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

• 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

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.