State-Space Analysis Of Model Error: A Probabilistic Parameter Estimation Framework With Spatial Analysis Of Variance
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2011
- Accession Number
- ADA556925
Entities
People
- Cari G. Kaufman
- James Hansen
- Joshua P. Hacker
Organizations
- Naval Postgraduate School