Predictability Assessment and Improving Ensemble Forecasts

Abstract

The PI and Co-I are examining mesoscale predictability with the goal of improving the utility of ensemble forecasts at ranges of 12 hours to 10 days. Our research addresses the issues of initial condition uncertainty (ICU) for mesoscale analyses, calibration of output from ensemble prediction systems (EPSs) by artificial neural networks, and predictability estimates for precipitation and processes that strongly influence precipitation. The PI also serves as Chief Scientist to Dr. Scott Sandgathe for ONR initiative on Predictability in the Atmosphere and Ocean.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 1999
Accession Number
ADA629905

Entities

People

  • Mary M. Poulton
  • Steven L. Mullen

Organizations

  • University of Arizona

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Atmospheric Sciences
  • Calibration
  • Data Sets
  • Electronic Mail
  • Engineering
  • Geographic Regions
  • Information Operations
  • Information Science
  • Kalman Filters
  • Neural Networks
  • Potential Energy
  • Precipitation
  • Standards
  • Statistical Analysis
  • Statistics
  • Uncertainty

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Distributed Systems and Data Platform Development
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy