Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet.
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Transcript of Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet.
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
• 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)