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Proposed new uses for the Ceilometer Network

Proposed new uses for the Ceilometer Network. Christine Chiu Ewan O’Conner, Robin Hogan, James Holmes University of Reading. Outline. What we propose to observe and why this is new. How we retrieve cloud optical depth from ceilometer data.

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Proposed new uses for the Ceilometer Network

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  1. Proposed new usesfor the Ceilometer Network Christine Chiu Ewan O’Conner, Robin Hogan, James Holmes University of Reading

  2. Outline What we propose to observe and why this is new How we retrieve cloud optical depth from ceilometer data How well the method performs and how we can work together

  3. 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?

  4. 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)

  5. Multi-filter rotating shadowband radiometer (MFRSR) works only for overcast cases

  6. Cloud mode (zenith-pointing) Normal aerosol mode (sun-seeking) AERONET cloud mode provides routine cloud optical depth measurements Chiu et al. (JGR, 2010)

  7. no Sun shoots cloudy Signal Zenith Radiance (arbitrary unit) lidar shoots clear lidar lidar Fractional day Ceilometers measure zenithradiance too! “solar background light” (a lidar noise source)

  8. 3D simulations Zenith Radiance plane-parallel Cloud optical depth 1-channel zenith radiance measurements are ambiguous for cloud retrievals in a 1D radiative transfer world

  9. Thick clouds – ceilometer’s active beam is completely attenuated

  10. 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

  11. Determine if ceilometer’s active beam is completely attenuated • Find the cloud top layer using cloud flags in Cloudnet products Range (km) • Calculate the mean backscatter signal from the cloud top to 1 km above cloud top Backscatter signal (sr-1 m-1)

  12. Histogram of mean backscatter for clear-sky cases counts 0 100 • This threshold properly indentifies 97% of clear-sky cases Altitude (km) cloudy clear clear-sky cases mean backscatter (log scale) between cloud top and 1km above

  13. Histogram of mean backscatter for overcast clouds counts 0 100 Altitude (km) • This threshold properly indentifies 86% of cloudy cases clear cloudy mean backscatter (log scale) between cloud top and 1km above

  14. Evaluate our classification scheme using cloud mode retrievals • drizzling • thin clouds • time/spatial resolution Cloud optical depth from ceilometer Cloud optical depth from AERONET cloud mode

  15. Intercomparison at Chilbolton and Oklahoma sites

  16. Comparison to other instruments • AERONET cloud mode observations • Microwave radiometer • Cloud radar reff in μm, Liquid Water Path in g/m2

  17. Example from Chilbolton 2010/08/17 Reflectivity Attenuated backscatter coefficient

  18. Retrievals from ceilometer, cloud mode and MWR agree well Cloud optical depth MWR Aeronet ct75K ct75K Time (UTC)

  19. Example – cirrus cloud (Oklahoma) Reflectivity Attenuated backscatter coefficient Time (UTC)

  20. Retrievals difference could be up to 30% if using a wrong cloud phase Cloud optical depth ice phase (D180) water phase ice phase (D60) Time (UTC)

  21. Ice water paths derived from various empirical relationships Ice water path (g/m2) ? Time (UTC)

  22. A more complex case – water cloud and thick ice cloud (Oklahoma) Reflectivity Attenuated backscatter coefficient

  23. Agreement is shown again for water clouds Retrieved cloud optical depth ceilometer AERONET MWR Time (UTC)

  24. Cloud optical depth could differ 30 –40% due to cloud phase Retrieved cloud optical depth Time (UTC)

  25. Water clouds at the Oklahoma site in 2007 May-November Occurrence counts cloud optical depth

  26. 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

  27. 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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