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Liquid Water Path Variabilities Damian Wilson CloudNet meeting, Paris, 4 th -5 th April 2005

Liquid Water Path Variabilities Damian Wilson CloudNet meeting, Paris, 4 th -5 th April 2005. 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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Liquid Water Path Variabilities Damian Wilson CloudNet meeting, Paris, 4 th -5 th April 2005

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  1. Liquid Water Path Variabilities Damian Wilson CloudNet meeting, Paris, 4th-5th April 2005

  2. 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

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

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

  5. 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)?

  6. Radiation biases results Tmodel/Ttruth Feb 2004: 0.961 0.3 Mar 2004: 0.876 0.35 Apr 2004: 0.897 0.4 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.

  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

  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.

  9. 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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