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*