identified
Climatological type
depending on region
Ice clouds follows
Delanoe & Hogan
(2008); Snow &
riming in
convective clouds
needs to be added
Liquid clouds
currently being
tackled
Basic rain to be
added shortly; Full
representation later
Basic aerosols to
be added shortly;
Full representation
via collaboration?
Radiative transfer forward models
• Infrared radiances
– Delanoe and Hogan (2008) model
– Currently testing RTTOV (widely used, can do microwave, has adjoint)
• Solar radiances
– Currently testing LIDORT
• Radar and lidar
– Simplest model is single scattering with attenuation: b’=b exp(-2d)
– Problem from space is multiple scattering: contains extra information on
cloud properties (particularly optical depth) but no-one has previously
been able to rigorously make use of data subject to pulse stretching
– Use combination of fast “Photon Variance-Covariance” method and
“Time-Dependent Two-Stream” methods
– Adjoints for these models recently coded
– Forward model for lidar depolarization is in progress
Radiative transfer forward models
•
Computational cost can scale with number of points describing vertical profile
N; we can cope with an N2 dependence but not N3
Radar/lidar model
Applications
Speed
Jacobian
Adjoint
Single scattering: b’=b exp(-2t)
Radar & lidar, no multiple scattering
N
N2
N
Platt’s approximation b’=b exp(-2ht)
Lidar, ice only, crude multiple scattering
N
N2
N
Photon Variance-Covariance (PVC)
method (Hogan 2006, 2008)
Lidar, ice only, small-angle multiple
scattering
N or N2
N2
N
Time-Dependent Two-Stream (TDTS)
method (Hogan and Battaglia 2008)
Lidar & radar, wide-angle multiple scattering
N2
N3
N2
Depolarization capability for TDTS
Lidar & radar depol with multiple scattering
N2
•
•
•
•
N2
Lidar uses PVC+TDTS (N2), radar uses single-scattering+TDTS (N2)
Jacobian of TDTS is too expensive: N3
We have recently coded adjoint of multiple scattering models
Future work: depolarization forward model with multiple scattering
Radiometer model
Applications
RTTOV (used at ECMWF & Met Office)
Infrared and microwave radiances
N
Two-stream source function technique
(e.g. Delanoe & Hogan 2008)
Infrared radiances
N
N2
LIDORT
Solar radiances
N
N2
•
•
Speed
Jacobian
Adjoint
N
Infrared will probably use RTTOV, solar radiances will use LIDORT
Both currently being tested by Julien Delanoe
N
Scattering regimes
• Regime 0: No attenuation
– Optical depth d << 1
• Regime 1: Single scattering
– Apparent backscatter b’ is easy to
calculate from d at range r :
b’(r) = b(r) exp[-2d(r)]
Footprint x
• Regime 2: Small-angle
multiple scattering
– Occurs when Ql ~ x
– Only for wavelength much less than
particle size, e.g. lidar & ice clouds
– No pulse stretching
Mean free path l
• Regime 3: Wide-angle multiple
scattering (pulse stretching)
– Occurs when l ~ x
THOR
lidar
Comparison of convergence rates
•
•
•
•
•
•
Solution is identical
Gauss-Newton method converges in < 10 iterations
L-BFGS Gradient Descent method converges in < 100 iterations
Conjugate Gradient method converges a little slower than L-BFGS
Each L-BFGS iteration >> 10x faster than each Gauss-Newton one!
Gauss-Newton method requires the Jacobian matrix, which must be
calculated by rerunning multiple scattering model multiple times
Observations
Retrieval
But lidar noise
Unified algorithm:
first results for ice+liquid
degrades retrieval
Convergence!
Truth
Retrieval
First guess
Iterations
Observations
Forward modelled retrieval
Forward modelled first guess
Retrieval
Add smoothness constraint
Truth
Retrieval
Smoother
retrieval
guess
butFirst
slower
Iterations
Observations
convergence
Observations
Forward modelled retrieval
Forward modelled first guess
Optical depth
Effective radius
Ice water path
Comparison with MODIS
• VarCloud-OA “Oblate ice”
– Our preferred ice model
– Poor agreement with MODIS optical
depth and effective radius
• VarCloud-BR “Bullet rosettes”
– Similar assumption to MODIS
– Better agreement
• So why does “OA” IWP agree better?
– IWP ~ opt. depth*effective radius
– MODIS effective radius is from top
few optical depths of the cloud and
assumed constant through cloud
– In reality (according to radar-lidar),
effective radius increases with depth
• MODIS underestimates IWP for a
given optical depth and effective
radius
Satellite observations: IceSAT
• Cloud observations from IceSAT 0.5-micron lidar
(first data Feb 2004)
• Global coverage but lidar attenuated by thick
clouds: direct model comparison difficult
Lidar apparent backscatter coefficient (m-1 sr-1)
Optically thick liquid cloud obscures view
of any clouds beneath
Latitude
Solution: forward-model the measurements (including
attenuation) using the ECMWF variables
ECMWF raw cloud fraction
Simulate lidar backscatter:
– Create subcolumns with max-rand overlap
– Forward-model lidar backscatter from
ECMWF water content & particle size
– Remove signals below lidar sensitivity
ECMWF cloud fraction after processing
IceSAT cloud fraction
Global cloud fraction comparison
ECMWF raw cloud fraction
• Results for October 2003
– Tropical convection peaks too high
– Too much polar cloud
– Elsewhere agreement is good
• Results can be ambiguous
– An apparent low cloud
underestimate could be a real
error, or could be due to high cloud
above being too thick
ECMWF processed cloud fraction
IceSAT cloud fraction
Wilkinson, Hogan, Illingworth and Benedetti (MWR 2008)
Testing the model climatology
Reduction in model due
to lidar attenuation
Error due to uncertain
extinction-to-backscatter ratio