Cloud Validation: The issues
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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 1
Liquid cloud 2
Liquid cloud 3
Ice cloud 1
Ice cloud 2
Ice cloud 3
Vapour 1
Vapour 2
Vapour 3Hei
ght
Ass
um
pti
on
s ab
ou
t ve
rtic
al
stru
ctu
res Another approach is to
simulate irradiances using model fields
Rad
iati
ve
tran
sfer
m
od
el
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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60°W 40°W 20°W 0° 20°E 40°E 60°E
METEOSAT 7 First Infrared Band Monday 11 April 2005 0600UTC
70°S 70°S
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50°S 50°S
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30°S 30°S
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10°S 10°S
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50°N 50°N
60°N60°N
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60°W 40°W 20°W 0° 20°E 40°E 60°E
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
30°N
35°N
40°N
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50°N
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60°N
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50°W
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30°E 40°E
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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
30°N
35°N
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30°W
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30°E 40°E
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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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30°S 30°S
0°0°
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60°N60°N
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45°W 0°
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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90°E 135°E
135°E
TOA lw CERES September 2000 nmonth=12 Global Mean: -239 50S-50N Mean: -250
[W/m2]
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-270
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-128.0
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90°E 135°E
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Difference ellu - CERES 50N-S Mean err -11.3 50N-S rms 15.8
[W/m2]
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-71.57
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Model T95 L91
CERES
Difference
too high
too low
Conclusions?
-150
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350
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30°S 30°S
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90°E 135°E
135°E
TOA sw ellu September 2000 nmonth=12 nens=3 Global Mean: 244 50S-50N Mean: 277
[W/m2]
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90°E 135°E
135°E
TOA sw CERES September 2000 nmonth=12 Global Mean: 244 50S-50N Mean: 280
[W/m2]
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30°S 30°S
0°0°
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Difference ellu - CERES 50N-S Mean err -2.86 50N-S rms 16.2
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SW SW
Model T95 L91
CERES
Difference
albedo high
albedo low
Conclusions?
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30°S 30°S
0°0°
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45°W 0°
0° 45°E
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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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45°W 0°
0° 45°E
45°E 90°E
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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90°E 135°E
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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
Difference
TCC high
TCC low
Conclusions?
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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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90°E 135°E
135°E
Liquid Water Path SSMI Wentz V5 September 2000 nmonth=12 Global Mean: 82.2
[g/m**2]
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234.1
-50
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Difference ellu - SSMI Wentz V5 Global Mean err -14.7 RMS 26.5
[g/m**2]
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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
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METEOSAT 7 First Infrared Band Monday 11 April 2005 1200UTC
20°N
25°N
30°N
35°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
ADRIAN