Anthony Illingworth, + Robin Hogan, Ewan OConnor, U of Reading, UK and the CloudNET team (F, D, NL, S, Su). Reading: 19 Feb 08 – Meeting with Met office.

Anthony Illingworth, + Robin Hogan , Ewan O’Connor, U of Reading, UK

and the CloudNET team (F, D, NL, S, Su).

Reading: 19 Feb 08 – Meeting with Met office.

Evaluation of the representation of clouds in NWP using ground based radar and

lidar: The Cloudnet Project.

The Cloudnet methodologyRecently completed EU project; www.cloud-net.org

BAMS Article June 2007

• Aim: to retrieve and evaluate the crucial cloud variables in forecast and climate models– Models: Met Office (4-km, 12-km and global), ECMWF, Météo-

France, KNMI RACMO, Swedish RCA model, DWD– Variables: target classification, cloud fraction, liquid water content,

ice water content, drizzle rate, mean drizzle drop size, ice effective radius, TKE dissipation rate

– Sites: 4 Cloudnet sites in Europe, 6 ARM including 3 for mobile facility

– Period: Several years near-continuous data from each site

• Crucial aspects– Common formats (including errors & data quality flags) allow all

algorithms to be applied at all sites to evaluate all models– Evaluate for months and years: avoid unrepresentative case studies

Standard CloudNET observations (e.g. Chilbolton)Radar Lidar, gauge, radiometers

But can the average user make sense of these

measurements?

Example fromUS ARM site:Need todistinguishinsects fromcloud

First step: target classification

Ice

LiquidRainAerosol 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

CHILBOLTON

Observations

Met Office

Mesoscale Model

ECMWF

Global Model

Meteo-France

ARPEGE Model

KNMI

RACMO Model

Swedish RCA model

Cloud fraction

Cloud fraction in 7 models• Mean & PDF for 2004 for Chilbolton, Paris and Cabauw

Illingworth et al, BAMS, 2007

0-7 km

– Uncertain above 7 km as must remove undetectable clouds in model

– All models except DWD underestimate mid-level cloud; some have separate “radiatively inactive” snow (ECMWF, DWD); Met Office has combined ice and snow but still underestimates cloud fraction

– Wide range of low cloud amounts in models– Not enough overcast boxes.

A change to Meteo-France cloud scheme

But human obs. indicate model now underestimates mean cloud-cover! Compensation of errors: overlap scheme changed from random to maximum-random

• Compare cloud fraction to observations before and after April 2003• Note that cloud fraction and water content are entirely diagnostic

before after

April 2003

ARM MOBILE 2007: MURGTAL , GERMANY.

140 days Cloud fraction Met Office Model

GOOD, but amount of mid level cloud too low

Equitable threat score

– ETS removes those hits that occurred by chance– 1 = perfect forecast, 0 = random forecast

• Measure of the skill of forecasting cloud fraction>0.05– Assesses the weather of the model not its climate– Persistence forecast is shown for comparison

• Lower skill in summer convective events• Met Office global and mesoscale – equally good.

Ice waterfrom Z

Observations

Met Office

Mesoscale Model

ECMWF

Global Model

Meteo-France

ARPEGE

Model

KNMI Regional

Atmospheric

Climate Model

Ice water content• IWC estimated from radar reflectivity and temperature

– Rain events excluded from comparison due to mm-wave attenuation– For IWC above rain, use cm-wave radar (e.g. Hogan et al., JAM, 2006)

3-7 km

– ECMWF and Met Office within the observational errors at all heights

– Encouraging: AMIP implied an error of a factor of 10!

– DWD (pink) far too low

- Be careful in interpretation: mean IWC dominated by occasional large values so PDF more relevant for radiative properties

- DWD (pink) pdf best – apart from max bin – so mean value worst.

To Do List.

• Classification of skill as a function of ascent/descent at 300mb, 700mb and Surface Stability (Malcolm Brooks et al 20??)

(‘Bony’ diagrams now very fashionable)• Does the 4km model have better skill for clouds than

the NAE 12km or the global? (What about the 1.5km)?

• Light drizzle falls nearly all the time from low level water clouds (should be more episodic) – also it reaches the ground where it should evaporate.

GLOBAL MODEL

CLOUD FRACTION

2006

MESOSCALE

MODEL

ONE YEAR2003

SPOT THE DIFFERENC

E

OCCURRENCE OF DRIZZLEYearly comparisons

– Met Office GLOBAL MODEL - Broken Contours– OBSERVATIONS – GREY SCALE AND SOLID CONTOURS

0.4

.004

mm/hr

OCCURRENCE OF DRIZZLEYearly comparisons

– Met Office mesoscale model – DOTTED CONTOURS– OBSERVATOINS GREY SCALE AND SOLID CONTOURS.

Drizzle drop size– Met Office model uses explicit size distributions– Treats all precipitation as marsahll-Palmer rain– Drizzle drops too big – so they don’t evaporate.

To Do List.

• Classification of skill as a function of ascent/descent at 300mb, 700mb and Surface Stability (Malcolm Brooks et al 20??)

(‘Bony’ diagrams now very fashionable)• Does the 4km model have better skill for clouds than

the NAE 12km or the global? (What about the 1.5km)?

• Light drizzle falls nearly all the time from low level water clouds (should be more episodic) – also it reaches the ground where it should evaporate.

ARM 2006: Niamey - Ice Water Content

ECMWF

Met Office

Observations June 2006

Weak convectionBut too often.

Niamey

Ice Water Content

Met OfficeGlobal model

GOOD, but pdf of IWC too peaked For midlevel cloud.

Summary

• Cloud radar and lidar sites worldwide• Cloud evaluation over Europe and ARM

– Rapid feedback (2 months) on model performance when changes in in parameterisation schemes.

– IWC and LWC profiles surprisingly good

– BUT:– Not enough mid level cloud.– Problems with supercooled clouds.– North slope: ECMWF, lwp too low: 60% low cloud, observed

20%; – Niamey: ECMWF low cloud fraction too low but too frequently. – We would welcome participation of US models.

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 TRIGGERS INTERMITTENTLY ALL THE TIME - GIVES V LOW CLOUD FRACTION IN TOO MANY BOXES.

OBSERVED – HIGH CIRRUS NOT YET CORRECT IN CLOUDNET

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.

Liquid water path: microwave radiometer+lidar

– Brightness temperatures -> Liquid water path• Use lidar to determine whether clear sky or not• Adjust coefficients to account for instrument drift• Removes offset for low LWP

LWP - initialLWP - lidar corrected

Compare measured lwp to adiabatic lwp • obtain ‘dilution coefficient’

Dilution coefficient versus depth of cloud

LIQUID WATER CONTENT (LWC) OF STRATOCUMULUS: Cloud base from lidar: Cloud top from radar.Use model temperatures to give adiabatic lwc.Scale adiabatic lwc profile to match lwp from radiometers

2004 – model performance for Sc

ECMWF Met Office

ECMWF: Mean LWC OK but occurs to often

MET OFFICE pdf of LWP wrong occurrence ok