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: * Documentation of analysis uncertainty from mesoscale and global analyses. * Calibration of output from ensemble forecast systems (EFS's) by artificial neural networks. * Design of optimal EFS's for precipitation. * Design of stochastic physics parameterizations that improve EFS performance. The PI also serves as Co-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, 2001
Accession Number
ADA627304

Entities

People

  • Mary M. Poulton
  • Steven L. Mullen

Organizations

  • University of Arizona

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Addressing
  • Algorithms
  • Atmospheric Sciences
  • Boundaries
  • Boundary Layer
  • Calibration
  • Data Sets
  • Electronic Mail
  • Information Operations
  • Neural Networks
  • Perturbations
  • Physics
  • Precipitation
  • Statistics
  • 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