Journal
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
Volume 63, Issue 3, Pages 429-444Publisher
CO-ACTION PUBLISHING
DOI: 10.1111/j.1600-0870.2010.00505.x
Keywords
-
Categories
Funding
- U.S. Air Force Weather Agency
- Div Atmospheric & Geospace Sciences
- Directorate For Geosciences [0939961] Funding Source: National Science Foundation
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available