Geoscience Reference
In-Depth Information
Prospects for Improving the
Operational Seasonal Prediction of
Tropical Cyclone Activity in the
Southern Hemisphere
Y. Kuleshov
1,2
*, Y. Wang
2
, J. Apajee
2
,
R. Fawcett
1,3
and D. Jones
1
1
National Climate Centre, Australian Bureau of
Meteorology, Melbourne, Australia
2
School of Mathematical and Geospatial Sciences, Royal Melbourne
Institute of Technology (RMIT) University, Melbourne, Australia
3
Centre for Australian Weather and Climate Research, Australian
Bureau of Meteorology, Melbourne, Australia
*e-mail: Y.Kuleshov@bom.gov.au
1. Introduction
Tropical cyclones (TCs) are the most destructive weather phenomena to impact
on tropical regions. Reliable prediction of seasonal TC activity is important
for preparedness of coastal communities of Australia and island nations in the
Pacific and Indian Oceans ahead of the coming cyclone season. Over recent
decades, statistical model-based methods for prediction of TC activity in the
coming season have been developed for a number of regions in various ocean
basins, starting with the pioneering work of Gray (1979). Statistical models
explore relationships between large-scale environmental drivers which modulate
TC activity, for example the El Niño-Southern Oscillation (ENSO)
phenomenon, and observed numbers of TCs to derive linear regression equations
which can be used for prediction of future cyclone activity. Indices such as the
Southern Oscillation Index (SOI) and sea surface temperatures (SSTs) in some
oceanic areas are commonly used to build such statistical models. However,
there are two major constraints associated with the statistical model-based
approach. Firstly, accurate historical cyclone records (ideally records covering
a reasonably long period of time) are required. Secondly, in a globally warming
environment, statistical relationships based on historical data may not produce
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