Predictability Assessment and Improving Ensemble Forecasts

Abstract

The PI continues to examine atmospheric predictability with the goal of improving ensemble forecasts at ranges of 12 hours to 10 days. The research is addressing several issues, including: 1. Documentation of analysis uncertainty from mesoscale and global analyses. 2. Calibration of ensemble forecast system (EFS) output by artificial neural networks. 3. Design of optimal EFS's, with an emphasis on precipitation forecasts. 4. Design of stochastic physics parameterizations that improve under-dispersion in EFS s.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2002
Accession Number
ADA627327

Entities

People

  • Steven L. Mullen

Organizations

  • University of Arizona

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Atmospheric Sciences
  • Boundary Layer
  • Calibration
  • Delphi Method
  • Electronic Mail
  • Humidity
  • Information Operations
  • Information Science
  • Neural Networks
  • Precipitation
  • Rain Gages
  • Simulations
  • Statistics
  • Two Dimensional
  • Uncertainty

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks