Summaries - Office of Research & Innovation
Back State-Space Analysis of Model Error: A Probabilistic Parameter Estimation Framework with Spatial Analysis of Variance
|Division||Graduate School of Engineering & Applied Science|
Durkee, Philip A.
Hacker, Joshua P.
|Sponsor||Office of Naval Research (Navy)|
|Summary||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 this proposal is to provide a method that begins to systematically disentangle the model inadequacy signal from the initial condition error signal. We propose 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 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. The research focuses on model error in boundary layer winds, and uses both the COAMPSTM and WRF model.|
|Publications||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|
|Data||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|