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Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Radar/lidar observations of boundary layer clouds.

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Presentation on theme: "Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Radar/lidar observations of boundary layer clouds."— Presentation transcript:

1 Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Radar/lidar observations of boundary layer clouds

2 Overview Radar and lidar can measure boundary layer clouds at high resolution: –Cloud boundaries - radar and lidar –LWP – microwave radiometer –LWC – cloud boundaries and LWP Cloudnet – compare forecast models and observations –3 remote-sensing sites (currently), 6 models (currently) –Cloud fraction, liquid water content statistics Microphysical profiles: –Water vapour mixing ratio - Raman lidar –LWC - dual-wavelength radar –Drizzle properties - Doppler radar and lidar –Drop concentration and size – radar and lidar

3 Vertically pointing radar and lidar Radar: Z~D 6 Sensitive to larger particles (drizzle, rain) Lidar: ~D 2 Sensitive to small particles (droplets, aerosol)

4 Statistics - liquid water clouds 2 year database Use lidar to detect liquid cloud base –Low liquid water clouds present 23% of the time (above 400 m) Summer: 25% Winter: 20% Use radar to determine presence of drizzle –46% of clouds detected by lidar contain occasional large droplets Summer: 42% Winter: 52 %

5 Dual wavelength microwave radiometer –Brightness temperatures -> Liquid water path –Improved technique – Nicolas Gaussiat Use lidar to determine whether clear sky or not Adjust coefficients to account for instrument drift Removes offset for low LWP LWP - initial LWP - lidar corrected

6 LWC - Scaled adiabatic method –Use lidar/radar to determine cloud boundaries –Use model to estimate adiabatic gradient of lwc –Scale adiabatic lwc profile to match lwp from radiometers

7 Compare measured lwp to adiabatic lwp obtain dilution coefficient Dilution coefficient versus depth of cloud

8 Cloudnet Cabauw,The Netherlands Chilbolton, UK SIRTA, Palaiseau (Paris), France

9 Cloudnet data levels Level 2a daily files –High-resolution meteorological products on the radar grid 30 s, 60 m resolution Level 2b daily files –Meteorological products averaged on to the grid of each particular model: separate dataset for each model and product 1 hour, 200 m resolution (typical) –Includes cloud fraction and liquid water content Level 3 files by month and year, model version –Statistics of a comparison between model and the observations –Observed, and raw & modified model means on same vert. grid –PDFs, skill scores, correlations, anything that might be useful!

10 Cloud fraction –Radar provides first guess of cloud fraction in each model gridbox Lidar refines the estimate by removing drizzle beneath stratocumulus and adding thin liquid clouds (warm and supercooled) that the radar does not detect Model gridboxes

11 Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI RACMO Model Swedish RCA model Cloud fraction

12 Monthly statistics On model height grid –Mean obs & model fraction –Frequency of occurrence and amount when present (thresholds 0.05-0.95) On regular 1km grid for fair comparison between models –Contingency table, ETS, Q –Mean cloud fraction In four height ranges (0- 3, 3-7, 7-12, 12-18 km) –PDFs of obs & model fraction Height-independent –Contingency table, ETS, Q

13 Cloud fraction – ECMWF Concatenation of monthly statistics to produce yearly file with exactly the same format Skill scores etc. all much smoother If modellers prefer, we could group together periods with forecasts from the same version of the model

14 Cloud fraction - Met Office Mesoscale

15 Cloud fraction - Met Office Global

16 Liquid water content


18 Chilbolton – ECMWF

19 Chilbolton - Met Office Mesoscale

20 Chilbolton – Met Office Global

21 Cabauw - ECMWF

22 Cabauw - Met Office Mesoscale

23 Cabauw – Met Office Global

24 Humidity – Raman lidar –Raman lidar measures Raman backscatter at 408 and 387 nm which correspond to water and nitrogen rotational bands. Ratio of the two channels gives humidity mixing ratio –Can generate pdfs of humidity on model grid box

25 Mixing ratio comparison 11 Nov 2001 Raman lidar Unified Model, Mesoscale version Cloud

26 Small-scale humidity structure Correlation between adjacent range gates shows that small-scale structure is not random noise Typical horizontal cell size around 500m ~500m Mixing ratio at 720m ±6m Wind speed ~6 m/s

27 PDF comparison Agreement is mixed between lidar and model: –Good agreement at low levels –Some bimodal PDFs in the vicinity of vertical gradients Further analysis required: –More systematic study –Partially cloudy cases with PDF of liquid+vapour content 12 UTC15 UTC 1.6 km 0.2 km 0.8 km Radiosonde Smith (1990) triangular PDF scheme

28 Stratocumulus liquid water content Problem of using radar to infer liquid water content: –Very different moments of a bimodal size distribution: LWC dominated by ~10 m cloud droplets Radar reflectivity often dominated by drizzle drops ~200 m An alternative is to use dual-frequency radar –Radar attenuation proportional to LWC, increases with frequency –Therefore rate of change with height of the difference in 35- GHz and 94-GHz yields LWC with no size assumptions necessary –Each 1 dB difference corresponds to an LWP of ~120 g m -2 Can be difficult to implement in practice –Need very precise Z measurements Typically several minutes of averaging is required Need linear response throughout dynamic range of both radars


30 Drizzle below cloud Doppler radar and lidar - 4 observables (OConnor et al. 2005) Radar/lidar ratio provides information on particle size

31 Drizzle below cloud –Retrieve three components of drizzle DSD (N, D, μ). –Can then calculate LWC, LWF and vertical air velocity, w.

32 Drizzle below cloud –Typical cell size is about 2-3 km –Updrafts correlate well with liquid water flux

33 Profiles of lwc – no drizzle Examine radar/lidar profiles - retrieve LWC, N, D

34 Profiles of lwc – no drizzle 260 cm -3 90 cm -3 80 cm -3 Consistency shown between LWP estimates.

35 Profiles of lwc – no drizzle Cloud droplet sizes <12μm no drizzle present Cloud droplet sizes 18 μm drizzle present Agrees with Tripoli & Cotton (1980) critical size threshold

36 Turbulence 30-s standard deviation of 1-s radar velocities, plus wind speed, gives eddy dissipation rate (Bouniol et al. 2003)

37 Turbulence Can generate pdfs of turbulence for different cloud types

38 Conclusion Relevant Sc properties can be measured using remote sensing; –Ideally utilise radar, lidar and microwave radiometer measurements together. –Cloudnet project provides yearly/monthly statistics for cloud fraction and liquid water content including comparisons between observations and models. –Soon - number concentration and size, drizzle properties. –Humidity structure, turbulence. –Satellite measurements A-Train (Cloudsat + Calipso + Aqua) EarthCARE IceSat

39 Satellite measurements Icesat – lidar profiles Modis – LWP (imager)

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