1. 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. CloudNET: evaluating the clouds in seven operational forecast models

2. 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 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) 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 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)

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

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. Changes to cloud scheme in 2003-2005 seem to have made performance worse!  More rms Error Worse Bias  2000 2005

10. 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.

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 SYSTEM MANUS 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 ECMWF MODEL - MODEL CONVECTION SCHEME CONTINUALLY TRIGGERING - GIVES V LOW CLOUD FRACTION IN TOO MANY BOXES. OBSERVED – HIGH CIRRUS NOT YET CORRECT IN CLOUDNET

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.