Work

Simple Yet Effective: A Comparative Study of Statistical Models for Yearly Hurricane Forecasting

Year: 2025

Type: article

Abstract: ABSTRACT In this article, we study the problem of forecasting the next year's number of Atlantic hurricanes, which is relevant in many fields of applications such as land‐use planning, hazard mitigation, reinsurance and long‐term weather derivative market. Considering a set of well‐known predictors, we compare the forecasting accuracy of both machine learning and classical statistical models, showing that the latter may be more adequate than the first. Quantile regression models, which are adopted for the first time for forecasting hurricane numbers, provide the best results. Moreover, we construct a new index showing good properties in anticipating the direction of the future number of hurricanes. We consider different evaluation metrics based on both magnitude forecasting errors and directional accuracy.

Source: Environmetrics

DOI: 10.1002/env.70009

Authors: Pietro Colombo, Raffaele Mattera, Philipp Otto

Institutions: University of Glasgow, University of Campania "Luigi Vanvitelli"

Language: English

Topic: Tropical and Extratropical Cyclones Research

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $4,780