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Liquid Water Path Variabilities Damian Wilson

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Presentation on theme: "Liquid Water Path Variabilities Damian Wilson"— Presentation transcript:

1 Liquid Water Path Variabilities Damian Wilson
CloudNet meeting, Paris, 4th-5th April 2005 © Crown copyright 2004

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 © Crown copyright 2004

3 Radiation biases Much variability exists in LWP. Because transmission is non-linear, T(LWP) = T(LWP) . Transmission Liquid water path © Crown copyright 2004

4 Radiation parametrization developments
Models will be looking to incorporate more consistent subgrid-scale models. E.g. Monte Carlo Independent Pixel Approximation method. © Crown copyright 2004

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)? © Crown copyright 2004

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. © Crown copyright 2004

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 © Crown copyright 2004

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. © Crown copyright 2004

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. © Crown copyright 2004


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