Improving Extended-Range Seasonal Predictions of Intense Atlantic Hurricane Activity

Elsner, James B.; Schmertmann, Carl P.

doi:10.1175/1520-0434(1993)008<0345:ierspo>2.0.co;2

Cited by 113 publications

(121 citation statements)

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“…One way to answer this question is to perform a Poisson regression of counts on the large-scale flow indices, as has been done in previous studies but generally only for landfall or basinwide counts (20); also see refs. [21][22][23][24][25][26][27][28][29].…”

Section: Resultsmentioning

confidence: 99%

Temporal clustering of tropical cyclones and its ecosystem impacts

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Stephenson

2011

Proc. Natl. Acad. Sci. U.S.A.

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Tropical cyclones have massive economic, social, and ecological impacts, and models of their occurrence influence many planning activities from setting insurance premiums to conservation planning. Most impact models allow for geographically varying cyclone rates but assume that individual storm events occur randomly with constant rate in time. This study analyzes the statistical properties of Atlantic tropical cyclones and shows that local cyclone counts vary in time, with periods of elevated activity followed by relative quiescence. Such temporal clustering is particularly strong in the Caribbean Sea, along the coasts of Belize, Honduras, Costa Rica, Jamaica, the southwest of Haiti, and in the main hurricane development region in the North Atlantic between Africa and the Caribbean. Failing to recognize this natural nonstationarity in cyclone rates can give inaccurate impact predictions. We demonstrate this by exploring cyclone impacts on coral reefs. For a given cyclone rate, we find that clustered events have a less detrimental impact than independent random events. Predictions using a standard random hurricane model were overly pessimistic, predicting reef degradation more than a decade earlier than that expected under clustered disturbance. The presence of clustering allows coral reefs more time to recover to healthier states, but the impacts of clustering will vary from one ecosystem to another.climate change | climate variability | multidecadal variability T he devastating economic, social, and ecological impacts of tropical cyclones are well established (1-3). Estimates of hurricane rates are needed to model the dynamics of many ecological (4-6), social (7), and economic (8) processes. A key implicit assumption of virtually all such models is that cyclones occur randomly in time with a constant rate that can vary geographically. Using a century of cyclone tracks from the Atlantic (9), we begin by testing whether such a model of hurricanes is indeed appropriate. In areas where such models are found to be inappropriate, because hurricane events are in fact clustered in time rather than obeying a constant rate, we then investigate whether this departure from a Poisson process matters when predicting the health of Caribbean coral reefs. Important theories of disturbance ecology originated from coral reefs (2), making them a convenient system to pose this question. It should be noted that clustering of natural hazards such as hurricanes can also have a profound impact on nonecosystem features: For example, clustering can induce a substantially enhanced probability of multiple large insured losses within the duration of a single reinsurance contract (10). Results and DiscussionClustering of Hurricanes. We examine tropical cyclone tracks from the Atlantic Basin Hurricane Database (HURDAT) between 1901 and 2010. Following the approach of Villarini et al. (11), we consider tracks lasting only for more than 2 d, to address partially the issues that have been raised about the quality of the HURDAT database in the ea...

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Section: Resultsmentioning

confidence: 99%

Temporal clustering of tropical cyclones and its ecosystem impacts

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Vitolo

Stephenson

2011

Proc. Natl. Acad. Sci. U.S.A.

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“…Predictions for Atlantic hurricane activity and intensity have been made in several prior studies (Blake and Gray 2004;Elsner and Schmertmann 1993;Gray 1984a,b;Gray et al 1992;Gray et al 1993;Gray et al 1994;Klotzbach and Gray 2004;Klotzbach 2007;Knaff 1997Knaff , 1998Zhang and Kieu 2005). Gray (1984b) developed the first prediction model for annual Atlantic hurricane activity based on three predictors during 1950-82 (33 yr), including the phase of stratospheric quasi-biennial oscillation (QBO), the occurrence and strength of El Niñ o, and the mean April-May SLP anomalies over the Caribbean Basin.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model for Atlantic Named Storm Frequency Using a Year-by-Year Increment Approach

Fan

2010

Weather and Forecasting

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This paper presents a year-by-year incremental approach to forecasting the Atlantic named storm frequency (ATSF) for the hurricane season (1 June-30 November). The year-by-year increase or decrease in the ATSF is first forecasted to yield a net ATSF prediction. Six key predictors for the year-by-year increment in the number of Atlantic named tropical storms have been identified that are available before 1 May. The forecast model for the year-by-year increment of the ATSF is first established using a multilinear regression method based on data taken from the years 1965-99, and the forecast model of the ATSF is then derived. The prediction model for the ATSF shows good prediction skill. Compared to the climatological average mean absolute error (MAE) of 4.1, the percentage improvement in the MAE is 29% for the hindcast period of 2004-09 and 46% for the cross-validation test from 1985 to 2009 (26 yr). This work demonstrates that the year-byyear incremental approach has the potential to improve the operational forecasting skill for the ATSF.

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“…Annual Hurricane Occurrence (AHO) rate estimates "the frequency of hurricanes occurring in a series of years based on an associated hurricane occurrence probability distribution, which is obtained through statistical analysis and calculation on the basis of historical hurricane records" (Chen et al, 2004, p. 6). In the recent years, substantial research in the area of modeling the occurrence of hurricanes has been done by Chen et al (2003Chen et al ( , 2004, Gray, Landsea, Mielke, and Berry (1992), Elsner and Schmertmann (1993), and Elsner and Jagger (2004). The basic principle of these papers was to develop the statistical models from the available historical data in order to estimate AHO.…”

Section: Annual Hurricane Occurrencementioning

confidence: 99%