Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

Abstract

The long-term goal of this project is to quantify the extent to which reduced-order models can be used for the description, understanding and prediction of atmospheric, oceanic and sea ice variability on time scales of 1 12 months and beyond.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2012
Accession Number
ADA572180

Entities

People

  • Adam Sobel
  • Alexey Kaplan
  • Andrew Robertson
  • Dake Chen
  • Dmitri Kondrashov
  • Mark Cane
  • Michael Ghil
  • Michael K. Tippett
  • Mickael D. Chekroun
  • Mingfang Ting
  • Suzana J. Camargo
  • Xiaojun Yuan
  • Yochanan Kushnir

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Analogs
  • Dynamics
  • Equations
  • Fluid Dynamics
  • Ice
  • New York
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Observation
  • Oscillation
  • Pacific Ocean
  • Physics
  • Sea Ice
  • Sea Surface Temperature
  • Surface Temperature
  • Transitions
  • Universities

Readers

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