Predicting Tropical Cyclone Genesis

Abstract

The long-term goal of this research is to provide probabilistic genesis forecast guidance to operational forecasters and develop a genesis index for operational dynamical model prediction of tropical cyclone (TC) genesis. Once regions of high TC genesis probability are identified, a movable, multi-nested version of COAMPS with resolution of roughly 3 km or less in the inner most grid will be used for predicting the genesis event. The objective of this project is to develop a statistical TC genesis model that is capable of separating developing and nondeveloping tropical disturbances. A TC genesis index will be constructed to provide the probability of cyclogenesis, based on NOGAPS global analysis and forecast fields. Our approach is to identify distinctive characteristics associated with developing and nondeveloping disturbances in the tropical western North Pacific and Atlantic oceans. A box-difference index (BDI) is introduced to quantitatively determine the relative importance of dynamic and thermodynamic parameters in determining the genesis events. Once key genesis parameters in different basins are determined, we can obtain several nonlinear logistic regression models with different combinations of these predictors. We finally apply BIC (Bayesian Information Criterion) on these models to optimally determine the best model for TC genesis probability forecast at different basins.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2011
Accession Number
ADA557271

Entities

People

  • James Hansen
  • Melinda S. Peng
  • Tim Li

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Atlantic Ocean
  • Atmospheric Motion
  • Biological Phenomena
  • Cyclones
  • Ecological And Environmental Phenomena
  • Ecological And Environmental Processes
  • Electronic Mail
  • False Alarms
  • Guidance
  • High Resolution
  • Information Operations
  • Military Research
  • Probability
  • Test And Evaluation
  • Tropical Cyclones
  • Warning Systems
  • Water Vapor

Readers

  • Atmospheric Science/Meteorology
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference