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I'm interested in the process that goes from, for example, huge data sets of temperature, pressure, precipitation etc readings to a model of the atmosphere. I'd like to know about how much relies on a priori knowledge of Navier-Stokes, and how much curve fitting is done.

Some sort of general overview of the process (ideally a fairly accessible one) would be perfect.

Edit

I'm looking for a reference, specifically. I know more or less the science from talking to people who do this stuff, but I would like to have some summary article or book chapter overview I can refer to in written work.

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Might want to change the title too so that it mentions you're interested in climate science. –  j.c. Nov 3 '10 at 14:23

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Climate modelling is a giant science of its own, and the proportion of CFD/statistics depends on the particular model.
In general, what models do is first a simple (often uncompressable) large-scale CFD to advect the scalar fields and then apply a bunch of subrutines simulating small scale and more complex processes, like radiation transfers, heat transfers, cloud dynamics, precipitation, evatranspiration and interactions with land/ocean. Those are mainly governed by some phenomenological theories, based either on some subscale simulations, approximated theories or purely statistical models. The proper construction of that step depends on the purpose of a model and has more to do with art than science (i.e. balancing accuracy and computational time).

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Is there a nice reference that summarises this stuff? –  Seamus Nov 5 '10 at 18:06

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