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1 Proposed new uses for the Ceilometer Network Christine Chiu Ewan OConner, Robin Hogan, James Holmes University of Reading.

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Presentation on theme: "1 Proposed new uses for the Ceilometer Network Christine Chiu Ewan OConner, Robin Hogan, James Holmes University of Reading."— Presentation transcript:

1 1 Proposed new uses for the Ceilometer Network Christine Chiu Ewan OConner, Robin Hogan, James Holmes University of Reading

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

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? 3

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) 4

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

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

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

8 8 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

9 Thick clouds – ceilometers active beam is completely attenuated 9

10 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 ceilometers active beam is completely attenuated Find the cloud top layer using cloud flags in Cloudnet products 11 Backscatter signal (sr -1 m -1 ) Range (km) cloud top Calculate the mean backscatter signal from the cloud top to 1 km above

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

13 13 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 counts Altitude (km) clearcloudy

14 14 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

15 Intercomparison at Chilbolton and Oklahoma sites 15

16 16 Comparison to other instruments AERONET cloud mode observations Microwave radiometer Cloud radar r eff in μm, Liquid Water Path in g/m 2

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

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

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

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

21 21 Ice water paths derived from various empirical relationships Time (UTC) Ice water path (g/m 2 ) ?

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

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

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

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

26 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 27


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