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Robin Hogan Anthony Illingworth Ewan OConnor Nicolas Gaussiat Malcolm Brooks University of Reading Cloudnet products available from Chilbolton.

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Presentation on theme: "Robin Hogan Anthony Illingworth Ewan OConnor Nicolas Gaussiat Malcolm Brooks University of Reading Cloudnet products available from Chilbolton."— Presentation transcript:

1 Robin Hogan Anthony Illingworth Ewan OConnor Nicolas Gaussiat Malcolm Brooks University of Reading Cloudnet products available from Chilbolton

2 Motivation Clouds 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

3 Standard Chilbolton observations at BADC RadarLidar, gauge, radiometers But can the average user make sense of these measurements?

4 The EU CloudNet project April 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

5 The three 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)

6 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~D 6 Sensitive to large particles (ice, drizzle) Lidar: ~D 2 Sensitive to small particles (droplets, aerosol)

7 Cloudnet processing chain

8 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?

9 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

10 Ice water content from reflectivity and temperature Error in ice water content Retrieval flag Mostly retrieval error Mostly liquid attenuation correction error

11 Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI Regional Atmospheric Climate Model Cloud fraction

12 Ice water Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI Regional Atmospheric Climate Model

13 Comparison of mean cloud fraction and ice water content One year of data from Chilbolton

14 IWC distributions The Met Office Unified Model tends to simulate very high and very low ice water contents too infrequently High cloud Mid-level Observations Unified Model

15 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

16 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

17 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

18 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

19 Model cloud Model clear-sky A: Cloud hitB: False alarm C: MissD: Clear-sky hit Observed cloud Observed clear-sky Comparison with Met Office model over Chilbolton, October 2003 Contingency tables

20 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


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