robin hogan ewan oconnor anthony illingworth nicolas gaussiat malcolm brooks cloudnet evaluating the...
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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
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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
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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
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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…
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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
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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)
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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)
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Level 0-1: observed quantities | Level 2-3: cloud products
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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?
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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
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Observations
Met Office
Mesoscale Model
ECMWF
Global Model
Meteo-France
ARPEGE Model
KNMI
RACMO Model
Swedish RCA model
Cloud Cloud fractionfraction
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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
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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
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• 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
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Ice waterIce water
Observations
Met Office
Mesoscale Model
ECMWF
Global Model
Meteo-France
ARPEGE Model
KNMI
RACMO Model
Swedish
RCA Model
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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
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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)
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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
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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
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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
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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
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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