Liquid Water Path Variabilities
Damian Wilson
CloudNet meeting, Paris, 4th-5th April 2005
© Crown copyright 2004
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Contents
Radiation biases from variable liquid water paths.
Using CloudNet data to help estimate the biases.
Comments on scaling liquid water paths in
radiation calculations.
Summary
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Much variability exists in LWP.
Because transmission is non-linear,
T(LWP) = T(LWP) .
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Transmission
Radiation biases
Liquid water path
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Radiation parametrization developments
Models will be looking to incorporate more
consistent subgrid-scale models.
 E.g. Monte Carlo Independent Pixel Approximation
method.
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Radiation biases
Ttruth = exp[ - 3/2 LWP / (waterre) ]
Tmodel = C exp [ -3/2 LWPcloudy / (waterre) ] + (1-C) 1
Tscaled = C exp[ - 3/2  LWPcloudy / (waterre) ] + (1-C) 1
What is the ratio Tmodel / Ttruth ?
What value of  is needed such that Tscaled = Ttruth
(is it 0.7, as suggested earlier)?
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Radiation biases results
Tmodel/Ttruth

10m re
Chilbolton
observations
Feb 2004:
0.961
0.3
Mar 2004:
0.876
0.35
Apr 2004:
0.897
0.4
6 hours of
averaging per
sample 200
km at 10 m s-1
Most bias is removed using the cloud / out-of-cloud
averaging. (With just a gridbox mean we would have 0.5
– 0.6)
But a variable amount -10% remains.
Factor  is a lot less than previous estimates.
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Is a scaling factor  likely to work?
Most important
aspect is to predict
the cloud fraction
correctly
Obs
Met Office
Met Office x 0.4
Meteo France
ECMWF
Scaling does not
better reproduce
the LWP
histogram shape
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Transmittivities
All models and
obs have a flat
distribution outside
of 0 and 1.
Obs
Met Office
Met Office x 0.4
Meteo France
ECMWF
Most significant
difference
between obs and
models is the
mean LWP, not
its distribution.
Scaling does not improve the
distribution shape.
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Summary
Can use CloudNet data to estimate radiation
biases.
Most significant quantity for the models to get
right is the mean LW path, not its distribution.
Most bias is corrected using the in-cloud, outof-cloud averaging (if the cloud fraction is
correct).
A small amount of bias remains – a single
scaling factor may not be the best way to treat
this.
© Crown copyright 2004
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