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5 - Subseasonal to Seasonal Tropical Cyclone Prediction

Published online by Cambridge University Press:  17 February 2022

Pao-Shin Chu  and
Hiroyuki Murakami
Pao-Shin Chu
Affiliation:
University of Hawaii, Manoa
Hiroyuki Murakami
Affiliation:
UCAR
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Summary

This chapter provides a current knowledge regarding the state of TC prediction on subseasonal to seasonal time scales. The TC forecasting methods can be generally classified into statistical, dynamical, and statistical-dynamical approaches. Statistical methods are generally based on logistic regression, multiple linear regression, Poisson regression, and the Poisson regression cast in the Bayesian paradigm. Dynamical forecast methods rely on dynamical climate models using either atmospheric general circulation models (GCMs) forced by observed or predicted SST anomalies, or coupled atmosphere–ocean models. Statistical-dynamical methods are a hybrid approach by combining forecast information from dynamical models and the observed statistical relationship between TC and environmental conditions to forecast TC changes.

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Climate Variability and Tropical Cyclone Activity , pp. 201 - 235
Publisher: Cambridge University Press
Print publication year: 2022

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