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Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet.

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Presentation on theme: "Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet."— Presentation transcript:

1 Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet

2 The EU Cloudnet project Development of a European pilot network of stations for observing cloud profiles Scientific objectives 1.To optimise the use of existing data sets to develop and validate cloud remote sensing synergy algorithms. 2. To demonstrate the importance of an operational network of cloud remote sensing stations to provide data for the improvement of the representation of clouds in climate and weather forecast models.

3 Cloudnet Cabauw,The Netherlands Chilbolton, UK SIRTA, Palaiseau (Paris), France Core instrumentation at each site –Radar, lidar, microwave radiometers, raingauge

4 Overview Aim: to retrieve continuously the cloud parameters from observations to evaluate climate and forecast models –Cloud parameterisation in operational NWP models. –Combine radar, lidar, model, raingauge and microwave radiometer into single product including instrument error characteristics. –Use common formats based around NetCDF to allow all algorithms to be applied at all sites and compared to all models –Report retrieval errors and data quality flags Generate products Compare forecast models and observations –4 remote-sensing sites (currently), 7 models (currently) –Cloud fraction, ice/liquid water content statistics

5 Cloud Parameterisation Operational models currently in each grid box typically two prognostic cloud variables: –Prognostic liquid water/vapour content –Prognostic ice water content (IWC) OR diagnose from T –Prognostic cloud fraction OR diagnosed from total water PDF Particle size is prescribed: –Cloud droplets - different for marine/continental –Ice particles – size decreases with temperature –Terminal velocity is a function of ice water content Sub-grid scale effects: –Overlap is assumed to be maximum-random –What about cloud inhomogeneity? How can we evaluate & hence improve model clouds?

6 Standard CloudNET observations (e.g. Chilbolton ) RadarLidar, gauge, radiometers

7 Basics of radar and lidar Radar/lidar ratio provides information on particle size Detects cloud base Penetrates ice cloud Strong echo from liquid clouds Detects cloud top Radar: Z~D 6 Sensitive to large particles (ice, drizzle) Lidar: ~D 2 Sensitive to small particles (droplets, aerosol)

8 The Instrument synergy/ Target categorization product Makes multi-sensor data much easier to use: –Combines radar, lidar, model, raingauge and -wave radiometer –Identical format for each site Performs many common pre-processing tasks: –Interpolation on to the same grid –Ingest model data (many algorithms need temperature & wind) –Correction of radar for gaseous attenuation (using model) and liquid attenuation (using microwave LWP and lidar) –Quantify random and systematic measurement errors –Quantify instrument sensitivity –Categorization of atmospheric targets: does my algorithm work with this target/hydrometeor type? –Data quality: are the data reliable enough for my algorithm?


10 Measurements



13 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

14 Target categorization Combining radar, lidar and model allows the type of cloud (or other target) to be identified Generate products and compare with model variables in each model gridbox

15 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, ice 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!

16 Products Level 2a daily files –High-resolution meteorological products on the radar grid 30 s, 60 m resolution –Target categorization/classification –Cloud fraction –Liquid water content –Ice water content –Turbulent kinetic energy dissipation rate –Ice cloud properties –Liquid cloud properties –Drizzle properties

17 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

18 Cloud fraction Observations ECMWF meso global Météo France RACMO SMHI RCA Met Office

19 Model intercomparison

20 Monthly statistics On model height grid –Mean obs & model fraction –Frequency of occurrence and amount when present (thresholds ) 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, km) –PDFs of obs & model fraction Height-independent –Contingency table, ETS, Q

21 Cloud fraction ECMWF Concatenation of monthly statistics to produce yearly file with exactly the same format Skill scores etc. all much smoother We can also group together periods with forecasts from the same version of the model

22 Cloud fraction Met Office mesoscale Low cloud: Cloud occurrence correct but cloud not thick enough. High cloud: Cloud occurrence correct but cloud not thick enough.

23 Modification of cloud scheme – cloud fraction and water content now diagnosed from total water content. Cloud fraction What happened to the Meteo France ARPEGE model on 18 April 2003?

24 Skill scores intercomparison

25 Forecast time intercomparison

26 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

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

28 Liquid water content

29 Model intercomparison

30 Liquid water content ECMWF

31 Liquid water content Met Office mesoscale

32 Liquid water content DWD Lokal Modell

33 Ice water content Cirrus in situ measurements suggest we can obtain IWC from Z to a factor of two –Particles tend to be smaller at lower temperatures, so with additional use of temperature, error is reduced to -30%/+40% –Less accurate between - 10°C and 0°C because of strong aggregation Met Office C-130 aircraft data

34 Ice water content from Z and T Error in ice water content Retrieval flag Mostly retrieval error Mostly liquid attenuation correction error

35 Ice water Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI Regional Atmospheric Climate Model

36 Model intercomparison

37 Ice water content ECMWF

38 Additional Products Product list: Cloud fraction LWC –Liquid water content (linear scaled adiabatic method) –Liquid water content (Krasnov and Russchenberg, 2005) –Stratocumulus effective radius and number concentration: coming soon IWC –Ice water content – radar-temperature (Hogan et al., 2006) –Ice water content – RadOn (Delanoë et al., 2006 ) –Ice cloud properties ((Donovan et al. 2001; Tinel et al., 2005) –Ice cloud microphysics (van Zadelhoff et al., 2004) Turbulence –Turbulent kinetic energy (TKE) dissipation rate (Bouniol et al., 2003). Drizzle –Drizzle parameters below cloud base (OConnor et al., 2005). Occurrence, optical depth and thermodynamic phase of clouds from high- power lidar observations (Morille et al., 2006; Cadet et al., 2005; Noel et al., 2005)

39 Observing station Instruments –Doppler cloud radar: -50 dBZ at 1 km Pulsed or FMCW, 35 GHz (less attenuation) –Ceilometer –Dual-frequency microwave radiometer 23.8, 36.5 GHz Use ceilometer to help calibrate

40 Observing station Instruments –Doppler cloud radar -55 dBZ detects 80% of ice > % > dBZ detects 98% of ice > % > GHz (no attenuation in rain) –High power depolarization lidars high-altitude cloud statistics particle phase discrimination –Multi-frequency microwave radiometer HATPRO instrument

41 Conclusion –Objective scheme for combining radar, lidar, microwave radiometer and model data. –Cloudnet – compare forecast models and observations 4 remote-sensing sites (currently), 7 models (currently) provides yearly/monthly statistics for cloud fraction and ice/liquid water content including comparisons between observations and models. Soon: number concentration and size, drizzle properties. –Apply to long time series of ARM data and more models –Quicklooks/data available at

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

43 Important for vertical mixing, warm rain initiation in cumulus etc. Spectral width v contaminated by variations in particle fall speed Turbulence Changes in 1-s mean Doppler velocity dominated by changes in vertical wind, not terminal fall-speed

44 Turbulence Can generate pdfs of turbulence for different cloud types

45 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


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

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

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

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

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

52 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

53 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

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

55 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

56 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

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

58 Radar/lidar – ARM SGP

59 Target categorization Combining radar, lidar and model allows the type of cloud (or other target) to be identified. From this can calculate cloud fraction in each model gridbox.

60 Products Product list: Cloud fraction IWC LWC Turbulence Drizzle IWC from Z and temperature (Hogan et al. 2004)

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