Evaluating Tropical Cyclone Forecast Track Uncertainty Using A Grand Ensemble of Ensemble Prediction Systems

Abstract

The skill of a combined grand ensemble (GE), which is constructed from three operational global ensemble prediction systems (EPS), is evaluated with respect to the probability forecast of a tropical cyclone (TC) being within a specified area. Anisotropic probability ellipses are defined from the GE to contain 68% of the ensemble members. Forecast reliability is based on whether the forecast verifying position is within the ellipse. A sharpness parameter is based on the size of the GE-based probability ellipse relative to other operational forecast probability ellipses. For the 2010 Atlantic TC season, results indicate that the GE ellipses exhibit a high degree of reliability whereas the operational probability circle tends to be over-dispersive. Additionally, the GE ellipse tends to be sharper than the operational product for forecast intervals beyond 48 hours. The size and shape of the GE ellipses varied with TC track types, which suggests that information about the physics of the flow-dependent system is retained whereas isotropic probability ellipses may not reflect variability associated with track type. It is concluded that the GE probability ellipse demonstrates utility for combined EPS to enhance probabilistic forecasts for use as TC-related decision aids, as there is a potential for reducing the sizes of warning areas.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2011
Accession Number
ADA551928

Entities

People

  • Douglas W. Pearman

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Case Studies
  • Computational Science
  • Cyclones
  • Databases
  • Equations
  • Intervals
  • Meteorology
  • North America
  • Probability
  • Reliability
  • Sharpness
  • Spatial Distribution
  • Statistics
  • Tropical Cyclones
  • Uncertainty
  • United States
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Regression Analysis.
  • Thin Film Deposition Science.