1. Cloud Validation: The issues
ECMWF Training Course
May 2001
Cloud 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?
Clouds 3

2. Cloud Validation: The problems
ECMWF Training Course
May 2001
Cloud Validation: The problems
How much of the ‘error’ derives from observations?
Cloud observations error = e1
parameterisation improvements
error
Cloud simulation error = e2
Clouds 3

3. Cloud Validation: The problems
ECMWF Training Course
May 2001
Cloud Validation: The problems
Which Physics is responsible for the error?
Cloud observations
parameterisation improvements
error
Cloud simulation
radiation
convection
cloud physics
dynamics
turbulence
Clouds 3

4. Model climate - Broadband radiative fluxes
ECMWF Training Course
May 2001
Model climate - Broadband radiative fluxes
Can compare Top of Atmosphere (TOA) radiative fluxes with satellite observations: TOA Shortwave radiation (TSR)
JJA 87
TSR
CY18R6-ERBE
Stratocumulus regions bad - also North Africa (old cycle!)
Clouds 3

5. Model climate - Cloud radiative “forcing”
ECMWF Training Course
May 2001
Model climate - Cloud radiative “forcing”
Problem: Can we associate these “errors” with clouds?
Another approach is to examine “cloud radiative forcing”
JJA 87
SWCRF
CY18R6-ERBE
Cloud Problems: strato-cu YES, North Africa NO!
Note CRF sometimes defined as Fclr-F, also differences in model calculation
Clouds 3

6. Model climate - “Cloud fraction” or “Total cloud cover”
ECMWF Training Course
May 2001
Model climate - “Cloud fraction” or “Total cloud cover”
Can also compare other variables to derived products: CC
JJA 87
TCC
CY18R6-ISCCP
references: ISCCP Rossow and Schiffer, Bull Am Met Soc. 91,
ERBE Ramanathan et al. Science 89
Clouds 3

7. Climatology: The Problems
ECMWF Training Course
May 2001
Climatology: The Problems
If more complicated cloud parameters are desired (e.g. vertical structure) then retrieval can be ambiguous
Channel 1
Channel 2
…..
Another approach is to
simulate irradiances using model fields
Radiative transfer model
Assumptions about vertical structures
Liquid cloud 1
Liquid cloud 2
Liquid cloud 3
Ice cloud 1
Ice cloud 2
Ice cloud 3
Vapour 1
Vapour 2
Vapour 3
Height
Clouds 3

8. Simulating Satellite Channels
ECMWF Training Course
May 2001
Simulating Satellite Channels
Examples:
Morcrette MWR 1991
Chevallier 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
Clouds 3

9. NWP forecast evaluation
ECMWF Training Course
May 2001
NWP 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
cloud cover bias
Time
Clouds 3

10. Example over Europe
-8:-3
-3:-1
-1:1
1:3
3:8
What are your conclusions concerning the cloud scheme?

11. Example over Europe Who wants to be a Meteorologist?
-8:-3
-3:-1
-1:1
1:3
3:8
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

12. Composites We want to look at a certain kind of model system:
ECMWF Training Course
May 2001
Composites
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
Clouds 3

13. Composites – Extra-tropical cyclones
ECMWF Training Course
Composites – Extra-tropical cyclones
May 2001
Overlay about 1000 cyclones, defined about a location of maximum optical thickness
Plot predominant cloud types by looking at anomalies from 5-day average
High Clouds too thin
Low clouds too thick
High tops=Red, Mid tops=Yellow, Low tops=Blue
Klein and Jakob, 1999, MWR
Clouds 3

14. OLR Model T95 L91 CERES Conclusions? Difference -150 -300 -150 -300
too high
Difference
too low

15. SW Model T95 L91 CERES Conclusions? Difference 350 100 350 100
albedo high
Difference
albedo low

16. TCC Model T95 L91 ISCCP Conclusions? Difference 80 5 80 5 TCC high
TCC low

17. 80
Model T95 L91
TCLW LWP 5 80
SSMI
Conclusions? 5
high
Difference
low

18. Map of MLS ice error

19. Daily Report 11th April 2005 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

20. Same Case, water vapour channels
Blue: moist
Red: Dry
30 hr forecast too dry in front region
Is not a FC-drift, does this mean the cloud scheme is at fault?

21. Future: Improved remote sensing capabilities, 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

22. Cloudsat
transect
Zonal mean cloud cover

23. Summary questions
AN ICTP
LECTURER
ADRIAN