Toward an Operational Particle Filter-Based Ensemble Data Assimilation System

Abstract

The primary goal of this project was to use a Markov chain Monte Carlo (MCMC) algorithm to examine the strengths and limitations of ensemble data assimilation algorithms, when applied to estimation of convective cloud system properties. MCMC methods return an exact solution, and as such are powerful tools for the evaluation of approximate data assimilation algorithms. The research was successful in advancing ensemble data assimilation theory, and has directly led to two peer reviewed journal articles, a peer reviewed book chapter, and 11 conference presentations.

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Document Details

Document Type
Technical Report
Publication Date
Sep 22, 2014
Accession Number
ADA614557

Entities

People

  • Derek J. Posselt

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Assimilation
  • Contracts
  • Data Science
  • Information Science
  • Kalman Filters
  • Markov Chains
  • Mathematics
  • Monte Carlo Method
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Particles
  • Probability
  • Sequential Monte Carlo Methods
  • Statistical Algorithms
  • Statistical Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Technical Research and Report Writing.