Cloud Validation: The issuesCloud Validation: The issues

• AIM: To perfectly simulate one aspect of nature: CLOUDS• APPROACH: Validate the model generated clouds against

observations, and to use the information concerning apparent errors to improve the model physics, and subsequently the cloud simulation

Cloud observations

Cloud simulation

error parameterisation improvements

sounds easy?

Cloud Validation: The Cloud Validation: The problemsproblems• How much of the ‘error’ derives from observations?

Cloud observations error = 1

Cloud simulation error = 2

error parameterisation

improvements

Cloud Validation: The Cloud Validation: The problemsproblems• Which Physics is responsible for the error?

Cloud observations

Cloud simulation

error parameterisation

improvements

radiation

convectioncloud

physicsdynamics

turbulence

Model climate - Model climate - Broadband radiative fluxesBroadband radiative fluxes

JJA 87

TSR

CY18R6-ERBE

Stratocumulus regions bad - also North Africa (old cycle!)

Can compare Top of Atmosphere (TOA) radiative fluxes with satellite observations: TOA Shortwave radiation (TSR)

Model climate - Cloud Model climate - Cloud radiative “forcing”radiative “forcing”• Problem: Can we associate these “errors” with clouds?• Another approach is to examine “cloud radiative forcing”

Note CRF sometimes defined as Fclr-F, also differences in model calculation

JJA 87

SWCRF

CY18R6-ERBE

Cloud Problems: strato-cu YES, North Africa NO!

Model climate - “Cloud Model climate - “Cloud fraction” or “Total cloud fraction” or “Total cloud cover”cover”

JJA 87

TCC

CY18R6-ISCCP

references: ISCCP Rossow and Schiffer, Bull Am Met Soc. 91, ERBE Ramanathan et al. Science 89

Can also compare other variables to derived products: CC

Climatology: The Problems Climatology: The Problems

If more complicated cloud parameters are desired (e.g. vertical structure) then retrieval can be ambiguous

Channel 1 Channel 2 …..

Liquid cloud 1Liquid cloud 2Liquid cloud 3

Ice cloud 1Ice cloud 2Ice cloud 3

Vapour 1Vapour 2Vapour 3H

eigh

t

Ass

umpt

ions

ab

out v

ertic

al

stru

ctur

es Another approach is tosimulate irradiances using model fieldsR

adia

tive

tran

sfer

m

odel

Simulating Simulating Satellite Satellite ChannelsChannels

Examples: Morcrette MWR 1991Chevallier et al, J Clim. 2001

More certainty in the diagnosis of the existence of a problem. Doesn’t necessarily help identify the origin of the problem

70°S 70°S

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METEOSAT 7 First Infrared Band Monday 11 April 2005 0600UTC

70°S 70°S

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RTTOV generated METEOSAT 7 First Inf rared Band (10 bit)Sunday 10 April 2005 12UTC ECMWF Forecast t+18 VT:Monday 11 April 2005 06UTC

NWP forecast evaluationNWP forecast evaluation• Differences in longer simulations may not be the direct result

of the cloud scheme– Interaction with radiation, dynamics etc.– E.g: poor stratocumulus regions

• Using short-term NWP or analysis restricts this and allows one to concentrate on the cloud scheme

Introduction of Tiedtke Scheme

Time

cloud cover bias

Example over EuropeExample over Europe

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N= 10761 BIAS= -0.55 STDEV= 2.61 MAE= 1.91FC PERIOD: 20050401 - 20050412 STEP: 48 VALID AT: 12 UTC

BIAS Total Cloud Cover [octa] ECM

-8 - -3 -3 - -1 -1 - 1 1 - 3 3 - 8-1:1 1:3 3:8-3:-1-8:-3

1. What are your conclusions concerning the cloud scheme?

Example over EuropeExample over Europe

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N= 10761 BIAS= -0.55 STDEV= 2.61 MAE= 1.91FC PERIOD: 20050401 - 20050412 STEP: 48 VALID AT: 12 UTC

BIAS Total Cloud Cover [octa] ECM

-8 - -3 -3 - -1 -1 - 1 1 - 3 3 - 8-1:1 1:3 3:8-3:-1-8:-3

1.Who wants to be a Meteorologist?

Which of the following is a drawback of SYNOP observations?

(a) They are only available over land (b) They are only available during daytime

(c) They do not provide information concerning vertical structure

(d) They are made by people

CompositesComposites

• We want to look at a certain kind of model system: – Stratocumulus regions– Extra tropical cyclones

• An individual case may not be conclusive: Is it typical?

• On the other hand general statistics may swamp this kind of system

• Can use compositing technique

Composites – Extra-tropical Composites – Extra-tropical cyclonescyclones

Overlay about 1000 cyclones, defined about a location of maximum optical thickness

Plot predominant cloud types by looking at anomalies from 5-day average

Klein and Jakob, 1999, MWR

High tops=Red, Mid tops=Yellow, Low tops=Blue

• High Clouds too thin

• Low clouds too thick

OLR OLR

-240

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135°W 90°W

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0° 45°E

45°E 90°E

90°E 135°E

135°E

TOA lw ellu September 2000 nmonth=12 nens=3 Global Mean: -249 50S-50N Mean: -261

[W/m2]

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-270

-240

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-180

-150

-124.0

-240

-240

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TOA lw CERES September 2000 nmonth=12 Global Mean: -239 50S-50N Mean: -250

[W/m2]

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-240

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-180

-150

-128.0

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30°S 30°S

0°0°

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Difference ellu - CERES 50N-S Mean err -11.3 50N-S rms 15.8[W/m2]

10

20

20.59

-71.57

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-60

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-40

-30

-20

-10

Model T95 L91

CERES

Difference

too high

too low

Conclusions?

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-300

-150

-300

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150150

350

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TOA sw ellu September 2000 nmonth=12 nens=3 Global Mean: 244 50S-50N Mean: 277

[W/m2]

56.85

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250

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350

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250

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TOA sw CERES September 2000 nmonth=12 Global Mean: 244 50S-50N Mean: 280

[W/m2]

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100

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370.4

15 60°S60°S

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Difference ellu - CERES 50N-S Mean err -2.86 50N-S rms 16.2[W/m2]

15

45

75

-130-115-100-85-70-55-40-25-15

SW SW

Model T95 L91

CERES

Difference

albedo high

albedo low

Conclusions?

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100

350

100

65

65

65

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90°E 135°E

135°E

Total Cloud Cover ellu September 2000 nmonth=12 nens=3 Global Mean: 60.5 50N-S Mean: 58.2

[percent]

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20

35

50

65

80

93.74

65

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90°E 135°E

135°E

Total Cloud Cover ISCCP D2 September 2000 nmonth=12 50N-S Mean: 62.2

[percent]

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35

50

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80

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95.54

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Difference ellu - ISCCP 50N-S Mean err -4.04 50N-S rms 11.3[percent]

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37.88

TCC TCC

Model T95 L91

ISCCP

Difference

TCC high

TCC low

Conclusions?

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75

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135°E

Liquid Water Path ellu September 2000 nmonth=12 nens=3 Global Mean: 67.6

[g/m**2]

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50

75

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150

171.4

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Liquid Water Path SSMI Wentz V5 September 2000 nmonth=12 Global Mean: 82.2

[g/m**2]

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50

75

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234.1

-50

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30°S 30°S

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Difference ellu - SSMI Wentz V5 Global Mean err -14.7 RMS 26.5[g/m**2]

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-75

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-25

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50

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117.8

TCLW TCLW

LWPLWP

Model T95 L91

SSMI

Difference

high

low

Conclusions?

80

5

80

5

Map of MLS ice errorMap of MLS ice error

20°N

25°N

30°N

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METEOSAT 7 First Infrared Band Monday 11 April 2005 1200UTC

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65°N

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RTTOV generated METEOSAT 7 First Inf rared Band (10 bit)Sunday 10 April 2005 12UTC ECMWF Forecast t+24 VT:Monday 11 April 2005 12UTC

Daily Report 11Daily Report 11thth April 2005 April 2005Lets see what you think? Lets see what you think?

“Going more into details of the cyclone, it can be seen that the model was able to reproduce the very peculiar spiral structure in the clouds bands. However large differences can be noticed further east, in the warm sector of the frontal system attached to the

cyclone, were the model largely underpredicts the typical high-cloud shield. Look for example in the two maps below where a clear deficiency of clod cover is evident in the model generated satellite images north of the Black Sea. In this case this was systematic

over different forecasts.” – Quote from ECMWF daily report 11th April 2005

Same Case, water vapour Same Case, water vapour channelschannels

METEOSAT 7 Water Vapour Band Monday 11 April 2005 2000UTC RTTOV generated METEOSAT 7 Water Vapour Band (10 bit)Sunday 10 April 2005 12UTC ECMWF Forecast t+30 VT:Monday 11 April 2005 18UTC

Blue: moistRed: Dry

30 hr forecast too dry in front regionIs not a FC-drift, does this mean the cloud scheme is at fault?

Future: Improved remote sensing Future: Improved remote sensing capabilities, CLOUDSATcapabilities, CLOUDSAT

• CloudSat is an experimental satellite that will use radar to study clouds and precipitation from space. CloudSat will fly in orbital formation as part of the A-Train constellation of satellites (Aqua, CloudSat, CALIPSO, PARASOL, and Aura)

• Launched 28th April

CloudsatCloudsattransect

Zonal mean cloud cover

Summary questionsSummary questions

AN ICTPLECTURER

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