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CloudNET: evaluating the clouds in seven operational forecast models

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

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CloudNET: evaluating the clouds in seven operational forecast models

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  1. 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 David Donovan, Gerd-Jan Zadelhoff, Henk Klein-Baltink KNMI, NL Adrian Tomkins, ECMWF, Charles Wrench, RAL Herman Russchenberg, Oleg Krasnov TUD, NL Jean-M Piriou Meteo France Pekka Ravilla, Vaisala, Finland. et al.

  2. The EU CloudNet project Since April 2001www.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 models COULD USE THE APPROACH FOR CLOUDSAT/CALIPSO GLOBAL DATA www.cloud-net.org

  3. The three original CloudNET sites • Core instrumentation at each site • Radar, lidar, microwave radiometers, raingauge Cabauw, The Netherlands 1.2-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) SIRTA, Palaiseau (Paris), France 5-GHz Doppler Radar (Ronsard) 94-GHz Doppler Radar (Rasta) 1064/532 nm polarimetric lidar 10.6 µm Scanning Doppler Lidar 24/37-GHz radiometer (DRAKKAR) 23.8/31.7-GHz radiometer (RESCOM) Chilbolton, UK 3-GHz Doppler/polarisation radar (CAMRa) 94-GHz Doppler cloud radar (Galileo) 35-GHz Doppler cloud radar (Copernicus) 905-nm lidar ceilometer 355-nm UV lidar 22.2/28.8 GHz dual frequency radiometer

  4. 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?

  5. Standard CloudNET observations (e.g. Chilbolton) Radar Lidar, gauge, radiometers But can the average user make sense of these measurements?

  6. 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

  7. Cloud fraction Observations OCTOBER 2003 Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI Regional Atmospheric Climate Model

  8. 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.

  9. Evaluation of Meteo-France ‘Arpege’ total cloud cover using conventional synoptic observations.  More rms Error Worse Bias  2000 2005 2000 2005 Changes to cloud scheme in 2003-2005 seem to have made performance worse!

  10. CloudNET: monthly profiles of mean cloud fraction and pdf of values of cloud fraction vmodel Jan 2003 Jan 2005 Objective CloudNET analysis shows a remarkable improvement in model clouds.

  11. Equitable threat scores for cloud fraction • Scores for cloud fraction > 0.05 over Cabauw for seven models together with persistence and climatology.

  12. Skill versus forecast lead time • Met Office best over Chilbolton • DWD best over Lindenberg.

  13. ARM SITES NOW BEING PROCESSED VIA CLOUDNET SYSTEMMANUS ARM SITE IN W PACIFIC. CLOUD FRACTION CEILOMETER ONLY: HIGH CIRRUS IS OBSERVED BY MPL LIDAR: NOT YET CORRECT IN CLOUDNET

  14. TROPICAL CONVECTION: MANUS ARM SITE IN W PACIFIC. CLOUD FRACTION OBSERVED – HIGH CIRRUS NOT YET CORRECT IN CLOUDNET ECMWF MODEL - MODEL CONVECTION SCHEME CONTINUALLY TRIGGERING - GIVES V LOW CLOUD FRACTION IN TOO MANY BOXES.

  15. 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.

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