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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
Time (UTC)
Retrieved cloud optical depth
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15 15.5 16 16.5 17
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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