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Description and Skill Evaluation of Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI

Suzana J. Camargo; Anthony G. Barnston; Columbia University. International Research Institute for Climate and Society

Title:
Description and Skill Evaluation of Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI
Author(s):
Camargo, Suzana J.; Barnston, Anthony G.; Columbia University. International Research Institute for Climate and Society
Date:
Type:
Technical reports
Department:
International Research Institute for Climate and Society
Permanent URL:
Series:
IRI Technical Report
Part Number:
08-02
Abstract:
The International Research Institute for Climate and Society has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper we describe the method used to obtain these forecasts, and evaluate their performance. The forecasts are based on tropical cyclone-like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the month ending at the forecast start time, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors observed SSTs preceding the forecast start time. For the recent 6-year period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued. Despite variations from one basin to another, the hindcast skills of the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent. The dynamical forecasts require statistical post-prossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Hence, during the recent period of real-time forecasts, the subjective forecasts are found to have resulted in probabilistic skill better than that of the raw model output, primarily because of the forecasters' elimination of the systematic bias of "overconfidence" in the model's forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered.
Subject(s):
Atmospheric sciences
Item views:
212
Metadata:
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