Meteorological and Model Traits Knowledge Bases for North Indian Ocean Tropical Cyclones

Abstract

These tropical cyclone Meteorology and Model Traits Knowledge Bases for the North Indian Ocean (NI0) complete the global coverage required for application of the Systematic Approach to tropical cyclone track forecasting introduced by Carr and Elsberry (1994). The database for the NIO Meteorological Knowledge Base includes 64 storms during 1991-2001, All of the 656 cases could be classified in three common synoptic patterns that have been found to apply in other basins, with no unique patterns. About 75% of the cases are in the standard pattern. This preliminary Model Traits Knowledge Base includes only eight tropical cyclones during 2000-2001, The model forecast track errors are relatively small, and only 33%, 62%, and 70% of the Navy global, regional, and UK Meteorological Office models, respectively, exceed 150 n mi at 72 h. Only 12 cases of large (>225 n mi at 72 h) track forecast errors occur. Half of these large-error cases are associated with erroneous model predictions in the midlatitudes. One third of the large errors originate from improper model treatments of the tropical circulations, and the remaining two cases originate from erroneous initial cyclone positions. Since these are the same error sources as in other basins, the Systematic Approach has global application.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2002
Accession Number
ADA408190

Entities

People

  • Rachael A. Spollen

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Arabian Sea
  • Boundaries
  • Case Studies
  • Cyclones
  • Databases
  • Geography
  • Indian Ocean
  • Meteorology
  • Oceans
  • Ridges
  • Sea Level Rise
  • Standards
  • Storm Surges
  • Terrain
  • Test And Evaluation
  • Topography
  • Tropical Cyclones

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Geospatial Intelligence and Artificial Intelligence Analytics