Recent advances in seasonal and multi-annual tropical cyclone forecasting

Yuhei Takaya; Louis-Philippe Caron; Eric Blake; François Bonnardot; Nicolas Bruneau; Joanne Camp; Johnny Chan; Paul Gregory; Jhordanne J. Jones; Namyoung Kang; Philip J. Klotzbach; Yuriy Kuleshov; Marie-Dominique Leroux; Julia F. Lockwood; Hiroyuki Murakami; Akio Nishimura; Dushmanta R. Pattanaik; Tom J. Philp; Yohan Ruprich-Robert; Ralf Toumi; Frédéric Vitart; Seonghee Won; Ruifen Zhan

doi:10.1016/j.tcrr.2023.09.003

Recent advances in seasonal and multi-annual tropical cyclone forecasting

Yuhei Takaya, Louis-Philippe Caron^*, Eric Blake, François Bonnardot, Nicolas Bruneau, Joanne Camp, Johnny Chan, Paul Gregory, Jhordanne J. Jones, Namyoung Kang, Philip J. Klotzbach, Yuriy Kuleshov, Marie-Dominique Leroux, Julia F. Lockwood, Hiroyuki Murakami, Akio Nishimura, Dushmanta R. Pattanaik, Tom J. Philp, Yohan Ruprich-Robert, Ralf ToumiFrédéric Vitart, Seonghee Won, Ruifen Zhan

^*Corresponding author for this work

School of Energy and Environment

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

18 Citations (Scopus)

38 Downloads (CityUHK Scholars)

Abstract

Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Niño–Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products. © 2023 The Shanghai Typhoon Institute of China Meteorological Administration.

Original language	English
Pages (from-to)	182-199
Journal	Tropical Cyclone Research and Review
Volume	12
Issue number	3
Online published	17 Sept 2023
DOIs	https://doi.org/10.1016/j.tcrr.2023.09.003
Publication status	Published - Sept 2023

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

Climate services
Seasonal forecasting
Tropical cyclones

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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Takaya, Y., Caron, L.-P., Blake, E., Bonnardot, F., Bruneau, N., Camp, J., Chan, J., Gregory, P., Jones, J. J., Kang, N., Klotzbach, P. J., Kuleshov, Y., Leroux, M.-D., Lockwood, J. F., Murakami, H., Nishimura, A., Pattanaik, D. R., Philp, T. J., Ruprich-Robert, Y., ... Zhan, R. (2023). Recent advances in seasonal and multi-annual tropical cyclone forecasting. Tropical Cyclone Research and Review, 12(3), 182-199. https://doi.org/10.1016/j.tcrr.2023.09.003

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title = "Recent advances in seasonal and multi-annual tropical cyclone forecasting",

abstract = "Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Ni{\~n}o–Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products. {\textcopyright} 2023 The Shanghai Typhoon Institute of China Meteorological Administration.",

keywords = "Climate services, Seasonal forecasting, Tropical cyclones",

author = "Yuhei Takaya and Louis-Philippe Caron and Eric Blake and Fran{\c c}ois Bonnardot and Nicolas Bruneau and Joanne Camp and Johnny Chan and Paul Gregory and Jones, {Jhordanne J.} and Namyoung Kang and Klotzbach, {Philip J.} and Yuriy Kuleshov and Marie-Dominique Leroux and Lockwood, {Julia F.} and Hiroyuki Murakami and Akio Nishimura and Pattanaik, {Dushmanta R.} and Philp, {Tom J.} and Yohan Ruprich-Robert and Ralf Toumi and Fr{\'e}d{\'e}ric Vitart and Seonghee Won and Ruifen Zhan",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).",

year = "2023",

month = sep,

doi = "10.1016/j.tcrr.2023.09.003",

language = "English",

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Takaya, Y, Caron, L-P, Blake, E, Bonnardot, F, Bruneau, N, Camp, J, Chan, J, Gregory, P, Jones, JJ, Kang, N, Klotzbach, PJ, Kuleshov, Y, Leroux, M-D, Lockwood, JF, Murakami, H, Nishimura, A, Pattanaik, DR, Philp, TJ, Ruprich-Robert, Y, Toumi, R, Vitart, F, Won, S & Zhan, R 2023, 'Recent advances in seasonal and multi-annual tropical cyclone forecasting', Tropical Cyclone Research and Review, vol. 12, no. 3, pp. 182-199. https://doi.org/10.1016/j.tcrr.2023.09.003

TY - JOUR

T1 - Recent advances in seasonal and multi-annual tropical cyclone forecasting

AU - Takaya, Yuhei

AU - Caron, Louis-Philippe

AU - Blake, Eric

AU - Bonnardot, François

AU - Bruneau, Nicolas

AU - Camp, Joanne

AU - Chan, Johnny

AU - Gregory, Paul

AU - Jones, Jhordanne J.

AU - Kang, Namyoung

AU - Klotzbach, Philip J.

AU - Kuleshov, Yuriy

AU - Leroux, Marie-Dominique

AU - Lockwood, Julia F.

AU - Murakami, Hiroyuki

AU - Nishimura, Akio

AU - Pattanaik, Dushmanta R.

AU - Philp, Tom J.

AU - Ruprich-Robert, Yohan

AU - Toumi, Ralf

AU - Vitart, Frédéric

AU - Won, Seonghee

AU - Zhan, Ruifen

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2023/9

Y1 - 2023/9

N2 - Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Niño–Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products. © 2023 The Shanghai Typhoon Institute of China Meteorological Administration.

AB - Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Niño–Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products. © 2023 The Shanghai Typhoon Institute of China Meteorological Administration.

KW - Climate services

KW - Seasonal forecasting

KW - Tropical cyclones

UR - http://www.scopus.com/inward/record.url?scp=85174356613&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85174356613&origin=recordpage

U2 - 10.1016/j.tcrr.2023.09.003

DO - 10.1016/j.tcrr.2023.09.003

M3 - RGC 21 - Publication in refereed journal

SN - 2225-6032

VL - 12

SP - 182

EP - 199

JO - Tropical Cyclone Research and Review

JF - Tropical Cyclone Research and Review

IS - 3

ER -

Recent advances in seasonal and multi-annual tropical cyclone forecasting

Abstract

Bibliographical note

Research Keywords

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