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Towards “unified” radar/lidar/radiometer retrievals for cloud radiation studies. Robin Hogan Julien Delanoe Department of Meteorology, University of Reading, UK. Motivation. Clouds are important due to their role in radiative transfer - PowerPoint PPT Presentation
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Robin HoganJulien Delanoe
Department of Meteorology, University of Reading, UK
Towards “unified” Towards “unified” radar/lidar/radiometer radar/lidar/radiometer
retrievals for cloud retrievals for cloud radiation studiesradiation studies
MotivationMotivation• Clouds are important due to their role in radiative transfer
– A good cloud retrieval must be consistent with broadband fluxes at surface and top-of-atmosphere (TOA)
• Increasingly, multi-parameter cloud radar and lidar are being deployed together with a range of passive radiometers– We want to retrieve an “optimum” estimate of the state of the
atmosphere that is consistent with all the measurements– But most algorithms use at most only two instruments/variables and
don’t take proper account of instrumental errors
• The “variational” framework is standard in data assimilation and passive sounding, but has only recently been applied to radar– Mathematically rigorous and takes full account of errors– Straightforward to add extra constraints and extra instruments
• In this talk it will be shown how radar, lidar and infrared radiometers can be combined for ice cloud retrievals– Demonstrated on ground-based and satellite (A-train) observations– Discuss challenges of extending to other clouds and other instruments
Surface/satellite observing Surface/satellite observing systemssystems
Ground-based sites
ARM and Cloudnet
NASA A-TrainAqua, CloudSat,
CALIPSO, PARASOL
ESA EarthCAREFor launch in 2013
Radar 35 and/or 94 GHzDoppler, Polarization
94 GHz CloudSat 94 GHz CPRDoppler
Lidar Usually532 or 905 nm
Polarization
532 & 1064 nm CALIOP
Polarization
355 nm ATLID Polarization, HSRL
VIS/IR radiometers
Some have infrared radiometer, sky
imager, spectrometer
MODIS, AIRS,CALIPSO IIR (Imaging Infrared Radiometer)
Multi-Spectral Imager (MSI)
Microwave radiometers
Dual-wavelength radiometer
(e.g. 22 & 28 GHz)
AMSR-E (6, 10, 18, 23, 36, 89 GHz)Polarization
None
Broadband radiometers
Surface BBREurope/Africa sites
have GERB overhead
CERES (TOA only) BBR (TOA only)
– Broadband radiometers used only to test retrievals made using the other instruments
Radar and lidarRadar and lidar• 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
• Some limitations of existing radar/lidar ice retrieval schemes (Donovan et al. 2000, Tinel et al. 2005, Mitrescu et al. 2005)– They only work in regions of cloud detected by both radar and lidar– Noise in measurements results in noise in the retrieved variables– Eloranta’s lidar multiple-scattering model is too slow to take to
greater than 3rd or 4th order scattering– Other clouds in the profile are not included, e.g. liquid water clouds– Difficult to make use of other measurements, e.g. passive radiances – Difficult to also make use of lidar molecular scattering beyond the
cloud as an optical depth constraint– Some methods need the unknown “lidar ratio” to be specified
• A “unified” variational scheme can solve all of these problems
Why not invert the lidar Why not invert the lidar separately?separately?
• “Standard method”: assume a value for the extinction-to-backscatter ratio, S, and use a gate-by-gate correction – Problem: for optical depth >2 is excessively sensitive to choice of S– Exactly the same instability for radar (Hitschfeld & Bordan 1954)
• Better method (e.g. Donovan et al. 2000): retrieve the S that is most consistent with the radar and other constraints– For example, when combined with radar, it should produce a profile of
particle size or number concentration that varies least with range
Implied optical depth is infinite
Example fromUS ARM site:Need todistinguishinsects fromcloud
First step: target First step: target classificationclassification
Ice
LiquidRainAerosol Insects
• Combining radar, lidar with temperature from a model allows the type of cloud (or other target) to be identified– Example from Cloudnet processing of ARM data (Illingworth et al., BAMS
2007)
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
Aerosol visible extinction coefficient profile
Liquid water path and number conc. for each liquid layer
Infrared radiance
Radiance difference
The cost functionThe cost function• The essence of the method is to find the state vector x that
minimizes a cost function:
2 2
2 21 1
( )y x
i i
n ni i i
i iy b
y H x bJ
x+ Smoothness
constraints
Each observation yi is weighted by the inverse of
its error variance
The forward model H(x) predicts the observations from the state vector x
Some elements of x are constrained by an a priori estimate
This term penalizes curvature in the
extinction profile
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
Radar forward model and Radar forward model and a a prioripriori• Create lookup tables
– Gamma size distributions– Choose mass-area-size relationships– Mie theory for 94-GHz reflectivity
• Define normalized number concentration parameter– “The N0 that an exponential
distribution would have with same IWC and D0 as actual distribution”
– Forward model predicts Z from extinction and N0
– Effective radius from lookup table
• N0 has strong T dependence– Use Field et al. power-law as a-priori– When no lidar signal, retrieval
relaxes to one based on Z and T (Liu and Illingworth 2000, Hogan et al. 2006)
Field et al. (2005)
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
Poster P3.10: Multiple Poster P3.10: Multiple scatteringscattering
CloudSat multiple scattering
• To extend to precip, need to model radar multiple scatteringNew model agrees well
with Monte Carlo
Radiance forward modelRadiance forward model• MODIS solar channels provide an estimate of optical depth
– Only very weakly dependent on vertical location of cloud so we simply use the MODIS optical depth product as a constraint
– Only available in daylight– Likely to be degraded by 3D cloud effects
• MODIS, CALIPSO and SEVIRI each have 3 thermal infrared channels in 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)
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
……add visible optical depth add visible optical depth constraintconstraint
• Integrated extinction now constrained by the MODIS-derived visible optical depth
Observations
State variables
Derived variables
Slight refinement to extinction and IWC
……add infrared radiancesadd infrared radiances
• Better fit to IWC and re at cloud top
Observations
State variables
Derived variables
Poorer fit to Z at cloud top: information here now from radiances
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
Ground based exampleGround based example• Radagast Campaign (AMMA)
– Based in Niamey, Niger
• ARM Mobile Facility– MMCR cloud radar – 532-nm micropulse lidar– SEVIRI radiometer aboard
MeteoSat 2nd Generation: 8.7, 10.8, 12µm channels
• Ice cloud case, 22 July 2006
Example from the AMF in Example from the AMF in NiameyNiamey
94-GHz radar reflectivity
532-nm lidar backscatter
Forward model at
final iteration
94-GHz radar reflectivity
532-nm lidar backscatter
Observations
Retrievals in regions where radar or lidar detects the cloud
Retrieved visible extinction coefficient
Retrieved effective radius
Results: radar+lidar Results: radar+lidar onlyonly
Large error where only one instrument detects the cloud
Retrieval error in ln(extinction)
TOA radiances increase retrieved optical depth and decrease particle size near cloud top
Cloud-top error greatly reduced
Retrieval error in ln(extinction)
Retrieved visible extinction coefficient
Retrieved effective radius
Results: radar, lidar, SEVERI Results: radar, lidar, SEVERI radiancesradiances
CloudSat/CALIPSO retrievalCloudSat/CALIPSO retrieval
0352 0355 0358
Oct 13, 2006 0352-0358
Radar Reflectivity from CloudSatRadar Reflectivity from CloudSat
Attenuated lidar backscatter from CALIPSOAttenuated lidar backscatter from CALIPSO
Heig
ht
[km
]H
eig
ht
[km
]
AVHRR
Forward modelForward modelObserved
radar reflectivity,
95 GHz
Attenuated lidar
backscatter, 532 nm
Radar reflectivity
forward model
Attenuated lidar
backscatter forward model
Preliminary results Preliminary results (radar+lidar)(radar+lidar)October 13th 2006
Granule 2006286023036_02443 between 3h52 and 3h58 UTC
Retrieved error in ln(extinction)
Heig
ht
[km
]
Retrieved number concentration
Heig
ht
[km
]
Retrieved effective radius
Heig
ht
[km
]
Retrieved visible extinction coefficient, log10(m-1)
Heig
ht
[km
]
Supercooled water?
MODIS radiancesMODIS radiances
Radar Reflectivity from CloudSatRadar Reflectivity from CloudSat
Attenuated lidar backscatter from CALIPSOAttenuated lidar backscatter from CALIPSO
Radiances W sr-1 m-2
Forward model
MODIS
8.4–8.7 micron
10.78–11.25 micron
Heig
ht
[km
]H
eig
ht
[km
]Radiances not used in retrieval, just forward modeled for comparison
CloudSat/CALIPSO exampleCloudSat/CALIPSO example
Radar Reflectivity from CloudSatRadar Reflectivity from CloudSat
2006 Day 286
Attenuated lidar backscatter from CALIPSOAttenuated lidar backscatter from CALIPSO
Supercooled water: strong signal from lidar, weak (or nothing) from radar
Radar fails to detect thin cirrus
Conclusions and ongoing Conclusions and ongoing workwork
• New radar/lidar/radiometer cloud retrieval scheme – Applied to ground based or satellite data– Appropriate choice of state vector and smoothness constraints
ensures the retrievals are accurate and efficient– Can include any relevant measurement if forward model is
available– Could provide the basis for cloud/rain data assimilation
• Extension to other cloud types– Retrieve properties of liquid-water layers, drizzle and aerosol– Incorporate microwave radiances and “wide-angle” radar/lidar
multiple-scattering forward models for deep precipitating clouds• Other activities
– Validate using aircraft underflights– Use in radiative transfer model to compare with TOA & surface
fluxes– Build up global cloud climatology to evaluate models