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Robin HoganEwan O’Connor
Anthony IllingworthNicolas GaussiatMalcolm Brooks
CloudnetCloudnetEvaluating the clouds in Evaluating the clouds in
European forecast modelsEuropean forecast models
OverviewOverview• Motivation
– Representation of clouds in GCMs
• About the Cloudnet project• Cloud products
– Instrument synergy and target categorization– Cloud fraction– Liquid water content– Ice water content
• Evaluation of models– Long-term means– Skill scores– PDFs
Representation of clouds in Representation of clouds in modelsmodels
• Reality– Structure on all scales– 3D interaction with radiation
• Typical GCM gridbox– Horizontal size: 1-100 km (forecast model)
~300 km (climate model)– Vertical size: ~500 m– Holds cloud fraction & mean water content– Clouds assumed to be horizontally uniform– Cloud phase and particle size are usually
functions of temperature
• How accurate is cloud fraction and water content in models?
Heig
ht
1-300 km
~500 m
Cloud water content in GCMsCloud water content in GCMs
14 global models (AMIP)
90N 80 60 40 20 0 -20 -40 -60 -80 90S
0.05
0.10
0.15
0.20
0.25
Latitude
Ver
tical
ly in
tegr
ated
clo
ud w
ater
(kg
m-2) But all
models tuned to give about the same top-of-atmosphere radiation!
• Water content in models varies by factor of 10!• Current satellites provide information at cloud top• Need instrument with high vertical resolution…
The EU Cloudnet projectThe EU Cloudnet projectApril 2001 – October 2005April 2001 – October 2005
• Aim: to retrieve continuously the crucial cloud parameters for climate and forecast models– Three sites: Chilbolton (GB) Cabauw (NL) and Palaiseau (F)– Soon to include all the US and Tropical ARM sites + Lindenberg
• To evaluate a number of operational models– Met Office (mesoscale and global versions)– ECMWF– Météo-France (Arpege)– KNMI (Racmo and Hirlam)– Swedish RCA model (…Coming soon: German & Canadian
models)
• Crucial aspects– Report retrieval errors and data quality flags– Use common formats based around NetCDF to allow all
algorithms to be applied at all sites and compared to all models
The three Cloudnet sitesThe three Cloudnet sites
• Core instrumentation at each site:– Cloud radar, cloud lidar, microwave radiometers, raingauge
Cabauw, The Netherlands1.2-GHz wind profiler + RASS (KNMI)3.3-GHz FM-CW radar TARA (TUD)35-GHz cloud radar (KNMI)1064/532-nm lidar (RIVM)905 nm lidar ceilometer (KNMI)22-channel MICCY radiometer (Bonn)IR radiometer (KNMI)
Chilbolton, UK3-GHz Doppler/polarisation radar (CAMRa)94-GHz Doppler cloud radar (Galileo)35-GHz Doppler cloud radar (Copernicus)905-nm lidar ceilometer355-nm UV lidar22.2/28.8 GHz dual frequency radiometer
SIRTA, Palaiseau (Paris), France5-GHz Doppler Radar (Ronsard)94-GHz Doppler Radar (Rasta)1064/532 nm polarimetric lidar10.6 µm Scanning Doppler Lidar24/37-GHz radiometer (DRAKKAR)23.8/31.7-GHz radiometer (RESCOM)
Basics of radar and lidarBasics 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)
Level 0-1: observed quantities | Level 2-3: cloud products
The Instrument synergy/The Instrument synergy/Target categorization Target categorization
product product • Makes multi-sensor data much easier to use:
– Combines radar, lidar, model, raingauge and -wave radiometer– Identical formatIdentical format for each site (based around NetCDF)
• Performs common pre-processing tasks:– Interpolation on to the same grid– Ingest model data (many algorithms need temperature & wind)– Correct radar for attenuationattenuation (gas and liquid)
• Provides essential extra information:– Random and systematic measurement errorsmeasurement errors– Instrument sensitivitysensitivity– Categorization of targets: droplets/ice/aerosol/insectsdroplets/ice/aerosol/insects etc. – Data quality flags:Data quality flags: when are the observations unreliable?
Example fromUS ARM site:Need todistinguishinsects fromcloud
Target categorizationTarget categorization
Ice
LiquidRainAerosol Insects
• 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
Observations
Met Office
Mesoscale Model
ECMWF
Global Model
Meteo-France
ARPEGE Model
KNMI
RACMO Model
Swedish RCA model
Cloud Cloud fractionfraction
Dual wavelength microwave Dual wavelength microwave radiometerradiometer
• 22 and 28 GHz optical depths sensitive to liquid water path (LWP) and water vapour path (WVP)
– Coefficients assumed constant, calibration drifts significantly
LWP - initialLWP - corrected
22222222 WVPLWP cba 28282828 WVPLWP cba
Lidar observes noliquid cloud in profile
• Improve by adding lidar and model (Gaussiat et al.)– Coefficients calculated from cloud temperature information – Use lidar to recalibrate in clear skies when LWP should be
zero
Liquid water contentLiquid water content• Can’t use radar Z for LWC: often affected by drizzle
– Simple alternative: lidar and radar provide cloud boundaries– Model temperature used to predict “adiabatic” LWC profile– Scale with LWP (entrainment often reduces LWC below
adiabatic)
Radar reflectivity
Liquid water contentRain at ground:
unreliable retrieval
• Ice water content from reflectivity and temperature
• Error in ice water content
• Retrieval flag
Mostly retrieval error
Mostly liquid attenuation correction error
No retrieval: unknown attenuation in rain
Ice waterIce water
Observations
Met Office
Mesoscale Model
ECMWF
Global Model
Meteo-France
ARPEGE Model
KNMI
RACMO Model
Swedish
RCA Model
Cloud fraction - Met Office Cloud fraction - Met Office MesoscaleMesoscaleSample level
3 output
Commonly frequency of occurrence is OK but mean amount when present is wrong.
UM has difficulty
predicting 100% cloud
fraction
LWC - Met Office MesoscaleLWC - Met Office Mesoscale
Frequency of occurrence is again OK but amount when present too low
BL height too low?
Supercooled liquid water occurrence is much too low
(kg m-3) (kg m-3)
IWC - Met Office MesoscaleIWC - Met Office Mesoscale
(kg m-3) (kg m-3)
Mean ice water content somewhat too high
Need to be careful due to radar sensitivity and because retrievals not carried out in rain
Model cloud
Model clear-sky
A: Cloud hit B: False alarm
C: Miss D: Clear-sky hit
Observed cloud Observed clear-sky
Comparison with Met Officemodel over ChilboltonOctober 2003
Contingency tablesContingency tables
Equitable Equitable threat threat scorescore
Change in Météo France cloud scheme April 2003Note that cloud fraction and water content in this model are entirely diagnostic
• Definition: ETS = (A-E)/(A+B+C-E)• 1 = perfect forecast, 0 = random forecast
Skill Skill versus versus heightheight
• Model performance:– ECMWF, RACMO, Met Office models perform similarly– Météo France not so well, much worse before April 2003– Met Office model significantly better for shorter lead time
• Potential for testing:– New model parameterisations– Global versus mesoscale versions of the Met Office model
Other Cloudnet productsOther Cloudnet products
• Radar/lidar drizzle flux and drizzle drop size– Crucial for lifetime of stratocumulus in climate models
• Radar/lidar ice particle size and optical depth• Turbulent kinetic energy dissipation rate• Incorporate US ARM data into Cloudnet analysis
– Agreed recently at new GEWEX working group: will enable these algorithms to be applied in tropical and polar climates
• Lots of work still to do in evaluating models!
Quicklooks and further information may be found at:
www.met.rdg.ac.uk/radar/cloudnetwww.met.rdg.ac.uk/radar/cloudnet