Sub-seasonal to seasonal

Recent advances in seasonal and multi-annual tropical cyclone forecasting

Yuhei Takaya, Louis‐Philippe Caron, Eric S. Blake, François Bonnardot, Nicolas Bruneau, Joanne Camp, Johnny C. L. Chan, Paul Gregory, Jhordanne J. Jones, Nam-Young Kang, Philip J. Klotzbach, Yuriy Kuleshov et al.

Tropical Cyclone Research and Review · 01 September 2023 · NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), NSF NCAR / UCAR, Met Office, Météo-France (incl. CNRM), Colorado State University (incl. CIRA), China Meteorological Administration (CMA), Japan Meteorological Agency (JMA), Bureau of Meteorology (Australia)

Paper

In one sentence. Reviews seasonal and multi-annual tropical cyclone forecasting, including emerging ML/AI methods, ENSO-neutral predictability and landfall skill gaps relative to user needs.

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.

Labels

Topic

Sub-seasonal to seasonal, Extremes & hazards

Type

Review, survey or tutorial

Method

Classical ML (trees, GP, linear, kernels)

Time scale

Seasonal to annual

Domain

Global

Data

NWP analysis or forecasts (IFS, GFS, ...)

Status

Operational

Identifiers

DOI:10.1016/j.tcrr.2023.09.003 OpenAlex:W4386813576

Added

2026-10-22

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