Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI

Publication Type  Journal Article
Year of Publication  2009
Authors  Camargo, S. J.; Barnston, A. G.
Journal Title  Weather and Forecasting
Volume  24
Issue  2
Pages  472-491
Journal Date  Apr
ISBN Number  0882-8156
Accession Number  ISI:000265787000007
Key Words  general-circulation model; western north pacific; atlantic hurricane activity; storm frequency; gcm integrations; climate-change; el-nino; interdecadal variability; statistical prediction; simulation
Abstract  

The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. 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 previous month's observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr 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 levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2-3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters' subjective elimination of the "overconfidence'' bias in the model's forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered.

Notes  

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URL  <Go to ISI>://000265787000007
DOI  Doi 10.1175/2008waf2007099.1