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 - Publisher Copyright:
© 2023 The Shanghai Typhoon Institute of China Meteorological Administration
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.
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.
KW - Climate services
KW - Seasonal forecasting
KW - Tropical cyclones
UR - http://www.scopus.com/inward/record.url?scp=85174356613&partnerID=8YFLogxK
U2 - 10.1016/j.tcrr.2023.09.003
DO - 10.1016/j.tcrr.2023.09.003
M3 - Article
AN - SCOPUS:85174356613
SN - 2225-6032
VL - 12
SP - 182
EP - 199
JO - Tropical Cyclone Research and Review
JF - Tropical Cyclone Research and Review
IS - 3
ER -