cloudnet products available from chilbolton
DESCRIPTION
Cloudnet products available from Chilbolton. Robin Hogan Anthony Illingworth Ewan O’Connor Nicolas Gaussiat Malcolm Brooks University of Reading. Motivation. Clouds are crucial for weather & climate forecasting but their representation in models needs testing In this talk - PowerPoint PPT PresentationTRANSCRIPT
Robin HoganAnthony IllingworthEwan O’ConnorNicolas GaussiatMalcolm BrooksUniversity of Reading
Cloudnet products available from Chilbolton
MotivationClouds are crucial for weather & climate forecasting
but their representation in models needs testing
In this talk• Chilbolton cloud observations held by BADC• About the EU Cloudnet project• Radar and lidar basics• Instrument synergy/target categorization
– Facilitates implementation of the algorithms• A few of the products and model comparisons
– Target classification: ice/liquid, cloud/precipitation etc.– Cloud fraction– Ice water content
Standard Chilbolton observations at BADCRadar Lidar, gauge, radiometers
But can the average user make sense of these
measurements?
The EU CloudNet projectApril 2001 – April 2004
• Aim: to retrieve continuously the crucial cloud parameters for climate and forecast models– Three sites: Chilbolton (GB) Cabauw (NL) and Palaiseau (F)
• To evaluate a number of operational models– Met Office (mesoscale and global versions)– ECMWF– Météo-France (Arpege)– KNMI (Racmo and Hirlam)
• 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
The three 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)
Basics 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)
Cloudnet processing chain
The Instrument synergy/Target categorization
product • Makes multi-sensor data much easier to use:
– Combines radar, lidar, model, raingauge and -wave radiometer– Identical format for each site
• Performs many common pre-processing tasks:– Interpolation on to the same grid– Ingest model data (many algorithms need temperature & wind) – Correction of radar for gaseous attenuation (using model
humidity) and liquid attenuation (using -wave LWP and lidar)– Quantify random and systematic measurement errors– Quantify instrument sensitivity– Categorization of atmospheric targets: does my algorithm work
with this target/hydrometeor type?– Data quality: are the data reliable enough for my algorithm?
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
• Ice water content from reflectivity and temperature
• Error in ice water content
• Retrieval flag
Mostly retrieval error
Mostly liquid attenuation correction error
Observations
Met OfficeMesoscale
Model
ECMWFGlobal Model
Meteo-FranceARPEGE
Model
KNMI RegionalAtmospheric
Climate Model
Cloud fraction
Ice water Observations
Met OfficeMesoscale
Model
ECMWFGlobal Model
Meteo-FranceARPEGE
Model
KNMI RegionalAtmospheric
Climate Model
Comparison of mean cloud fraction and ice water content• One year of data from Chilbolton
IWC distributions
• The Met Office Unified Model tends to simulate very high and very low ice water contents too infrequently
High cloud
Mid-level
ObservationsUnified Model
Cloud fraction
skill score
• 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
Other Cloudnet products• Radar/lidar ice water content and particle size
– KNMI algorithm: restricted to clouds penetrated by lidar, but more accurate than IWC from radar alone
• Radar/lidar drizzle flux and drizzle drop size– Important for lifetime of stratocumulus in climate models
• Cloud phase (part of target categorization product)– Important for cloud radiative properties: details later today
• Turbulent dissipation rate, dual-wavelength radar liquid water content and ice products– Details later today
Visit our web site at www.met.rdg.ac.uk/radar/cloudnet
Cloud fraction– Radar
provides first guess of cloud fraction in each model gridbox
Lidar refines the estimate by
removing drizzle beneath
stratocumulus and adding thin
liquid clouds (warm and
supercooled) that the radar
does not detect
Model gridboxes
Ice water content from cloud radar
• Cirrus in situ measurements suggest we can obtain IWC from Z and temperature to to a factor of two -30%/+40%
Met Office aircraft data
IWC also available from KNMI radar/lidar algorithm
Model cloud
Model clear-skyA: Cloud hit B: False alarm
C: Miss D: Clear-sky hit
Observed cloud Observed clear-sky• Comparison with Met Office model over Chilbolton, October 2003
Contingency tables
Skill versus
time
• Cabauw Equitable threat score
• Cabauw mean cloud fraction
• Chilbolton Equitable threat score
• Chilbolton mean cloud fraction
Change in Météo France cloud scheme April 2003