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

Cloudnet

The EU Cloudnet project

Development of a European pilot network of stations for observing cloud profiles

• Scientific objectives1. 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.

CloudnetCabauw,The Netherlands

Chilbolton, UK SIRTA, Palaiseau (Paris), France

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

• Core instrumentation at each site– Radar, lidar, microwave radiometers, raingauge

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

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?

Standard CloudNET observations (e.g. Chilbolton)Radar Lidar, gauge, radiometers

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~D6

Sensitive to large particles (ice, drizzle)

Lidar: ~D2

Sensitive to small particles

(droplets, aerosol)

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?

Measurements

Measurements

Measurements

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 - initialLWP - lidar corrected

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

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!

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

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

Cloud fractionObservations

ECMWF

meso

global

Météo France

RACMO

SMHI RCA

Met Office

Model intercomparis

on

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

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

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.

Modification of cloud scheme – cloud fraction and water content now diagnosed from total water content.

Cloud fractionWhat happened to the Meteo France ARPEGE model on 18 April 2003?

Skill scores intercomparis

on

Forecast time intercomparis

on

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/

Compare measured lwp to adiabatic lwp

• obtain ‘dilution coefficient’

Dilution coefficient versus depth of cloud

Liquid water content

Model intercomparis

on

Liquid water content ECMWF

Liquid water content

Met Office mesoscale

Liquid water content

DWD Lokal Modell

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

• Ice water content

from Z and T

• Error in ice water content

• Retrieval flag

Mostly retrieval error

Mostly liquid attenuation correction error

Ice water

Observations

Met Office

Mesoscale Model

ECMWF

Global Model

Meteo-France

ARPEGE

Model

KNMI Regional

Atmospheric

Climate Model

Model intercomparis

on

Ice water content ECMWF

Additional ProductsProduct list:

Cloud fractionLWC– Liquid water content (linear scaled adiabatic method)– Liquid water content (Krasnov and Russchenberg, 2005)– Stratocumulus effective radius and number concentration: coming

soonIWC– 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)

Observing stationInstruments

– 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

Observing stationInstruments

– 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

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/

Turbulence30-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/

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

TurbulenceCan generate pdfs of turbulence for different cloud types

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

Drizzle below cloudDoppler radar and lidar - 4 observables (O’Connor et al. 2005)

• Radar/lidar ratio provides information on particle size

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

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

Profiles of lwc – no drizzleExamine radar/lidar profiles - retrieve LWC, N, D

Profiles of lwc – no drizzle

260 cm-3 90 cm-3 80 cm-3

Consistency shown between LWP estimates.

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

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

Mixing ratio comparison 11 Nov 2001

Ramanlidar

UnifiedModel,Mesoscaleversion

Cloud

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

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 UTC 15 UTC

1.6 km

0.2 km

0.8 km

Radiosonde

Smith (1990) triangular PDF

scheme

Satellite measurements

Icesat – lidar profiles

Modis – LWP (imager)

Radar/lidar – ARM SGP

Target categorizationCombining 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.

Products

Product list:Cloud fractionIWCLWCTurbulenceDrizzle

IWC from Z and temperature (Hogan et al. 2004)