- Title
- Subseasonal prediction framework for tropical cyclone activity in the Solomon Islands region
- Creator
- Haruhiru, Alick; Chand, Savin; Sultanova, Nargiz; Ramsay, Hamish; Sharma, Krishneel; Tahani, Lloyd
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198365
- Identifier
- vital:19035
- Identifier
-
https://doi.org/10.1002/joc.8173
- Identifier
- ISSN:0899-8418 (ISSN)
- Abstract
- Recently, we developed seasonal prediction schemes with improved skill to predict tropical cyclone (TC) activity up to 3 months in advance for the Solomon Islands (SI) region (5°–15°S, 155°–170°E) using sophisticated Bayesian regression techniques. However, TC prediction at subseasonal timescale (i.e., 1–4 weeks in advance) is not being researched for that region despite growing demands from decision makers at sectoral level. In this paper, we first assess the feasibility of developing subseasonal prediction frameworks for the SI region using a pool of predictors that are known to affect TC activity in the region. We then evaluate multiple predictor combinations to develop the most appropriate models using a statistical approach to forecast weekly TC activity up to 4 weeks in advance. Predictors used include indices of various natural climate variability modes, namely the Madden–Julian Oscillation (MJO), the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and the Interdecadal Pacific Oscillation (IPO). These modes often have robust physical and statistical relationships with TC occurrences in the SI region and the broader southwest Pacific territory as shown by preceding studies. Additionally, we incorporate TC seasonality as a potential predictor given the persistence of TCs occurring more in certain months than others. Note that a model with seasonality predictor alone (hereafter called the “climatology” model) forms a baseline for comparisons. The hindcast verifications of the forecasts using leave-one-out cross-validation procedure over the study period 1975–2019 indicate considerable improvements in prediction skill of our logistic regression models over climatology, even up to 4 weeks in advance. This study sets the foundation for introducing subseasonal prediction services, which is a national priority for improved decision making in sectors like agriculture and food security, water, health and disaster risk mitigation in the Solomon Islands. © 2023 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
- Publisher
- John Wiley and Sons Ltd
- Relation
- International Journal of Climatology Vol. 43, no. 12 (2023), p. 5763-5777
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 The Authors
- Rights
- Open Access
- Subject
- 3701 Atmospheric sciences; 3702 Climate change science; 3707 Hydrology; El Niño–Southern Oscillation; Madden–Julian Oscillation; Subseasonal prediction; Tropical cyclones
- Full Text
- Reviewed
- Funder
- Alick Haruhiru is grateful to the Australian Government, for funding his PhD, through the Australian Award Scholarship, at Federation University Australia. We also acknowledge funding from the Climate Systems Hub of the Australian Government’s National Environmental Science Program (NESP). Open access publishing facilitated by Federation University Australia, as part of the Wiley - Federation University Australia agreement via the Council of Australian University Librarians.
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