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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

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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”

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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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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)

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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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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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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

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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

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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