4.1 Article

Linear and non-linear response to parameter variations in a mesoscale model

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Publisher

CO-ACTION PUBLISHING
DOI: 10.1111/j.1600-0870.2010.00505.x

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  1. U.S. Air Force Weather Agency
  2. Div Atmospheric & Geospace Sciences
  3. Directorate For Geosciences [0939961] Funding Source: National Science Foundation

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Parameter uncertainty in atmospheric model forcing and closure schemes has motivated both parameter estimation with data assimilation and use of pre-specified distributions to simulate model uncertainty in short-range ensemble prediction. This work assesses the potential for parameter estimation and ensemble prediction by analysing 2 months of mesoscale ensemble predictions in which each member uses distinct, and fixed, settings for four model parameters. A space-filling parameter selection design leads to a unique parameter set for each ensemble member. An experiment to test linear scaling between parameter distribution width and ensemble spread shows the lack of a general linear response to parameters. Individual member near-surface spatial means, spatial variances and skill show that perturbed models are typically indistinguishable. Parameter-state rank correlation fields are not statistically significant, although the presence of other sources of noise may mask true correlations. Results suggest that ensemble prediction using perturbed parameters may be a simple complement to more complex model-error simulation methods, but that parameter estimation may prove difficult or costly for real mesoscale numerical weather prediction applications.

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