State-Space Analysis Of Model Error: A Probabilistic Parameter Estimation Framework With Spatial Analysis Of Variance
NAVAL POSTGRADUATE SCHOOL MONTEREY CA DEPT OF METEOROLOGY
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An over-arching goal in prediction science is to objectively improve numerical models of nature. Meeting that goal requires objective quantification of deficiencies in our models. The structural differences between a numerical model and a true system are difficult to ascertain in the presence of multiple sources of error. Numerical weather prediction NWP is subject to temporally and spatially varying error, resulting from both imperfect atmospheric models and the chaotic growth of initial condition IC error. The aim of our work is to provide a method that begins to systematically disentangle the model inadequacy signal from the initial condition error signal. We are engaging a comprehensive effort that uses state-of-the-science estimation methods in data assimilation DA and statistical modeling, including 1 the characterization of existing model-to-model differences via novel spatial Analysis of Variance ANOVA methods 2 the development of a flexible representation for the various spatial and temporal scales of model error 3 the estimation of parameters in representing those scales using a probabilistic approach to DA, namely the Ensemble Kalman Filter and 4 the determination of whether incorporation of estimated error structure in improves short-term forecasts, again using spatial ANOVA methods, this time within a formal testing framework. Research focus is on near-surface winds over both the ocean and land. The method under development are sufficiently general and can apply to a wide range of battlespace environments.
- Atmospheric Physics