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The Advanced Study Program 2013 Seminar Series second seminar is presented by Sue Ellen Haupt of RAL.
Although atmospheric models provide a best estimate of the future state of the atmosphere, due to sensitivity to initial conditions, it is intractable to predict the precise future state. For applied problems, however, users often expect just that – predicting a specific realization of atmospheric flow. How can meteorologists make useful predictions in the face of such uncertainty? By applying state-of-the-science modeling, assimilating observations, and applying appropriate post-processing techniques, we can approach predicting realizations of atmospheric flow and quantifying their uncertainty.
Predicting a particular realization of an evolving flow field requires knowledge of the current state of that field and assimilation of observations into the model. A first example is modeling atmospheric transport and dispersion of a contaminant when the observation is of the transported contaminant, a problem that exemplifies the issue of uncertain turbulent flow. We will discuss the inner vs. the outer variability and how both can be recovered with judicious use of the observations. In this case, the problem is compounded by the fact that the field observed is a tracer that is advected and mixed by the flow field, but does not directly alter the flow field. This one-way coupled system presents a challenge: one must first infer the changes in the flow field from observations of the contaminant, then assimilate that data to recover both the advecting flow and information on the subgrid processes that provide the mixing. To accomplish such assimilation requires a robust method to match the observed contaminant field to that modeled. Here we use a genetic-algorithm variational approach to dynamically assimilate the concentration and use that to infer both the resolved and unresolved variables.
Next we will show how assimilation can help bridge the gap between modeling flows at the mesoscale and flows at the fine scale that is often important for resolving flow around local features. By assimilating mesoscale model data into a computational fluid dynamics model, we can force the fine scale model to with the features at the mesoscale, providing a coupling mechanism. Additionally, we look at ensemble mesoscale prediction in terms of predicting realizations and quantifying their uncertainty. Computational intelligence techniques can be used to identify regimes, which allows closer prediction of a realization. Postprocessing allows better quantification of uncertainty.
Finally, we will look at the value of assimilation and ensemble prediction for the renewable energy industry. If the industry decision makers have confidence in the wind and solar power forecasts, they can build their power allocations around the expected renewable resource, saving money for the ratepayers as well as reducing emissions.