Robin HoganRobin Hogan, Julien Delanoe, Julien DelanoeDepartment of Meteorology, University of Reading, UKDepartment of Meteorology, University of Reading, UK

Richard ForbesRichard ForbesEuropean Centre for Medium Range Weather ForecastsEuropean Centre for Medium Range Weather Forecasts

Alejandro Bodas-SalcedoAlejandro Bodas-SalcedoMet Office, UKMet Office, UK

Radar/lidar/radiometer Radar/lidar/radiometer retrievals of ice clouds retrievals of ice clouds

from the A-trainfrom the A-train

MotivationMotivation• Clouds are important for climate due to interaction with radiation

– A good cloud retrieval must be consistent with broadband fluxes at surface and top-of-atmosphere (TOA)

• Advantages of combining radar, lidar and radiometers– Radar ZD6, lidar ’D2 so the combination provides particle size– Radiances ensure that the retrieved profiles can be used for radiative

transfer studies

• How do we do we combine them optimally?– Use a “variational” framework: takes full account of observational errors– Straightforward to add extra constraints and extra instruments– Allows seamless retrieval between regions of different instrument

sensitivity

• In this talk a new variational radar-lidar-radiometer algorithm is applied to a month of A-Train data– Comparison with MODIS retrievals– Evaluation of Met Office and ECMWF model ice clouds– Investigation of the morphology of tropical cirrus

Formulation of variational Formulation of variational schemescheme

m

m

m

n

I

I

Z

Z

0.127.8

7.8

1

1

ln

ln

y

aer1

liq1

1

ice

ice1

ice1

ln

ln

LWP

ln

ln

ln

ln

N

S

N

N

m

n

x

For each ray of data we define:• Observation vector • State vector

– Elements may be missing– Logarithms prevent unphysical negative values

Attenuated lidar backscatter profile

Radar reflectivity factor profile (on different grid)

Ice visible extinction coefficient profile

Ice normalized number conc. profile

Extinction/backscatter ratio for ice

Visible optical depth

(TBD) Aerosol visible extinction coefficient profile

(TBD) Liquid water path and number conc. for each liquid layer

Infrared radiance

Radiance difference

Solution methodSolution method• An iterative method is required

to minimize the cost function

New ray of dataLocate cloud with radar & lidarDefine elements of xFirst guess of x

Forward modelPredict measurements y from state vector x using forward model H(x)Predict the Jacobian H=yi/xj

Has solution converged?2 convergence test

Gauss-Newton iteration stepPredict new state vector:

xk+1= xk+A-1{HTR-1[y-H(xk)]

-B-1(xk-b)-Txk}where the Hessian is

A=HTR-1H+B-1+T

Calculate error in retrieval

No

Yes

Proceed to next ray

Lidar forward model: multiple Lidar forward model: multiple scatteringscattering

• 90-m footprint of Calipso means that multiple scattering is a problem

• Eloranta’s (1998) model – O (N m/m !) efficient for N

points in profile and m-order scattering

– Too expensive to take to more than 3rd or 4th order in retrieval (not enough)

• New method: treats third and higher orders together– O (N 2) efficient – As accurate as Eloranta

when taken to ~6th order– 3-4 orders of magnitude

faster for N =50 (~ 0.1 ms)

Hogan (Applied Optics, 2006). Code: www.met.rdg.ac.uk/clouds

Ice cloud

Molecules

Liquid cloud

Aerosol

Narrow field-of-view:

forward scattered

photons escape

Wide field-of-view:

forward scattered

photons may be returned

Wide-angle multiple Wide-angle multiple scatteringscattering

CloudSat multiple scattering

• To extend to precip, need to model radar multiple scattering– Talk on Wednesday, session B!

New model agrees well with Monte Carlo

Radiance forward modelRadiance forward model• MODIS and CALIPSO each have 3 thermal infrared channels in

the atmospheric window region– Radiance depends on vertical distribution of microphysical

properties– Single channel: information on extinction near cloud top– Pair of channels: ice particle size information near cloud top

• Radiance model uses the 2-stream source function method– Efficient yet sufficiently accurate method that includes scattering– Provides important constraint for ice clouds detected only by lidar– Ice single-scatter properties from Anthony Baran’s aggregate

model– Correlated-k-distribution for gaseous absorption (from David

Donovan and Seiji Kato)

• MODIS solar channels provide an estimate of optical depth– Only available in daylight– Likely to be degraded by 3D radiative transfer effects– Only usable when no liquid clouds in profile … currently not used

Ice cloud: non-variational Ice cloud: non-variational retrievalretrieval

• Donovan et al. (2000) algorithm can only be applied where both lidar and radar have signal

Observations

State variables

Derived variables

Retrieval is accurate but not perfectly stable where lidar loses signal

Aircraft-simulated profiles with noise (from Hogan et al. 2006)

Variational radar/lidar Variational radar/lidar retrievalretrieval

• Noise in lidar backscatter feeds through to retrieved extinction

Observations

State variables

Derived variables

Lidar noise matched by retrieval

Noise feeds through to other variables

……add smoothness constraintadd smoothness constraint

• Smoothness constraint: add a term to cost function to penalize curvature in the solution ( J’ = id2i/dz2)

Observations

State variables

Derived variables

Retrieval reverts to a-priori N0

Extinction and IWC too low in radar-only region

……add a-priori error add a-priori error correlationcorrelation

• Use B (the a priori error covariance matrix) to smooth the N0 information in the vertical

Observations

State variables

Derived variables

Vertical correlation of error in N0

Extinction and IWC now more accurate

CloudSat-CALIPSO-MODIS CloudSat-CALIPSO-MODIS exampleexample

1000 km

Lidar observations

Radar observations

CloudSat-CALIPSO-MODIS CloudSat-CALIPSO-MODIS exampleexample

Lidar observations

Lidar forward model

Radar observations

Radar forward model

• Extinction coefficient

• Ice water content

• Effective radius

Forward modelMODIS 10.8-m observations

Radar-lidar retrievalRadar-lidar retrieval

Radiances matched by increasing extinction near cloud top

……add infrared radiancesadd infrared radiances

Forward modelMODIS 10.8-m observations

One orbit in July 2006

A-Train

Model

Comparison with Met Office Comparison with Met Office modelmodel

log10(IWC[kg m-3])

Antarctica

CentralPacific

ArcticOcean

CentralAtlantic

SouthAtlantic

Russia

Effective radius versus Effective radius versus temperaturetemperature

All clouds

An effective radius parameterization?

log10(IWC [kg m-3])

log10(IWC)

Lidar only

log10(IWC)

Radar only

log10(IWC)

Radar+lidar only

Frequency of IWC vs. Frequency of IWC vs. temperaturetemperature

• Mean and variance of IWC both increase with temperature

• Clearly need both radar and lidar to detect full range of ice clouds

Comparison of mean effective Comparison of mean effective radiusradius

• July 2006 mean value of re=3IWP/2i from CloudSat-CALIPSO only

• Just the top 500 m of cloud

• MODIS/Aqua standard product

Comparison of ice water pathComparison of ice water pathMean of all skies

Mean of clouds

CloudSat-CALIPSO MODIS

• Need longer period than just one month (July 2006) to obtain adequate statistics from poorer sampling of radar and lidar

Comparison of optical depthComparison of optical depthMean 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

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

)

Observations- Note limitation

of each instrument

Retrievals

Tropical Tropical Indian Indian Ocean Ocean cirruscirrus

MODIS infrared window radiance

Turbulent fall-streaks in lower half of cloud?

Stratiform region in upper half of

cloud?

Hogan and Kew (QJ 2005) found that mid-latitude

cirrus structure affected by cloud top turbulence

with a typical outer scale of 50-100 km

Outer scale 90 km

-5/3 law

600 km 120 km

Stratiform upper region dominated by larger scales

A-Train data show quite different structure above ~12.5 km in tropical cirrus: gravity waves?

Mid-latitude cirrus Mid-latitude cirrus Tropical cirrusTropical cirrus320 km

1300 km

Summary and future workSummary and future workNew dataset provides a unique perspective on global ice clouds• Planned retrieval enhancements

– Retrieve liquid clouds and precipitation at the same time to provide a truly seamless retrieval from the thinnest to the thickest clouds

– Incorporate microwave and visible radiances– Adapt for EarthCARE satellite (ESA/JAXA: launch 2013)

• Model evaluation– How can Met Office and ECMWF model cloud schemes be

improved?– High-resolution simulations of tropical convection in “CASCADE”– Use CERES to determine the radiative error associated with

misrepresented clouds in model

• Cloud structure and microphysics– What is the explanation for the different regions in tropical cirrus?– What determines the outer scale of variability?– Can we represent tropical cirrus in the Hogan & Kew fractal model?– Can we resolve the “small crystal” controversy?

ConvergenceConvergence• The solution generally

converges after two or three iterations– When formulated in terms

of ln(), ln(’) rather than ’ the forward model is much more linear so the minimum of the cost function is reached rapidly