Mesoscale Predictability and Improving the Utility of Ensemble Forecasts

Abstract

Examining mesoscale predictability with the goal of improving the utility of ensemble forecasts (EFs) at ranges of 12 hours to 2 days. Our research addresses the issues of initial condition uncertainty (ICU) for mesoscale analyses and the merit of calibration of output from ensemble prediction systems (EPSs) by artificial neural networks.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 1998
Accession Number
ADA532394

Entities

People

  • Mary M. Poulton
  • Steven L. Mullen

Organizations

  • University of Arizona

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Atmospheric Sciences
  • Calibration
  • Data Sets
  • Electronic Mail
  • Geographic Regions
  • Kalman Filters
  • Meteorology
  • Neural Networks
  • Precipitation
  • Reliability
  • Signal Processing
  • Standards
  • Statistics
  • Uncertainty
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Neural Network Machine Learning.

Technology Areas

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