Comparing various Lidar/Radar inversion strategies using Raman Lidar data (part II)D.Donovan, G-J Zadelhof (KNMI)

Z. Wang (NASA/GSFC)

D. Whiteman (NASA/GSFC)

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Background/Rational Raman-vs-Elastic backscatter lidars Results 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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Rational KNMI lidar/radar routine developed for simple

elastic lR backscatter lidar. No Rayleigh return MPL lidar data from ARM and SIRTA data has

good Rayleigh signal. Should exploit it ! Good Raman lidar data can serve as semi-

independent test of the strengths and weakness of different approaches.

Will first concentrate on Visible extinction retrieval.

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Elastic vs Inelastic scattering

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No Rayleigh, No RamanThe 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 with the Ze profile to estimate an effective particle size.

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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 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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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 R’effE3Minimize derivative of extE4Minimize derivative of No*

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A Test Case Using GSFC Raman lidar data and ARM MMCR.

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Comparison

Using Rayleigh returnabove cloud

Using smooth Reff (/Ze) constraint

Signature of MS

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Raman RatioRaman Direct

Method 1:Use RayleighMethod 2: Smooth /Ze

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Raman Direct

Method 1:Use RayleighMethod 2: Smooth /Ze

Raman Ratio

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Raman Ratio-vs-Direct

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Combine Methods 1+2(4) !Should work well in thickerClouds also.

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Combine Methods 1+4 (or 2) !Should work well in thickerClouds also.

4

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Ext –vs- Ze

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Conclusions•Multiple scattering effects clearly seen. Appear well accounted for using Eloranta’s approach.

•Should use Rayleigh info if available !

•Aim to create blended approach for non-Raman lidars to smoothly handle range of cases for non-Raman (i.e MPL) where Rayleigh signal from above cloud may or may not be available (almost there).

• Inferring optical depth using Ze alone very tricky on a case-by-case basis.

•Needs robust cloud masking (cld/nocloud/no info)

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Combine Methods 1+4 (or 2) !Should work well in thickerClouds also.

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Raman Ratio-vs-Direct

Raman Ratio Raman Direct

Raman Ratio Raman Direct

Method 1: Ray above

Method 2: Smooth /Ze

(Raman direct)