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 new methods that begin to systematically disentangle the model inadequacy signal from the initial condition error signal.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2012
Accession Number
ADA574466

Entities

People

  • Cari G. Kaufman
  • James Hansen
  • Joshua P. Hacker

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Bayesian Networks
  • Climate Change
  • Computational Science
  • Data Science
  • Data Sets
  • Differential Equations
  • Information Science
  • Machine Learning
  • Mathematical Filters
  • Monte Carlo Method
  • Partial Differential Equations
  • Probabilistic Models
  • Probability
  • Simulations
  • Statistics
  • Weather Forecasting

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Systems Analysis and Design

Technology Areas

  • Space