Improving Methodology for Tropical Cyclone Seasonal Forecasting in the Australian and the South Pacific Ocean Regions by Selecting and Averaging Models via Metropolis–Gibbs Sampling

نویسندگان

چکیده

A novel model selection and averaging approach is proposed—through integrating the corrected Akaike information criterion (AICc), Gibbs sampler, Poisson regression models, to improve tropical cyclone seasonal forecasting in Australian South Pacific Ocean regions sub-regions. It has been found by new that indices which describe inter-annual variability such as Dipole Mode Index (DMI) El Niño Modoki (EMI) are among most important predictors used selected models. The core computational method underlying proposed a stochastic search algorithm we have developed, named Metropolis–Gibbs random scan (MGRS). By applying MGRS minimize AICc over all candidate set of identified can form small number optimal These models then averaged their overall predictability. Results from our case study show MGRS-AICc performs significantly better than commonly step-wise method.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14225872