Global assessment of tropical cyclone intensity statistical-dynamical hindcasts

• Published in: Quarterly Journal of the Royal Meteorological Society
• Main Authors: Neetu, Suresh, Lengaigne, Matthieu, Menon, H. B., Vialard, Jérôme, Mangeas, M., Menkès, Christophe E., Ali, M. M., Suresh, Iyyappan, Knaff, J. A.
• Other Authors: National Institute of Oceanography (NIO), Council of Scientific and Industrial Research India (CSIR), Indo-French Cell for Water Sciences (IFCWS), Indian Institute of Science Bangalore (IISc Bangalore), Processus de la variabilité climatique tropicale et impacts (PARVATI), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Goa University, Institut de Recherche pour le Développement (IRD Nouvelle-Calédonie ), Processus de couplage à Petite Echelle, Ecosystèmes et Prédateurs Supérieurs (PEPS), Florida State University Tallahassee (FSU), NOAA Center for Satellite Applications and Research (STAR), NOAA National Environmental Satellite, Data, and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA)-National Oceanic and Atmospheric Administration (NOAA)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2017
• Subjects: atmospheric predictors; tropical cyclone; statistical model; multiple linear regression; intensity forecast; [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph]; North Atlantic
• Online Access: https://hal.science/hal-01630522; https://doi.org/10.1002/qj.3073

International audience This paper assesses the characteristics of linear statistical models developed for tropical cyclone (TC) intensity prediction at global scale. To that end, multilinear regression models are developed separately for each TC-prone basin to estimate the intensification of a TC given its initial characteristics and environmental parameters along its track. We use identical large-scale environmental parameters in all basins, derived from a 1979–2012 reanalysis product. The resulting models display comparable skill to previously described similar hindcast schemes. Although the resulting mean absolute errors are rather similar in all basins, the models beat persistence by 20–40% in most basins, except in the North Atlantic and northern Indian Ocean, where the skill gain is weaker (10–25%). A large fraction (60–80%) of the skill gain arises from the TC characteristics (intensity and its rate of change) at the beginning of the forecast. Vertical shear followed by the maximum potential intensity are the environmental parameters that yield most skill globally, but with individual contributions that strongly depend on the basin. Hindcast models built from environmental predictors calculated from their seasonal climatology perform almost as well as using real-time values. This has the potential to considerably simplify the implementation of operational forecasts in such models. Finally, these models perform poorly to predict intensity changes for Category 2 and weaker TCs, while they are 2–4 times more skilful for the strongest TCs (Category 3 and above). This suggests that these linear models do not properly capture the processes controlling the early stages of TC intensification.