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Robin Hogan Ewan OConnor Anthony Illingworth Nicolas Gaussiat Malcolm Brooks Cloudnet Evaluating the clouds in European forecast models.

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Presentation on theme: "Robin Hogan Ewan OConnor Anthony Illingworth Nicolas Gaussiat Malcolm Brooks Cloudnet Evaluating the clouds in European forecast models."— Presentation transcript:

1 Robin Hogan Ewan OConnor Anthony Illingworth Nicolas Gaussiat Malcolm Brooks Cloudnet Evaluating the clouds in European forecast models

2 Overview Motivation –Representation of clouds in GCMs About the Cloudnet project Cloud products –Instrument synergy and target categorization –Cloud fraction –Liquid water content –Ice water content Evaluation of models –Long-term means –Skill scores –PDFs

3 Representation of clouds in models Reality –Structure on all scales –3D interaction with radiation Typical GCM gridbox –Horizontal size: km (forecast model) ~300 km (climate model) –Vertical size: ~500 m –Holds cloud fraction & mean water content –Clouds assumed to be horizontally uniform –Cloud phase and particle size are usually functions of temperature How accurate is cloud fraction and water content in models? Height km ~500 m

4 Cloud water content in GCMs 14 global models (AMIP) 90N S Latitude Vertically integrated cloud water (kg m -2 ) But all models tuned to give about the same top-of- atmosphere radiation! Water content in models varies by factor of 10! Current satellites provide information at cloud top Need instrument with high vertical resolution…

5 Cloud feedback in models Increase in global average surface temperature due to increased greenhouse gases is fairly well understood But how would clouds change in a warmer world? –Less low cloud extra warming –Less high cloud less warming –More aerosol less warming Clouds in some models amplify the warming (up to factor of 2), others reduce it Also very different longwave and shortwave responses Amplification of climate change due to clouds (Cess et al 1996)

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

7 The EU Cloudnet project April 2001 – October 2005 Aim: to retrieve continuously the crucial cloud parameters for climate and forecast models –Three sites: Chilbolton (GB) Cabauw (NL) and Palaiseau (F) –Soon to include all the US and Tropical ARM sites + Lindenberg To evaluate a number of operational models –Met Office (mesoscale and global versions) –ECMWF –Météo-France (Arpege) –KNMI (Racmo and Hirlam) –Swedish RCA model (…Coming soon: German & Canadian models) Crucial aspects –Report retrieval errors and data quality flags –Use common formats based around NetCDF to allow all algorithms to be applied at all sites and compared to all models

8 The three Cloudnet sites Core instrumentation at each site: –Cloud radar, cloud 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)

9 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)

10 Level 0-1: observed quantities | Level 2-3: cloud products

11 The Instrument synergy/ Target categorization product Makes multi-sensor data much easier to use: –Combines radar, lidar, model, raingauge and -wave radiometer –Identical format –Identical format for each site (based around NetCDF) Performs common pre-processing tasks: –Interpolation on to the same grid –Ingest model data (many algorithms need temperature & wind) attenuation –Correct radar for attenuation (gas and liquid) Provides essential extra information: measurement errors –Random and systematic measurement errors sensitivity –Instrument sensitivity droplets/ice/aerosol/insects –Categorization of targets: droplets/ice/aerosol/insects etc. –Data quality flags: –Data quality flags: when are the observations unreliable?

12 Ice Liquid Rain Aerosol Insects 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

13 Example from US ARM site: Need to distinguish insects from cloud Target categorization Ice Liquid Rain Aerosol Insects 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

14 Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI RACMO Model Swedish RCA model Cloud fraction

15 Dual wavelength microwave radiometer 22 and 28 GHz optical depths sensitive to liquid water path (LWP) and water vapour path (WVP) –Coefficients assumed constant, calibration drifts significantly LWP - initial LWP - corrected Lidar observes no liquid cloud in profile Improve by adding lidar and model (Gaussiat et al.) –Coefficients calculated from cloud temperature information –Use lidar to recalibrate in clear skies when LWP should be zero

16 Liquid water content Cant use radar Z for LWC: often affected by drizzle –Simple alternative: lidar and radar provide cloud boundaries –Model temperature used to predict adiabatic LWC profile –Scale with LWP (entrainment often reduces LWC below adiabatic) Radar reflectivity Liquid water content Rain at ground: unreliable retrieval

17 Liquid water content comparison Observed ECMWF Met Office

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 is also available from KNMI radar/lidar algorithm

19 Ice water content from reflectivity and temperature Error in ice water content Retrieval flag Mostly retrieval error Mostly liquid attenuation correction error No retrieval: unknown attenuation in rain

20 Ice water Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI RACMO Model Swedish RCA Model

21 Cloud fraction - Met Office Mesoscale Sample level 3 output Commonly frequency of occurrence is OK but mean amount when present is wrong. UM has difficulty predicting 100% cloud fraction

22 Cloud fraction - Met Office Global

23 Cloud fraction – ECMWF Low cloud: Correct cloud amount when present but occurs to often, therefore mean cloud fraction too high. High cloud: Cloud occurrence correct but not thick enough.

24 LWC - Met Office Mesoscale Frequency of occurrence is again OK but amount when present too low BL height too low? Supercooled liquid water occurrence is much too low (kg m -3 )

25 LWC – Met Office Global (kg m -3 )

26 LWC – ECMWF Mean LWC good but contained in too many overly tenuous clouds Too many low LWC values (kg m -3 )

27 IWC - Met Office Mesoscale (kg m -3 ) Mean ice water content somewhat too high Need to be careful due to radar sensitivity and because retrievals not carried out in rain

28 IWC - ECMWF (kg m -3 )

29 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

30 Equitable threat score From now on we use Equitable Threat Score with threshold of 0.1

31 Equitable threat score Cabauw Equitable threat score Cabauw mean cloud fraction Chilbolton Equitable threat score Chilbolton mean cloud fraction Change in Météo France cloud scheme April 2003 Note that cloud fraction and water content in this model are entirely diagnostic Cabauw Equitable Threat Score Definition: ETS = (A-E)/(A+B+C-E) 1 = perfect forecast, 0 = random forecast

32 Skill versus height 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 Potential for testing: –New model parameterisations –Global versus mesoscale versions of the Met Office model Occurrence of cloud fraction > 0.1

33 Other Cloudnet products Radar/lidar drizzle flux and drizzle drop size –Crucial for lifetime of stratocumulus in climate models Radar/lidar ice particle size and optical depth Turbulent kinetic energy dissipation rate Incorporate US ARM data into Cloudnet analysis –Agreed recently at new GEWEX working group: will enable these algorithms to be applied in tropical and polar climates Lots of work still to do in evaluating models! Quicklooks and further information may be found at:www.met.rdg.ac.uk/radar/cloudnet


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