Open AccessThe Development and Evaluation of a Statistical–Dynamical Tropical Cyclone Genesis Guidance Tool
Author(s) -
Daniel J. Halperin,
Robert E. Hart,
Henry E. Fuelberg,
Joshua H. Cossuth
Publication year - 2016
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/waf-d-16-0072.1
Subject(s) - tropical cyclone , probabilistic logic , global forecast system , tropical cyclone forecast model , climatology , meteorology , brier score , environmental science , numerical weather prediction , probabilistic forecasting , consensus forecast , computer science , econometrics , mathematics , geology , geography , machine learning , artificial intelligence
The National Hurricane Center (NHC) has stated that guidance on tropical cyclone (TC) genesis is an operational forecast improvement need, particularly since numerical weather prediction models produce TC-like features and operationally required forecast lead times recently have increased. Using previously defined criteria for TC genesis in global models, this study bias corrects TC genesis forecasts from global models using multiple logistic regression. The derived regression equations provide 48- and 120-h probabilistic genesis forecasts for each TC genesis event that occurs in the Environment Canada Global Environmental Multiscale Model (CMC), the NCEP Global Forecast System (GFS), and the Met Office's global model (UKMET). Results show select global model output variables are good discriminators between successful and unsuccessful TC genesis forecasts. Independent verification of the regression-based probabilistic genesis forecasts during 2014 and 2015 are presented. Brier scores and reliability diagrams indicate that the forecasts generally are well calibrated and can be used as guidance for NHC’s Tropical Weather Outlook product. The regression-based TC genesis forecasts are available in real time online.