Predictability Assessment and Improving Ensemble Forecasts

Abstract

The PI is examining atmospheric predictability with the goal of improving ensemble forecasts at ranges of 12 hours to 10 days. The research is addressing several issues, including: a) Documentation of analysis uncertainty from mesoscale and global analyses. b) Calibration of ensemble forecast system (EFS) output by artificial neural networks. c) Design of optimal EFS's, with an emphasis on precipitation forecasts. d) 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, 2001
Accession Number
ADA625671

Entities

People

  • Steven L. Mullen

Organizations

  • University of Arizona

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Atmospheric Sciences
  • Boundary Layer
  • Calibration
  • Climate Change
  • Data Sets
  • Electronic Mail
  • False Alarms
  • Information Operations
  • Neural Networks
  • Physics
  • Precipitation
  • Simulations
  • Statistics
  • Two Dimensional
  • Uncertainty
  • United States

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Computational Modeling and Simulation
  • Research Science/Academic Research

Technology Areas

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