1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. State-Space Analysis of Model Error: A Probabilistic Parameter Estimation Framework With Spatial Analysis Of Variance

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 new methods that begin to systematically disentangle the model inadequacy signal from the initial condition error signal. OBJECTIVES 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 heirarchical spatial modeling methods; (2) the development of a flexible representation for the various spatial and temporal scales of model error; (3) the estimation of parameters to represent 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 Report Documentation Page Form ApprovedOMB No. 0704-018