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Neetu S., Lengaigne Matthieu, Vialard Jérôme, Mangeas Morgan, Menkès Christophe, Suresh I., Leloup J., Knaff J. A. (2020). Quantifying the benefits of nonlinear methods for global statistical hindcasts of tropical cyclones intensity. Weather and Forecasting, 35 (3), 807-820. ISSN 0882-8156

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Lien direct chez l'éditeur doi:10.1175/waf-d-19-0163.1

Titre
Quantifying the benefits of nonlinear methods for global statistical hindcasts of tropical cyclones intensity
Année de publication2020
Type de documentArticle référencé dans le Web of Science WOS:000541591200003
AuteursNeetu S., Lengaigne Matthieu, Vialard Jérôme, Mangeas Morgan, Menkès Christophe, Suresh I., Leloup J., Knaff J. A.
SourceWeather and Forecasting, 2020, 35 (3), p. 807-820. ISSN 0882-8156
RésuméWhile tropical cyclone (TC) track forecasts have become increasingly accurate over recent decades, intensity forecasts from both numerical models and statistical schemes have been trailing behind. Most operational statistical-dynamical forecasts of TC intensity use linear regression to relate the initial TC characteristics and most relevant large-scale environmental parameters along the TC track to the TC intensification rate. Yet, many physical processes involved in TC intensification are nonlinear, hence potentially hindering the skill of those linear schemes. Here, we develop two nonlinear TC intensity hindcast schemes, for the first time globally. These schemes are based on either support vector machine (SVM) or artificial neural network (ANN) algorithms. Contrary to linear schemes, which perform slightly better when trained individually over each TC basin, nonlinear methods perform best when trained globally. Globally trained nonlinear schemes improve TC intensity hindcasts relative to regionally trained linear schemes in all TC-prone basins, especially the SVM scheme for which this improvement reaches similar to 10% globally. The SVM scheme, in particular, partially corrects the tendency of the linear scheme to underperform for moderate intensity (category 2 and less on the Saffir-Simpson scale) and decaying TCs. Although the TC intensity hindcast skill improvements described above are an upper limit of what could be achieved operationally (when using forecasted TC tracks and environmental parameters), it is comparable to that achieved by operational forecasts over the last 20 years. This improvement is sufficiently large to motivate more testing of nonlinear methods for statistical TC intensity prediction at operational centers.
Plan de classementLimnologie physique / Océanographie physique [032] ; Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Informatique [122]
LocalisationFonds IRD [F B010079135]
Identifiant IRDfdi:010079135
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010079135

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