Objective Identification of Environmental Patterns Related to Tropical Cyclone Track Forecast Errors

Abstract

The increase in skill of numerical model guidance and the use of consensus forecast techniques have led to significant improvements in the accuracy of tropical cyclone track forecasts at ranges beyond 72 hours. Identification of instances when the forecast track from an individual numerical model may be in error could lead to additional improvement in the accuracy of tropical cyclone track forecasts. An objective methodology is tested to characterize the spread among the three primary global numerical model forecast tracks used as guidance by the Joint Typhoon Warning Center. Statistically significant principal components derived from empirical orthogonal functions of mid-tropospheric height and vorticity forecast fields identify cases of large spread among model forecasts. Cases in which the three-model average forecast track resulted in a large error were characterized by a distribution of principal components such that one component was significantly different from the other two. Removal of the forecast track associated with the outlying principal component resulted in a reduced forecast error. Therefore, the objective methodology may be utilized to define a selective consensus by removing forecast tracks from consideration based on the projection of forecast fields onto empirical orthogonal functions and inspecting the distribution of the resulting principal components.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA457263

Entities

People

  • Elizabeth R. Sanabia

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Accuracy
  • California
  • Case Studies
  • Correlation Techniques
  • Cyclones
  • Databases
  • Errors
  • Grids
  • Guidance
  • Identification
  • Information Science
  • North Pacific Ocean
  • Sea Level
  • Statistical Analysis
  • Tropical Cyclones
  • United States
  • United States Naval Academy

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Computational Modeling and Simulation
  • Systems Analysis and Design