cloudnet: evaluating the clouds in seven operational forecast models

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.