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1 CloudNet data comparison These slides demonstrate the suggested comparison strategy for CloudNet data, although they are applicable for other model - observation comparisons. They should be viewed with the CloudNet User Requirement Document. Damian Wilson, Met Office and Jean-Marcel Piriou, Meteo-France

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2 The problem 1 2 3 4 IWC LWC CF Z A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites How do we transform between fields? Processing to combine ice categories etc. Processing to remove clutter etc.

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3 Observations to models 1 2 3 4 IWC LWC CF Z A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites We could transform obs-like fields into model- like fields Algorithms Assumptions

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4 Models to observations 1 2 3 4 IWC LWC CF Z A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites Or from models to observations. The algorithms might not be reversible. A -1 A I New algorithms, assumptions

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5 Direct comparison 1 2 3 4 IWC LWC CF Z A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites It might be possible to transform directly from the model, but not for all models and obs fields. No new assumptions

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6 Unavailable information 1 2 3 4 IWC LWC CF Z A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites If data is absent then different transforms are required. New algorithms, assumptions.

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7 Sources of error 1 2 3 4 IWC LWC CF Z A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites Error in transforms Initial conditions and forward model can produce errors Measurement and estimation error CloudNet wishes to assess forward model errors.

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8 Recommendations Sets of well defined quantities should be identified, which correspond closely with variables available in models and measurements available from observing sites. Models and observations should store data in their processed state. Algorithms should be developed to transform in either direction. These algorithms are not necessarily reversible.

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9 Recommendations Algorithms should transform between variables which are readily available from different types of models and observation sites, so transforms are not site or model specific. This will help future comparisons with other sites and models. If a piece information is not available from a model or site then a different algorithm must be developed.

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10 Recommendations Comparisons can be carried out in both model and observation space by using the transforms. Each would provide different sorts of information. It may be possible in specific, limited circumstances to transform directly from a model to observations with the addition of no new assumptions. Such comparison is also of value and a model should supply information to do this if this is possible.

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11 Recomendations Errors should be assessed for each part of the comparison.

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