Lidar+Radar ice cloud remote sensing within CLOUDNET.
D.Donovan, G-J Zadelhof (KNMI) and the CLOUDNET team
With outside contributions from…
Z. Wang (NASA/GSFC)
D. Whiteman (NASA/GSFC)
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Background/Rational Approaches used in CloudNET How we have used data within
Cloudnet Testing using Raman lidar data (the
future) Summary
Introduction
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Active (lidar/radar) cloud remote sensing
LidarLidar
Radar
Returned Power
Tim
e or
R
ange
Lidar Radar
Difference in returns is a function of particle size !!
350-1100nm
3-100mm
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Basic ConsiderationsThe lidar extinction must first be extracted from the
lidar signal (or, equivalently, the observed lidar backscatter must be corrected for attenuation).
Observed signalCalibrationConstant
Backscatter
Extinction
Ze used to link backscatter and extinction and facilitate extinction correction/determination process.
The retrieved extinction (corrected backscatter) can then be used to estimate an effective particle size and IWC.
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No Rayleigh No RamanMust use Klett (Fernald + Rayleigh)
Must estimate extinction at zm(cloud top)
Very difficult to do directly if one only has IR lidar info
If have Radar then use smoothness constraint on derived lidar/radar particle size, or extinction, or No*.
But solutions converge if optical depth is above 1 or so !!
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Forward inversion is more direct but it is unstable !!Forward inversion is more direct but it is unstable !!
Radar
Lidar 10 % error
ForwardBackward
10 % error
In CLOUDNET most lidar data is IR No usableRayleigh signal Radar quite helps
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Effective Particle Size for Ice Crystals
Ice particles are large compared to lid (Optical scattering regime)Ice particles are small compared to rad (Rayleigh scattering regime)
Exact treatment of scattering difficult (impossible?)However:
Confirmed using DDA and RT calculations
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Two main approaches within CNET (for IR lidars)
Lidar SignalLidar Signal
Effective RadiusEffective RadiusIWCIWC
ExtinctionExtinction
Lidar+RadarSignals /Z
e=F(R'
eff)
RetrieveR'
eff,
Habit/sizedist form info.
Reff
IWC
Radar ReflectivityRadar Reflectivity
MS correctionMS correction
KNMIApproachChooses BVSuch thatVariation of ReffIs minimized
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CETP approach uses concept ofNormalized number density No* associatedwith scaled size distribution
Based on In-situ Aircraft Observations
CETPApproachChooses BVSuch thatVariation of No*Is minimized
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How we have used results within CNET ?
Current approachesLimited by fact thatMust have BOTHGood Radar and Lidar Data even to get Extinction
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So coverage is incomplete……
But we still have105s of data points !!
Good for parameterization development !
RedARM SGP
BLUECNET
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Can also use limited Lidar+Radar data as benchmark to assess accuracy of Radar only IWC estimates
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The Future ? The `super’ IR celiometers used at CABAUW
and Chilbolton really are not optimal. Ideal is a high power 24/7 Raman system !! But that is expensive (but will be coming to
CABAUW!) Settle for 24/7 visible or UV system of good
sensitivity for ice clouds.
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Elastic vs Inelastic scattering
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A Test Case Using GSFC Raman lidar data and ARM MMCR.
Eo S-S’E1 Force R=1 where no cloud.E2 Minimize derivative of extE3 Minimize derivative of R’effE4Minimize derivative of No*
Test various approachesw.r.t Raman results
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MS effects: Consistency between approaches Can be accounted for
Signature of MS
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Comparison of Techniques
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Comparison of Techniques (In terms of OT)
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OPTIMAL APPROACH(FOR ELASTIC RAYLEIGH LIDARS)
Combine Methods 1+3(4) !Should work well in thickerClouds also.
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Conclusions
• Lidar Radar ice cloud remote sensing is becoming mature
•Limitations and strengths of technique becoming more understood.
•Increasing body of comparisons with In-Situ measurements
•Most useful in CNET for statistical parameterization of ice cloud effective radius parameterizations and to help estimate accuracy of Radar only IWC estimations.
•Ideal is to use Raman Lidar. If this is not an option then a good vis/uv lidar with a Radar is a good option.
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Ext –vs- Ze
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If we have Useful Rayleigh above the cloud.
Then (effectively) can find S and Clid so thatThe scattering ratio R is 1.0 below and above cloud
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If We have good Raman data then…
Direct but noisy
Less noisy butindirect
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Implementation
Cost = Eo + W1*E1 +W2* E2 +W3*E3 + W4*E4
Eo S-S’E1 Force R=1 where no cloud.E2 Minimize derivative of extE3 Minimize derivative of R’effE4Minimize derivative of No*