parameterization cloud scheme validation
DESCRIPTION
ADRIAN. AN ICTP LECTURER. Parameterization Cloud Scheme Validation. [email protected]. Cloud observations. parameterisation improvements. error . Cloud simulation. Cloud Validation: The issues. AIM : To perfectly simulate one aspect of nature: CLOUDS - PowerPoint PPT PresentationTRANSCRIPT
AN ICTPLECTURER
ParameterizationParameterizationCloud Scheme ValidationCloud Scheme Validation
ADRIAN
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
The path to improved cloud The path to improved cloud parameterisation…parameterisation…
cloud validation
parameterisation improvement
Comparison to Satellite Products
Case studies
Composite studies
NWP validation
?
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
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METEOSAT 7 First Infrared Band Monday 11 April 2005 0600UTC
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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
A more complicated analysis is A more complicated analysis is possible:possible:
DIURNAL CYCLEOVER TROPICAL
LAND
VARIABILITY
Observations:late afternoon peak in
convectionModel:
morning peak(Common problem)
Has this Improved? 29r1, 06 Has this Improved? 29r1, 06 UTCUTC
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METEOSAT 7 First Infrared Band Monday 11 April 2005 0600UTC
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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
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METEOSAT 7 First Infrared Band Monday 11 April 2005 1200UTC
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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
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METEOSAT 7 First Infrared Band Monday 11 April 2005 1800UTC
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RTTOV generated METEOSAT 7 First Inf rared Band (10 bit)Sunday 10 April 2005 12UTC ECMWF Forecast t+30 VT:Monday 11 April 2005 18UTC
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
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1. What are your conclusions concerning the cloud scheme?
Example over EuropeExample over Europe
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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
Case StudiesCase Studies
• These were examples of general statistics: globally or for specific regions
• Can look concentrate on a particular location in more details, for which more data is collected:CASE STUDY
• Examples: – GATE, CEPEX, TOGA-COARE, ARM...
Evaluation of vertical cloud Evaluation of vertical cloud structurestructure
Mace et al., 1998, GRL
Examined the frequency of occurrence of ice cloud
Reasonable match to data found
ARM Site - America Great Plains
Evaluation of vertical cloud Evaluation of vertical cloud structurestructure
Hogan et al., 2000, JAM
Analysis using the Chilbolton radar and
Lidar
Reasonable Match
Hogan et al. More details Hogan et al. More details possiblepossible
Hogan Hogan et al.et al.
Found that comparison improved when snow was
taken into account
Issues Raised:Issues Raised:
• WHAT ARE WE COMPARING?– Is the model statistic really equivalent to what the
instrument measures?– e.g: Radar sees snow, but the model may not include this is
the definition of cloud fraction. Small ice amounts may be invisible to the instrument but included in the model statistic
• HOW STRINGENT IS OUR TEST? – Perhaps the variable is easy to reproduce– e.g: Mid-latitude frontal clouds are strongly dynamically
forced, cloud fraction is often zero or one. Perhaps cloud fraction statistics are easy to reproduce in short term forecasts
Can also use to validate Can also use to validate “components” of cloud “components” of cloud schemescheme
EXAMPLE: Cloud Overlap AssumptionsHogan and Illingworth, 00,
QJRMS
Issues Raised: HOW REPRESENTATIVE IS OUR CASE STUDY LOCATION?e.g: Wind shear and dynamics very different between Southern England and the tropics!!!
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 - a cloud surveyComposites - a cloud survey
Tselioudis et al., 2000, JCL
From Satellite attempt to derive
cloud top pressure and cloud optical
thickness for each pixel - Data is then
divided into regimes according
to sea level pressure anomaly
Use ISCCP simulator
1. High Clouds too thin
2. Low clouds too thick
Data Model Modal-Data
Optical depth
Clo
ud to
p pr
essu
re
1.
2.
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SLP
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SLP
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
A strategy for cloud A strategy for cloud parametrization evaluationparametrization evaluation
Jakob, Thesis
Where are the difficulties?
Recap: The problemsRecap: The problems
• All Observations– Are we comparing ‘like with like’? What assumptions are contained
in retrievals/variational approaches?
• Long term climatologies:– Which physics is responsible for the errors?– Dynamical regimes can diverge
• NWP, Reanalysis, Column Models– Doesn’t allow the interaction between physics to be represented
• Case studies– Are they representative? Do changes translate into global skill?
• Composites As case studies.
• And one more problem specific to NWP…
NWP cloud scheme developmentNWP cloud scheme development
Timescale of validation exercise– Many of the above validation exercises are complex and
involved– Often the results are available O(years) after the project
starts for a single version of the model– NWP operational models are updated 2 to 4 times a year
roughly, so often the validation results are no longer relevant, once they become available.
RequirementRequirement: A quick and easy test bench
Example: LWP ERA-40 and recent Example: LWP ERA-40 and recent cyclescycles
23r4: June 2001 26r1: April 2003
mod
elS
SM
ID
iff
Example: LWP ERA-40 and recent Example: LWP ERA-40 and recent cyclescycles
23r4: June 2001 26r3: Oct 2003
mod
elS
SM
ID
iff
Example: LWP ERA-40 and recent Example: LWP ERA-40 and recent cyclescycles
23r4: June 2001 28r1: Mar 2004
mod
elS
SM
ID
iff
Example: LWP ERA-40 and recent Example: LWP ERA-40 and recent cyclescycles
23r4: June 2001 28r3: Sept 2004
mod
elS
SM
ID
iff
Example: LWP ERA-40 and recent Example: LWP ERA-40 and recent cyclescycles
23r4: June 2001 29r1: Apr 2005
Do ERA-40 cloud studies still have relevance for the operational model?
mod
elS
SM
ID
iff
So what is used at ECMWF?So what is used at ECMWF?
• T799-L91– Standard
“Scores” (rms, anom corr of U, T, Z)
– “operational” validation of clouds against SYNOP observations
– Simulated radiances against Meteosat 7
• T159-L91 – “climate” runs– 3 ensemble members of 13 months– Automatically produces comparisons to:
• ERBE, NOAA-x, CERES TOA fluxes• Quikscat & SSM/I, 10m winds• ISCCP & MODIS cloud cover• SSM/I, TRMM liquid water path• (soon MLS ice water content)• GPCP, TRMM, SSM/I, Xie Arkin, Precip• Dasilva climatology of surface fluxes• ERA-40 analysis winds
All datasets treated as “truth”
OLR OLR
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TOA lw ellu September 2000 nmonth=12 nens=3 Global Mean: -249 50S-50N Mean: -261
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Difference ellu - CERES 50N-S Mean err -11.3 50N-S rms 15.8[W/m2]
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CERES
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too high
too low
Conclusions?
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TOA sw ellu September 2000 nmonth=12 nens=3 Global Mean: 244 50S-50N Mean: 277
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TOA sw CERES September 2000 nmonth=12 Global Mean: 244 50S-50N Mean: 280
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Difference ellu - CERES 50N-S Mean err -2.86 50N-S rms 16.2[W/m2]
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SW SW
Model T95 L91
CERES
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albedo high
albedo low
Conclusions?
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Total Cloud Cover ellu September 2000 nmonth=12 nens=3 Global Mean: 60.5 50N-S Mean: 58.2
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Total Cloud Cover ISCCP D2 September 2000 nmonth=12 50N-S Mean: 62.2
[percent]
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Difference ellu - ISCCP 50N-S Mean err -4.04 50N-S rms 11.3[percent]
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TCC TCC
Model T95 L91
ISCCP
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Conclusions?
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Color: MLSWhite bars: EC IWC sampled with MLS tracks
First Ice Validation: microwave limb sounders
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Liquid Water Path ellu September 2000 nmonth=12 nens=3 Global Mean: 67.6
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Liquid Water Path SSMI Wentz V5 September 2000 nmonth=12 Global Mean: 82.2
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TCLW TCLW
LWPLWP
Model T95 L91
SSMI
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high
low
Conclusions?
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Map of MLS ice errorMap of MLS ice error
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METEOSAT 7 First Infrared Band Monday 11 April 2005 1200UTC
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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: Long term ground-based Future: Long term ground-based validation, CLOUDNETvalidation, CLOUDNET
• Network of stations processed for multi-year period using identical algorithms, first Europe, now also ARM sites
• Some European provide operational forecasts so that direct comparisons are made quasi-realtime
• Direct involvement of Met services to provide up-to-date information on model cycles
Cloudnet ExampleCloudnet Example
• In addition to standard quicklooks, longer-term statistics are available
• This example is for ECMWF cloud cover during June 2005
• Includes preprocessing to account for radar attenuation and snow
• See www.cloud-net.org for more details and examples!
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
A chasm still exists!!!
In summary: Many methods In summary: Many methods for examining cloudsfor examining clouds
parameterisation improvement
cloud validation
but all too often...