期刊
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
卷 63, 期 3, 页码 429-444出版社
CO-ACTION PUBLISHING
DOI: 10.1111/j.1600-0870.2010.00505.x
关键词
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资金
- U.S. Air Force Weather Agency
- Div Atmospheric & Geospace Sciences
- Directorate For Geosciences [0939961] Funding Source: National Science Foundation
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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