1. Liquid Water Path Variabilities Damian Wilson
CloudNet meeting, Paris, 4th-5th April 2005
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2. Radiation biases from variable liquid water paths.
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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3. Radiation biases
Much variability exists in LWP. Because transmission is non-linear, T(LWP) = T(LWP) .
Transmission
Liquid water path
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4. 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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5. Ttruth = exp[ - 3/2 LWP / (waterre) ]
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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6. Radiation biases results
Tmodel/Ttruth 
Feb 2004:
Mar 2004:
Apr 2004:
10m re
Chilbolton observations
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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7. Is a scaling factor  likely to work?
Obs
Most important aspect is to predict the cloud fraction correctly
Met Office
Met Office x 0.4
Meteo France
ECMWF
Scaling does not better reproduce the LWP histogram shape
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8. 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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9. Can use CloudNet data to estimate radiation biases.
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, out-of-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.
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