Statistical-Dynamical Forecasting of Tropical Cyclogenesis in the North Atlantic at Intraseasonal Lead Times

We have created a combined statistical-dynamical model to predict the probability of tropical cyclone (TC) formation at daily, 2.5 degree horizontal resolution in the North Atlantic (NA) at intraseasonal lead times. Based on prior research and our own analyses, we chose five large-scale environmenta...

Full description

Bibliographic Details
Main Author: Raynak, Chad S.
Other Authors: NAVAL POSTGRADUATE SCHOOL MONTEREY CA
Format: Text
Language:English
Published: 2009
Subjects:
Meteorology
Physical and Dynamic Oceanography
Statistics and Probability
*WEATHER FORECASTING
*TROPICAL CYCLONES
*NORTH ATLANTIC OCEAN
*CYCLOGENESIS
*LONG RANGE(TIME)
REGRESSION ANALYSIS
WIND SHEAR
CLIMATOLOGY
SURFACE TEMPERATURE
CORIOLIS EFFECT
VORTICES
THESES
PROBABILITY
OCEAN SURFACE
MATHEMATICAL MODELS
OCEAN CURRENTS
*TROPICAL CYCLOGENESIS
*INTRASEASONAL FORECASTING
*STATISTICAL-DYNAMICAL MODELS
*HINDCASTING
SMART CLIMATOLOGY
TROPICAL GENESIS PARAMETERS
LARGE-SCALE ENVIRONMENTAL FACTORS
RELATIVE VORTICITY
SEA SURFACE TEMPERATURE
NCEP(NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTION)
NOAA(NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION)
CFS(CLIMATE FORECAST SYSTEM)
North Atlantic
Online Access:http://www.dtic.mil/docs/citations/ADA501774
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA501774
Description
Summary:We have created a combined statistical-dynamical model to predict the probability of tropical cyclone (TC) formation at daily, 2.5 degree horizontal resolution in the North Atlantic (NA) at intraseasonal lead times. Based on prior research and our own analyses, we chose five large-scale environmental factors (LSEFs) to represent favorable environments for TC formation. The LSEFs include 850 mb relative vorticity, sea surface temperature, vertical wind shear, Coriolis, and 200 mb divergence. We used logistic regression to create a statistical model that depicts the probability for TC formation based on these LSEFs. Through verification of zero-lead hindcasts, we determined that our regression model performs better than climatology. For example, these hindcasts had a Brier skill score of 0.04 and a relative operating characteristic skill score of 0.72. We then forced our regression model with LSEF fields from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) to produce non-zero lead hindcasts and forecasts. We conducted a series of case studies to evaluate and study the predictive skill of our regression model, with the results showing that our model produces promising results at intraseasonal lead times.

Similar Items