Tropical cyclone track forecasting techniques - A review

摘要

Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and clima tological characteristics of the various cyclone formation basins are not similar, which entail that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific tech niques to increase the forecast accuracy. As cyclone track is governed by a range of factors var iations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth's rotational force—the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cy clones and where human casualties are the highest.