Long-lead seasonal forecast - where do we stand?
The performance of five ENSO prediction systems are examined: two are dynamical; one is a hybrid coupled model; and two are statistical. With increasing physical understanding, dynamically based forecasts have the potential to become more skillful than purely statistical ones. At a lead time of 6 mo...
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| Veröffentlicht in: | Bulletin of the American Meteorological Society 1994-01, Vol.75 (11), p.2097-2114 |
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| container_issue | 11 |
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| container_title | Bulletin of the American Meteorological Society |
| container_volume | 75 |
| creator | Barnston, Anthony G van den Dool, Huug M Zebiak, Stephen E Barnett, Tim P Ji, Ming Rodenhuis, David R Cane, Mark A Leetmaa, Ants Graham, Nicholas E Ropelewski, Chester R Kousky, Vernon E O'Lenic, Edward A Livezey, Robert E |
| description | The performance of five ENSO prediction systems are examined: two are dynamical; one is a hybrid coupled model; and two are statistical. With increasing physical understanding, dynamically based forecasts have the potential to become more skillful than purely statistical ones. At a lead time of 6 months, the SST forecasts have an overall correlation skill in the 0.60s for 1982-93, which easily outperforms persistence and is regarded as useful. Both types of forecasts are not much better than local higher-order autoregressive controls. However, continual progress is being made in understanding relations among global oceanic and atmospheric climate-scale anomaly fields. |
| format | Article |
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| ispartof | Bulletin of the American Meteorological Society, 1994-01, Vol.75 (11), p.2097-2114 |
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| source | Jstor Complete Legacy; American Meteorological Society; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| title | Long-lead seasonal forecast - where do we stand? |
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