New Methods For Predictability Analysis

Abstract

Our long-term goal is to improve atmospheric and oceanic deterministic forecast capability by maximizing the accuracy of the initial state estimate from which a forecast is made while minimizing observational and computational costs required for obtaining the initial state. Our present objective is to increase fundamental understanding of error dynamics and develop practical methods for computing error statistics so that optimal state estimation algorithms can be implemented. We intend to accomplish this by reducing the dimension of the error system so that error statistics can be practically obtained using forecast products. In addition from knowledge of the statistical structure of the time dependent error field we intend to determine the areas in space and time where observational resources can be most effectively applied.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2000
Accession Number
ADA610076

Entities

People

  • Brian Farrell

Organizations

  • Harvard University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Atmospheric Sciences
  • Control Theory
  • Dimensionality Reduction
  • Dynamics
  • Engineering
  • Errors
  • Factor Analysis
  • Filters
  • Flow
  • Fluid Flow
  • Frequency Domain
  • Kalman Filters
  • Planetary Sciences
  • Statistics
  • Time Dependence
  • Truncation

Readers

  • Coastal Oceanography
  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

  • Space