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Proposed new usesfor the Ceilometer Network

Christine Chiu Ewan O’Conner, Robin Hogan, James Holmes

University of Reading

Outline

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How well the method performs and how we can work together

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Cloud radar

Ceilometer

What we propose to observe and why this is new

How we retrieve cloud optical depth from ceilometer data

Ceilometers have been used to observe aerosols and clouds

• Cloud base height for all cloud cases

• Cloud optical depth for thin clouds

• How about thick clouds?

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Cloud optical depth is the great unknown• Differences between climate models: factor 2-4

(Zhang et al., JGR, 2005)

• Differences between ground-based methods: factor 2-4 (Turner et al., BAMS, 2007)

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Multi-filter rotating shadowband radiometer (MFRSR)

works only for overcast cases

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AERONET cloud mode provides routine cloud optical depth measurements

Normal aerosol mode(sun-seeking)

Cloud mode(zenith-pointing)

Chiu et al. (JGR, 2010)

Fractional day

Zenith Radiance

(arbitrary unit)

cloudy

clear

“solar background light” (a lidar noise source)

Ceilometers measure zenith

radiance too!

lidar

lidarshoots

lidar

Sunshoots

Signal

no

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1-channel zenith radiance measurements are ambiguous for cloud retrievals in a 1D radiative transfer world

Cloud optical depth

Zenith Radiance

plane-parallel

3D simulations

Thick clouds – ceilometer’s active beam is completely

attenuated

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Use known overcast and clear-sky cases to develop our classification scheme

Overcast thick clouds

• Cloud optical depth > 10 continuously at least for 1 hour

Clear-sky

• Cloud optical depth < 3 continuously at least for 1hour

Determine if ceilometer’s active beam is completely attenuated

• Find the cloud top layer using cloud flags in Cloudnet products

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Backscatter signal (sr-1 m-1)

Range (km)

cloud top

• Calculate the mean backscatter signal from the cloud top to 1 km above

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Histogram of mean backscatter for clear-sky cases

•This threshold properly indentifies 97% of clear-sky cases

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clear-sky cases

clear

mean backscatter (log scale) between cloud top and 1km above

Altitude (km)

cloudy

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Histogram of mean backscatter for overcast clouds

•This threshold properly indentifies 86% of cloudy cases

mean backscatter (log scale) between cloud top and 1km above

0 100countsAltitude (km)

clearcloudy

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Evaluate our classification scheme using cloud mode retrievals

Cloud optical depth from AERONET cloud mode

Cloud optical depth from ceilometer

• drizzling

• thin clouds

• time/spatial resolution

Intercomparison at Chilbolton and Oklahoma sites

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Comparison to other instruments

• AERONET cloud mode observations

• Microwave radiometer

• Cloud radarτ =

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LWP

reff

reff in μm,

Liquid Water Path in g/m2

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Example from Chilbolton 2010/08/17

Attenuated backscatter coefficient

Reflectivity

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ct75K

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Retrievals from ceilometer, cloud mode and MWR agree well

ct75K

Aeronet

Time (UTC)

Cloud optical depthMWR

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Example – cirrus cloud (Oklahoma)

Time (UTC)

Attenuated backscatter coefficient

Reflectivity

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Retrievals difference could be up to 30% if using a wrong cloud phase

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18 18.5 19 19.5 20 20.5 21

water phase

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18 18.5 19 19.5 20 20.5 21

ice phase (D180)

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18 18.5 19 19.5 20 20.5 21

ice phase (D60)

Time (UTC)

Cloud optical depth

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Ice water paths derived from various empirical relationships

Time (UTC)

Ice water path (g/m2)

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18 18.5 19 19.5 20 20.5 21

Wang and Sassen (2002)Ebert and Curry (1992)Heymsfield et al 2003 (midlatitude)Cloudnet: Z-T?

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A more complex case – water cloud and thick ice cloud (Oklahoma)

Attenuated backscatter coefficient

Reflectivity

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Agreement is shown again for water clouds

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15 15.5 16 16.5 17

MWR0

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ceilometer

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Retrieved cloud optical depth

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AERONET

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MWRice phase with D60liquid phase

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Cloud optical depth could differ 30 –40% due to cloud phase

Time (UTC)

Retrieved cloud optical depth

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Water clouds at the Oklahoma site in 2007 May-November

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Cloud radar

Ceilometer

cloud optical depth

Occurrence counts

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Difference between ceilometer and lidar applications

Pros

• Seem easier to cross-calibrate ceilometer solar background light data

• Smaller impact from aerosol and Rayleigh scattering at ceilometer wavelengths

Cons

• Surface albedo could fluctuate quite significantly at 905 nm

• A few weak water vapor absorption lines around 905 nm

Summary

• The use of solar background light can greatly enhance current cloud products of ceilometer networks

• Confident about cloud optical depth retrievals for water clouds

• Continue testing our classification algorithm that distinguishes optically thin and thick clouds

• A lot of work needs to be done for retrieving ice- and mixed-phase clouds

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