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

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 Office

Mesoscale Model

ECMWF

Global Model

Meteo-France

ARPEGE

Model

KNMI Regional

Atmospheric

Climate Model

Cloud fraction

Ice water

Observations

Met Office

Mesoscale Model

ECMWF

Global Model

Meteo-France

ARPEGE

Model

KNMI Regional

Atmospheric

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-sky

A: 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