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CloudNET: evaluating the clouds in seven operational forecast models. Anthony Illingworth, Robin Hogan , Ewan O’Connor, U of Reading, UK Nicolas Gaussiat Damian Wilson, Malcolm Brooks Met Office, UK Dominique Bouniol, Alain Protat Martial Haeffelin , CETP, France - PowerPoint PPT Presentation
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Anthony Illingworth, Robin Hogan , Ewan O’Connor, U of Reading, UKNicolas Gaussiat Damian Wilson, Malcolm Brooks Met Office, UKDominique Bouniol, Alain Protat Martial Haeffelin, CETP, FranceDavid Donovan, Gerd-Jan Zadelhoff, Henk Klein-Baltink KNMI, NLAdrian Tomkins, ECMWF, Charles Wrench, RALHerman Russchenberg, Oleg Krasnov TUD, NLJean-M Piriou Meteo FrancePekka Ravilla, Vaisala, Finland. et al.
CloudNET: evaluating the clouds in seven operational
forecast models
The EU CloudNet project Since April 2001
www.met.rdg.ac.uk/radar/cloudnet
• Aim: to retrieve continuously the crucial cloud parameters for climate and forecast models– Three sites: Chilbolton (UK) Cabauw (NL) and Palaiseau (F)– + recently Lindenberg (D) and ARM sites (USA & Pacific)
• To evaluate a number of operational models– Met Office (mesoscale and global versions)– ECMWF - Météo-France (Arpege)– KNMI (Racmo and Hirlam)– + recently: DWD Lokal Model and SMHI RCA model
• Crucial aspects– Report retrieval errors and data quality flags– Use common formats based around NetCDF allow all algorithms
to be applied at all sites and compared to all modelsCOULD USE THE APPROACH FOR CLOUDSAT/CALIPSO GLOBAL DATA
www.cloud-net.org
The three original CloudNET sites
• Core instrumentation at each site– Radar, 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)
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
But can the average user make sense of these
measurements?
Target categorization• 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
OCTOBER 2003
Met Office
Mesoscale Model
ECMWF
Global Model
Meteo-France
ARPEGE
Model
KNMI Regional
Atmospheric
Climate Model
Cloud fraction
What happened to the MeteoFrance Arpege model on 18 April 2003?
Modification of cloud scheme – cloud fraction and water content now diagnosed from total water content.
Evaluation of Meteo-France ‘Arpege’ total cloud cover using conventional synoptic observations.
Changes to cloud scheme in 2003-2005 seem to have made performance worse!
More rmsError
Worse Bias
2000 2005 2000 2005
CloudNET: monthly profiles of mean cloud fraction and pdf of values of cloud fraction v model Jan 2003 Jan 2005
Objective CloudNET analysis shows a remarkable improvement in model clouds.
Equitable threat scores for cloud fraction
• Scores for cloud fraction > 0.05 over Cabauw for seven models together with persistence and climatology.
Skill versus forecast lead time
• Met Office best over
Chilbolton
• DWD best over Lindenberg.
ARM SITES NOW BEING PROCESSED VIA CLOUDNET SYSTEM
MANUS ARM SITE IN W PACIFIC. CLOUD
FRACTION
CEILOMETER ONLY: HIGH CIRRUS IS OBSERVED BY MPL LIDAR: NOT YET CORRECT IN CLOUDNET
TROPICAL CONVECTION: MANUS ARM SITE IN W PACIFIC.
CLOUD FRACTION
ECMWF MODEL - MODEL CONVECTION SCHEME CONTINUALLY TRIGGERING - GIVES V LOW CLOUD FRACTION IN TOO MANY BOXES.
OBSERVED – HIGH CIRRUS NOT YET CORRECT IN CLOUDNET
TODAY’S TIMETABLE• CLOUD OBSERVING STATIONS.
• RETRIEVAL ALGORITHMS
• Lunch
• COMPARISON WITH THE OPERATIONAL MODELS.
• MODELLER’S PERSPECTIVE AND GENERAL DISCUSSION.
• SPECIFICATION FOR A CLOUD OBSERVING STATION.