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All Rights Reserved. No part of this document may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of
DEIMOS Space S.L.
CASPER Final Review (AR)CCloud and loud and AAerosol erosol SSynergetic ynergetic PProducts from roducts from EEarthCARE arthCARE RRetrievalsetrievals
January 19th-20th, 2009, ESTEC [19th - room Fr413 / 20th - Space Expo]
Scientific Presentation for:
ACM-Ice-Reading (variational synergetic ice retrieval)
D. Donovan (KNMI), G.J. van Zadelhoff‘ (KNMI) P. Kollias (McGill), W. Szyrmer (McGill), Aleksandra Tatarevic (McGill), R. Hogan (Univ. reading), J. Delanoe (Univ. Reading), F. Berger (DWD), K. Barfus (DWD), Juan-R. Acarreta (DMS)
DEIMOS Space S.L. (2009)DEIMOS Space S.L. (2009)
Robin Hogan and Julien Delanoe
University of Reading
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OverviewOverview
Introduction
1. Summary of achievements in Casper
2. Overview of synergy products, need for target classification
CASPER Algorithm: ACM-Ice-Reading (including AC-Ice-Reading)
1. Why this algorithm is needed ?
2. Input Data and Product Definition
3. Theoretical description
4. Summary of the performance and error analysis
5. Verification and Validation
1. “Blind-test” cases using aircraft data
2. ECSIM cases
3. Application of a similar algorithm to CloudSat, CALIPSO and MODIS
Conclusions
1. Generalizing to “unified” synergy algorithm
2. Recommendations for necessary post-Casper work
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Summary of achievements
• Identified the synergy products required by EarthCARE
• Reviewed the relevant literature for each of them (PARD)
• Prioritized future work on synergy algorithms for EarthCARE
• Described a retrieval algorithm for ice clouds that uses radar,
HSRL lidar and infrared radiances (ATBD)
• Developed the code for the algorithm
• Integrated it into ECSIM
• Tested the code on simulated data
• Applied a similar algorithm (simple backscatter rather than
HSRL) to a month of CloudSat/CALIPSO/MODIS data
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Synergy (Level 2b) overview
• Target classification
– Radar-lidar target classification
• Two-instrument algorithms
– Various combinations of radar (Z, v), lidar (backscatter,
HSRL), MSI (IR, solar)…
– To estimate ice, liquid, aerosol, precipitation properties
– Too many combinations possible – need to be selective
• Three-instrument algorithms
– Needs variational framework
• Higher level products
– L2b-2D Cloud fraction, overlap, mean water content and
inhomogeneity on pseudo-model grid
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Reading contribution to Casper
Implemente
d in Casper
(ATBD)
Planned
in
Casper
(PARD)
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Target Classification product
• Importance: MANDATORY
– Classification essential to facilitate synergetic algorithms
– Also useful to provide one file from which subsequent algorithms could
run by regridding, storing errors etc.
• Maturity: NOVEL/MATURE
– This work has been carried out on ground-based radar and lidar data
– Application to CloudSat/CALIPSO is ongoing but less mature
• Wang & Sassen (2001) designed CloudSat 2B-CLDCLASS
– Attempt to match traditional classification of “stratocu”, “altostratus” etc.
• But subsequent algorithms actually want to know
– Target phase (liquid/ice) and where we can’t be sure
– Whether cloud or precipitation
– Details: Hail/graupel, melting ice, warm/”cold” rain?
– Other targets: aerosol, insects, molecular
– Co-existence of the above target types
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Ground-based classification
• Example of target classification during the “Cloudnet” project – In this case the classes are: ice, liquid cloud, drizzle/rain and aerosol
Ground-based cloud radar observations
from Chilbolton
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CloudSat/CALIPSO
Cloudsat radar
CALIPSO lidar
Preliminary target classificationInsectsAerosolRainSupercooled liquid cloudWarm liquid cloudIce and supercooled liquidIceClearNo ice/rain but possibly liquidGround
• This is an example of how such a product might look
• Priority for development after CASPER
9
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Radar-Lidar-MSI ice cloud product
• Why is this product required?
– Ice clouds an important component of the radiation budget of
the earth, and their properties still vary widely in climate models
– A lot is known on how to combine radar and lidar in synergy, and
indeed much of the preparatory work has been carried out
– By adding MSI information the profile of cloud properties should
more consistent with the broadband radiation measurements,
which is a key mission requirement
– This product should be the first “official” global radar-lidar
retrieval of ice cloud properties, particularly effective radius
• Therefore this product is a key EarthCARE output and is Mandatory
– Radar-Lidar (AC-Ice-Reading) version is Mature
– Radar-Lidar-Radiometer (ACM-Ice-Reading) version is Novel
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Input data required
• Platform and orbit parameters
– time(t), longitude(t), latitude(t), altitude(t), height(t,z) ...
• Instrument characteristics
– lidar_div, lidar_fov, C_lid(t)
• Measurements
– Z(t, z), bscat_Mie(t, z), bscat_Ray(t, z), radiance(t, )
• Measurement errors
– Standard error in each of the input data
• Cloud mask
– mask_radar(t, z), mask_lidar(t, z), cloud_phase(t, z)
• Met and surface data (ECMWF)
– temperature(t, z), pressure(t, z), q(t, z), ozone(t, z)
– surf_pressure(t), skin_temperature(t), surf_emissivity(t)
• Note that these are as in the KNMI “merged file”
11
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Product definition
• Platform and orbit parameters
– Repeated from the input data
• Directly retrieved variables
– Extinction (t, z), N0* (t, z), lidar_ratio (t, z)
• Variables derived from retrieved variables
– IWC (t, z), re (t, z), optical depth (t)
• Forward modelled variables at final iteration
– Z_fwd (t,z), bscat_Mie_fwd (t,z), bscat_Ray_fwd (t,z), radiance_fwd(t,)
• Measures of convergence
– n_iterations (t), chi_squared (t, iteration)
• Status flags
– retrieval_flag (t, z), instrument_flag(t, z), radiance_flag(t)
• Error standard deviations
– ln_extinction_err (t, z), ln_N0*_err(t, z), ln_lidar_ratio_err(t, z)
– ln_IWC_err (t, z), ln_effective_radius_err(t, z), optical_depth_err (t)
12
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Previous 2-instrument algorithms
• Various combinations of instruments similar to those on EarthCARE have
been tried for ice clouds before
– Lidar and radar definitely the most promising!
– Radar ZD6, lidar ’D2 so the combination provides particle size
Radar
Lidar Wang & Sassen, Donovan et al., Tinel et al., Delanoe and Hogan
IR Not feasible: radar would need to see to cloud top
Chiriaco et al.; limited to thin clouds
Solar Benedetti et al., Polonsky et al. (CloudSat); need to do liquid simultaneously, day only
Possible; limited to thin clouds with no liquid beneath, day only
Possible; need radar to be sure no liquid cloud beneath, day only
ICE Radar Lidar IR Solar
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Radar-lidar ice algorithm history
• Don’t correct lidar for attenuation: Intrieri et al. (1993)
– Limited to very thin clouds; lidar ratio must be assumed
• Invert lidar separately: Mace et al. (1998), Wang & Sassen (2002), Okamoto et
al. (2003)
– Extinction error increases into cloud due to assumed lidar ratio
• Optimal estimation but lidar inverted separately: Mitrescu et al. (2005)
– Same lidar errors as Wang & Sassen
• Invert lidar with radar: Donovan & van Lammeren (2001), Tinel et al. (2005)
– Lidar ratio is retrieved: much more accurate
• Full optimal-estimation (=variational) approach: Delanoe & Hogan (2008)
– Same strengths as Donovan & van Lammeren
– Allows extra constraints/obs to be included, e.g. infrared radiances
– Can blend into regions detected only by radar or lidar
– Provides retrieval errors and error covariances
• Exploit the HSRL channels when available (new in CASPER)
14
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Formulation of the problem
• Observation vector
– Elements may be missing
,1
,
1
,1
,
ln
ln
ln
= .
ln
ln
ln
v
v n
g
b
b m
S
S
N
N
x
1
1
'
1
ln
ln
ln
= .ln
ln
ln
mie
miep
ray
ray p
q
Z
Z
I
I
y
• State vector
– Retrieved variables
HSRL Mie channel
HSRL Rayleigh channel
Radar reflectivity
Radiances
Extinction coefficient
Lidar ratio
Ratio N0*/0.6
15
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Synergetic retrieval framework
New ray of data: define state vector x
Use merged file to specify variables describing ice cloud at each gate
Radar model
Radar reflectivityLidar model
Including HSRL channels and multiple scattering
Radiance model
IR channels
Compare to observations
Check for convergence
Gauss-Newton iteration
Derive a new state vector
Forward model
Not converged
Converged
Calculate errors and proceed to next ray of data
• Minimize cost function of the form: J = squared difference between observations and forward model + squared
difference between state vector and a-priori + smoothness constraints
16
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DEIMOS Space S.L.
Why N0*/0.6?
• In-situ aircraft data show
that N0*/0.6 has
temperature dependence
that is independent of
IWC
• Therefore we have a
good a-priori estimate to
constrain the retrieval
• Also assume vertical
correlation to spread
information in height,
particularly to parts of
the profile detected by
only one instrument
17
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Why N0*???
• We need to be able to
forward model Z and other
variables from x
• Large scatter between
extinction and Z implies 2D
lookup-table is required
• When normalized by N0*,
there is a near-unique
relationship between /N0*
and Z/N0* (as well as re,
IWC/N0* etc.)
18
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• Photon variance-covariance method– Hogan (Applied Optics 2006, JAS 2008)– As light propagates through a medium
where size r » wavelength , narrow-angle forward-scattering widens beam
– Write down differential equations for
• Total energy P
• Positional variance
• Directional variance
• Covariance
– E.g.
2s
r s
Lidar field-of-view(equivalent
medium theorem allows forward
scattering on the return journey to
be neglected)
Modelling HSRL with multiple scattering
22 / rαz/ζ cloud
2ζ
ζs
– Thus can calculate positional variance versus range z and hence the fraction of light remaining in field of view
– Very efficient: time proportional to number of pixels squared• Modelling HSRL channels in CASPER: a straightforward modification
– Particles and molecules already treated separately since molecules don’t have a forward-scattering lobe
19
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Effect of errors on retrievals
Source of error Extinction Eff. Radiusre
IWC
Any error in lidar calibration No effect No effect No effect
Any change in absolute value of lidar ratio
No effect No effect No effect
Radar calibration a factor of 2 too high (+3 dB)
No effect +5 mm +10%
Uncertainties in the representation of small crystals
No effect ±15% ±15%
Uncertainties in mass–size relationship No effect ±30% ±30%
Difference in radar and lidar footprints ±8% ±1 mm ±8%
Partly taken from Hogan et al. (JTECH 2006)
20
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DEIMOS Space S.L.
No HSRLNo HSRL“M
easu
rem
en
ts”
Retr
ievals
Aircraft-observed ice size spectra used to generate pseudo-measurements (Hogan et al 2006 “blind test” case)
Note that the same lidar forward model (which includes multiple scattering) is used in generating the pseudo-measurements and in the retrieval
Lidar ratio S assumed constant in retrieval
21
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DEIMOS Space S.L.
S free to vary with height (except for smoothness constraint)
Can reproduce features of true S
More accurate extinction where lidar has signal
Some noise from Rayleigh channel in retrieved S: need more smoothness
With HSRLWith HSRL“M
easu
rem
en
ts”
Retr
ievals
22
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DEIMOS Space S.L.
ECSIM fractal cirrus case
KNMI have run ECSIM using a cirrus cloud generated by the Hogan and Kew (2005) model
ECSIM radar reflectivity
ECSIM lidar Mie channel ECSIM lidar Rayleigh channel
23
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ECSIM fractal cirrus case
KNMI have run ECSIM using a cirrus cloud generated by the Hogan and Kew (2005) model
At the final iteration, the variational scheme attempts to “forward model” the observations but without reproducing instrument noise
ECSIM radar reflectivity
ECSIM lidar Mie channel ECSIM lidar Rayleigh channel
Retrieval forward model
Retrieval forward model Retrieval forward model
24
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Comparison with “truth”“True” ECSIM extinction coefficient
Retrieved extinction coefficient
Retrieved effective radius
Retrieved ice water content
Slightly poorer agreement than “blind-test” profiles, presumably because ECSIM instrument simulator
different from forward model in the retrieval algorithm
25
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…Add radiances to retrieval“True” ECSIM extinction coefficient
Retrieved extinction coefficient
Retrieved effective radius
Retrieved ice water content
Merits of radiances are inconclusive; HSRL already accurate, and forward-model errors may be important…
26
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CloudSat-CALIPSO-MODIS example
1000 km
• Lidar observations
• Radar observations
27
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CloudSat-CALIPSO-MODIS example
• Lidar observations
• Lidar forward model
• Radar observations
• Radar forward model
28
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• Extinction coefficient
• Ice water content
• Effective radius
Forward modelMODIS 10.8-m observations
Radar-lidar retrieval
29
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Radiances
matched by
increasing
extinction near
cloud top
…add infrared radiances
Forward modelMODIS 10.8-m observations
30
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Radar-lidar complementarityRadar-lidar complementarity
CloudSat radar
CALIPSO lidar
MODIS 11 micron channel
Time since start of orbit (s)
Heig
ht
(km
)H
eig
ht
(km
)
Cirrus detected only by lidar
Mid-level liquid clouds
Deep convection penetrated only by radar
Retrieved extinction (m-1)
31
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1-month optical depth comparison1-month optical depth comparison
Mean of all skies
Mean of clouds
CloudSat-CALIPSO MODIS
• Mean optical depth from CloudSat-CALIPSO is lower than MODIS simply because CALIPSO detected many more optically thin clouds not seen by MODIS
• Hence need to compare PDFs as well
32
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First comparison with ECMWF
log10(IWC[kg m-3])
33
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A-Train
Tem
pera
ture
(°C
)Comparison with model IWCComparison with model IWC
Met Office ECMWF
• Global forecast model data extracted underneath A-Train• A-Train ice water content averaged to model grid
– Met Office model lacks observed variability– ECMWF model has artificial threshold for snow at around 10-4 kg m-3
Tem
pera
ture
(°C
)
34
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Post-Casper work
• Essential further work for ACM-Ice-Reading algorithm:
– Verify that when one of the instruments is missing,
algorithm will approximate existing two-instrument
algorithms, e.g. Chiriaco et al. (lidar-MSI)
– Check infrared forward model (e.g. against “RTTOV”)
– Apply to real HSRL data and compare to in-situ “truth”
• Broader outlook for synergy algorithms for EarthCARE
– Develop “unified” algorithm to retrieve all species (ice
cloud, liquid cloud, aerosols and precipitation)
simultaneously; often several present in the same profile
– Considerable work required to select best state vector etc.
35
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General synergetic framework
New ray of data: define state vector
Use classification to specify variables describing each species at each gate• Ice: extinction coefficient and N0*
• Liquid: liquid water content and number concentration• Rain: rain rate and mean drop diameter• Aerosol: extinction coefficient and particle size
Radar model
Including surface return and multiple scattering
Lidar model
Including HSRL channels and multiple scattering
Radiance model
Solar and IR channels
Compare to observations
Check for convergence
Gauss-Newton iteration
Derive a new state vector
Forward model
Not converged
Converged
Proceed to next ray of data
(Black) Ingredients delivered in Casper (Delanoe and Hogan
JGR 2008)
(Red) Ingredients remaining to be
developed
36
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Components of liquid-cloud algorithm
Source of information Caveats
Radar reflectivity of cloud: if sensitive to the cloud droplets then Z is strongly related to LWC
Not applicable if cloud contains drizzle droplets, which is the case in most clouds over the ocean (Fox & Illingworth 1997)
MSI optical depth provides path constraint (e.g. Austin & Stephens 2001 combined with radar) and size information
Only works in daylight with no other clouds in the profile; less accurate over more reflective surfaces
Surface return provides estimate of path attenuation, proportional to LWP (Smith and Illingworth, in prep)
Only over the sea, need to find clear-sky regions to each side for baseline; dependence on surface wind stress
For < 1.5 (e.g. some supercooled clouds), can be derived from integrated lidar backscatter (Hogan et al. 2003)
Only a small fraction of optically thin clouds, and more difficult when no cloud above
In optically thick clouds, multiple scattering can result in “exponential tail” related to (Polonsky and Davis 2004)
Narrow EarthCARE field of view probably means that the exponential tail is too weak
Width in range of lidar backscatter peak in optically thick clouds is related to number concentration (O’Connor)
Needs validation; need to check dependence on entrainment of dry air and effect of cloud inhomogeneity in sampling period
HSRL provides extinction coefficient near cloud top Unclear how useful this information is further into the cloud due to dilution by entrainment of dry air near cloud top
Rate of increase of depolarization ratio due to multiple scattering provides a measure of extinction coefficient
Currently no fast forward model for the effects of multiple scattering on depolarization
37
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Recommendations
• Radar/lidar mis-pointing should be < 500 m, equivalent to RMS error in Z of 0.5 dB
• Most two-instrument algorithms should be tested as limiting cases of a more general
multi-instrument algorithm, covering EarthCARE in case of instrument failure
• The target classification should be developed as a priority, and would include the
measurements on the same grid, to facilitate synergetic algorithms
• Level 2b-2D products should be produced: cloud fraction, overlap, etc., under
EarthCARE but averaged to typical model resolution; satisfies a key mission requirement
• A flexible optimal-estimation software library should be developed to facilitate
implementation of synergetic algorithms, in particular the “best-estimate” algorithms
• A scattering library and associated tools should be developed, to enable the look-up
tables required by all algorithms to be generated consistently across the full spectrum
• A concerted effort is required to validate algorithms using a wide variety of data
sources, including the A-train, ECSIM and dedicated aircraft campaigns
• Areas requiring focussed attention:
– Need a shortwave forward model to allow shortwave radiances to be utilized
– Utilizing surface return over the ocean to detect small liquid attenuation
– Exploiting multiply-scattered radar returns in using appropriate forward model
– Treating the complex microphysics of deep convective cloud adequately
– Developing appropriate constraints on vertical profile of retrieved variables,
such as continuity of mass flux across the melting layer in precipitating situations
top related