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Cloudnet

Cloudnet. Ewan O’Connor, Anthony Illingworth, Robin Hogan and the Cloudnet team. The EU Cloudnet project. Development of a European pilot network of stations for observing cloud profiles Scientific objectives

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Cloudnet

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  1. Cloudnet Ewan O’Connor, Anthony Illingworth, Robin Hogan and the Cloudnet team

  2. The EU Cloudnet project Development of a European pilot network of stations for observing cloud profiles • Scientific objectives • 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 SIRTA, Palaiseau (Paris), France Chilbolton, UK http://www.cloud-net.org/ • 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) Radar Lidar, gauge, radiometers

  7. Basics of radar and lidar Penetrates ice cloud Detects cloud top Strong echo from liquid clouds Detects cloud base Radar: Z~D6 Sensitive to large particles (ice, drizzle) Lidar: b~D2 Sensitive to small particles (droplets, aerosol) Radar/lidar ratio provides information on particle size

  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?

  9. Measurements

  10. Measurements

  11. Measurements

  12. 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

  13. 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

  14. 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!

  15. 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

  16. Cloud fraction Model gridboxes • 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

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

  18. Model intercomparison

  19. 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

  20. 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

  21. 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.

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

  23. Skill scores intercomparison

  24. Forecast time intercomparison

  25. 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 http://www.met.rdg.ac.uk/radar/cloudnet/quicklooks/

  26. Compare measured lwp to adiabatic lwp • obtain ‘dilution coefficient’ Dilution coefficient versus depth of cloud

  27. Liquid water content

  28. Model intercomparison

  29. Liquid water content ECMWF

  30. Liquid water content Met Office mesoscale

  31. Liquid water contentDWD Lokal Modell

  32. Ice water content Met Office C-130 aircraft data • 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

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

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

  35. Model intercomparison

  36. Ice water content ECMWF

  37. 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 (O’Connor 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)

  38. 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

  39. Observing station Instruments • Doppler cloud radar • -55 dBZ detects 80% of ice  > 0.05 97%  > 0.1 • -60 dBZ detects 98% of ice  > 0.05 100%  > 0.1 • 10 GHz (no attenuation in rain) • High power depolarization lidars • high-altitude cloud statistics • particle phase discrimination • Multi-frequency microwave radiometer • HATPRO instrument

  40. 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 http://www.cloud-net.org/

  41. Turbulence 30-s standard deviation of 1-s radar velocities, plus wind speed, gives eddy dissipation rate (Bouniol et al. 2003) http://www.met.rdg.ac.uk/radar/cloudnet/quicklooks/

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

  43. Turbulence Can generate pdfs of turbulence for different cloud types

  44. 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 mm • 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

  45. Drizzle below cloud Doppler radar and lidar - 4 observables(O’Connor et al. 2005) • Radar/lidar ratio provides information on particle size

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

  47. Drizzle below cloud • Typical cell size is about 2-3 km • Updrafts correlate well with liquid water flux

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

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

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