{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:31:56Z","timestamp":1760232716481,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A novel model selection and averaging approach is proposed\u2014through integrating the corrected Akaike information criterion (AICc), the Gibbs sampler, and the Poisson regression models, to improve tropical cyclone seasonal forecasting in the Australian and the South Pacific Ocean regions and sub-regions. It has been found by the new approach that indices which describe tropical cyclone inter-annual variability such as the Dipole Mode Index (DMI) and the El Ni\u00f1o Modoki Index (EMI) are among the most important predictors used by the selected models. The core computational method underlying the proposed approach is a new stochastic search algorithm that we have developed, and is named Metropolis\u2013Gibbs random scan (MGRS). By applying MGRS to minimize AICc over all candidate models, a set of the most important predictors are identified which can form a small number of optimal Poisson regression models. These optimal models are then averaged to improve their overall predictability. Results from our case study of tropical cyclone seasonal forecasting show that the MGRS-AICc method performs significantly better than the commonly used step-wise AICc method.<\/jats:p>","DOI":"10.3390\/rs14225872","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:33:32Z","timestamp":1669005212000},"page":"5872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving Methodology for Tropical Cyclone Seasonal Forecasting in the Australian and the South Pacific Ocean Regions by Selecting and Averaging Models via Metropolis\u2013Gibbs Sampling"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3818-0564","authenticated-orcid":false,"given":"Guoqi","family":"Qian","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8326-6781","authenticated-orcid":false,"given":"Lizhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3241-1667","authenticated-orcid":false,"given":"Yuriy","family":"Kuleshov","sequence":"additional","affiliation":[{"name":"Bureau of Meteorology, Docklands, VIC 3008, Australia"},{"name":"SPACE Research Centre, School of Science, Royal Melbourne Institute of Technology (RMIT University), Melbourne, VIC 3000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"ref_1","first-page":"675","article-title":"Seasonal prediction of climate extremes for the Pacific: Tropical cyclones and extreme ocean temperatures","volume":"20","author":"Kuleshov","year":"2012","journal-title":"J. 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