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Christopher W. Landsea

Abstract

The variability of intense (or major) hurricanes in the Atlantic basin is investigated on both intraseasonal and interannual time scales. Differences are highlighted in characteristics between intense hurricanes and the weaker minor hurricanes and tropical storms. Intense hurricanes show a much more peaked annual cycle than do weaker tropical cyclones. Ninety-five percent of all intense hurricane activity occurs during August to October. In addition, over 80% of all intense hurricanes originate from African easterly waves, a much higher proportion than is observed for weaker cyclones. Of all classes of Atlantic basin tropical cyclones, the intense hurricanes display the greatest year-to-year variability. The incidence of intense hurricanes also has decreased during the last two decades. A small portion of this decreased activity appears to be due to an overestimation of hurricane intensity during the period spanning the 1940s through the 1960s. After adjusting for this bias, however, a substantial downward trend in intense hurricane activity during recent years is still apparent. Given that intense hurricanes are responsible for more than 70% of all destruction caused by tropical cyclones in the United States, an understanding is needed of the physical mechanisms for these observed variations of intense hurricane activity.

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John P. Cangialosi and Christopher W. Landsea

Abstract

While the National Hurricane Center (NHC) has been issuing analyses and forecasts of tropical cyclone wind radii for several years, little documentation has been provided about the errors in these forecasts. A key hurdle in providing routine verification of these forecasts is that the uncertainty in the wind radii best tracks is quite large for tropical cyclones that are well away from land and unmonitored by aircraft reconnaissance. This study evaluates the errors of a subset of NHC and model 34-, 50-, and 64-kt (1 kt = 0.514 m s−1) wind radii forecasts from 2008 through 2012 that had aircraft reconnaissance available at both the initial and verification times. The results show that the NHC wind radii average errors increased with forecast time but were skillful when compared against climatology and persistence. The dynamical models, however, were not skillful and had errors that were much larger than the NHC forecasts, with substantial negative (too small) biases even after accounting for their initial size differences versus the tropical cyclone’s current wind radii. Improvements in wind radii forecasting will come about through a combination of better methods for observing tropical cyclone size as well as enhanced prediction techniques (dynamical models, statistical methods, and consensus approaches).

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John A. Knaff and Christopher W. Landsea

Abstract

A statistical prediction method, which is based entirely on the optimal combination of persistence, month-to-month trend of initial conditions, and climatology, is developed for the El Niño–Southern Oscillation (ENSO) phenomena. The selection of predictors is by design intended to avoid any pretense of predictive ability based on “model physics” and the like, but rather is to specify the optimal “no-skill” forecast as a baseline comparison for more sophisticated forecast methods. Multiple least squares regression using the method of leaps and bounds is employed to test a total of 14 possible predictors for the selection of the best predictors, based upon 1950–94 developmental data. A range of zero to four predictors were chosen in developing 12 separate regression models, developed separately for each initial calendar month. The predictands to be forecast include the Southern Oscillation (pressure) index (SOI) and the Niño 1+2, Niño 3, Niño 4, and Niño 3.4 SST indices for the equatorial eastern and central Pacific at lead times ranging from zero seasons (0–2 months) through seven seasons (21–23 months). Though hindcast ability is strongly seasonally dependent, substantial improvement is achieved over simple persistence wherein largest gains occur for two–seven-season (6–23 months) lead times. For example, expected maximum forecast ability for the Niño 3.4 SST region, depending on the initial date, reaches 92%, 85%, 64%, 41%, 36%, 24%, 24%, and 28% of variance for leads of zero to seven seasons. Comparable maxima of persistence only forecasts explain 92%, 77%, 50%, 17%, 6%, 14%, 21%, and 17%, respectively. More sophisticated statistical and dynamic forecasting models are encouraged to utilize this ENSO-CLIPER model in place of persistence when assessing whether they have achieved forecasting skill; to this end, real-time results for this model are made available via a Web site.

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Christopher W. Landsea and John P. Cangialosi

Abstract

The tropical cyclone is the largest single-day-impact meteorological event in the United States and worldwide through its effects from storm surge, extreme winds, freshwater flooding, and embedded tornadoes. Fortunately, over the last three decades there have been incredible advances in forecast accuracy, especially for the track of the tropical cyclone’s center. Errors have been cut by two-thirds in just 25 years due to global modeling advances, data assimilation improvements, dramatic increases in observations primarily derived from satellite platforms, and use of ensemble forecast techniques. These four factors have allowed for highly accurate synoptic-scale atmospheric initial conditions and forecasts of the steering flow out through several days into the future. However, such improvements cannot continue indefinitely. It is well known in the atmospheric sciences that there exists an inherent “limit of predictability” because of errors at the smallest scales (microscale—meters and seconds) that eventually cascade up to the largest scales (synoptic scale—thousands of kilometers and several days). While there have been estimates of the limits of predictability for tropical cyclone track prediction in the past, our current capabilities have exceeded those somewhat pessimistic earlier outlooks. This essay discusses the current state of the art for tropical cyclone track prediction and reassesses whether reaching the “limit of predictability” is imminent. The ramifications of this eventual conclusion—whether in the short-term or still decades away—could be critical for all users of tropical cyclone track forecast information, including the emergency management community/governments, the media, the private sector, and the general public.

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Christopher W. Landsea and James L. Franklin

Abstract

“Best tracks” are National Hurricane Center (NHC) poststorm analyses of the intensity, central pressure, position, and size of Atlantic and eastern North Pacific basin tropical and subtropical cyclones. This paper estimates the uncertainty (average error) for Atlantic basin best track parameters through a survey of the NHC Hurricane Specialists who maintain and update the Atlantic hurricane database. A comparison is then made with a survey conducted over a decade ago to qualitatively assess changes in the uncertainties. Finally, the implications of the uncertainty estimates for NHC analysis and forecast products as well as for the prediction goals of the Hurricane Forecast Improvement Program are discussed.

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Christopher W. Landsea and John A. Knaff

The very strong 1997–98 El Niño was the first major event in which numerous forecasting groups participated in its real-time prediction. A previously developed simple statistical tool—the El Niño–Southern Oscillation Climatology and Persistence (ENSO–CLIPER) model—is utilized as a baseline for determination of skill in forecasting this event. Twelve statistical and dynamical models were available in real time for evaluation. Some of the models were able to outperform ENSO–CLIPER in predicting either the onset or the decay of the 1997–98 El Niño, but none were successful at both for a medium-range two season (6–8 months) lead time. There were no models, including ENSO–CLIPER, able to anticipate even one-half of the actual amplitude of the El Niño's peak at medium-range (6–11 months) lead. In addition, none of the models showed skill (i.e., lower root-mean-square error than ENSO–CLIPER) at the zero season (0–2 months) through the two season (6–8 months) lead times. No dynamical model and only two of the statistical models [the canonical correlation analysis (CCA) and the constructed analog (ANALOG)] outperformed ENSO–CLIPER by more than 5% of the root-mean-square error at the three season (9–11 months) and four season (12–14 months) lead time. El Niño impacts were correctly anticipated by national meteorological centers one half-year in advance, because of the tendency for El Niño events to persist into and peak during the boreal winter. Despite this, the zero to two season (0–8 month) forecasts of the El Niño event itself were no better than ENSO–CLIPER and were, in that sense, not skillful—a conclusion that remains unclear to the general meteorological and oceanographic communities.

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William M. Gray and Christopher W. Landsea

This paper describes a predictive relationship between West African rainfall and U.S. hurricane-spawned destruction, which is based on information for the 42-yr period 1949–90. It is shown that above-average rainfall during the previous year along the Gulf of Guinea, in combination with above-average rainfall in the western Sahel during June and July, is linked to hurricane-spawned destruction along the U.S. East Coast occurring after 1 August, which is 10–20 times greater than in years when pre-1 August precipitation for these West African regions is below average. Similar hurricane-spawned damage along the U.S. Gulf Coast shows only a negligible relationship with African rainfall. Hurricane-caused deaths for both U.S. coastal regions also show a similar association with West African rainfall.

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Brian F. Owens and Christopher W. Landsea

Abstract

Since 1984, W. Gray of Colorado State University and a team of researchers have been issuing seasonal tropical cyclone forecasts for the North Atlantic Ocean. Prior to this, little work had been done in the area of long-term tropical cyclone forecasting because researchers saw minimal potential skill in any prediction models and no obvious benefits to be gained. However, seasonal forecasts have been attracting more attention as economic and insured losses from hurricane-related catastrophes rose sharply during recent decades. Initially, the forecasts issued by Gray consisted of output from simple statistical prediction models. Over time, the models became increasingly more complex and sophisticated, with new versions being introduced in 1992, 1993, 1994, 1996, and 1997. In addition, based on a combination of experience with the statistical models and other qualitative considerations such as examinations of analog years, the statistical forecasts were modified to create adjusted seasonal forecasts. This analysis assessed the skill demonstrated, if any, of both the statistical and adjusted forecasts over the benchmarks of climatology and persistence and examined whether the adjusted forecasts were more accurate than the statistical forecasts. The analysis indicates that, over the past 18 yr, both the statistical and adjusted forecasts demonstrated some skill over climatology and persistence. There is also evidence to suggest that the adjusted forecast was more skillful than the statistical model forecast.

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Christopher W. Landsea and William M. Gray

Abstract

Seasonal variability of Atlantic basin tropical cyclones is examined with respect to the monsoon rainfall over West Africa. Variations of intense hurricanes are of the most interest, as they are responsible for over three-quarters of United States tropical cyclone spawned destruction, though they account for only one-fifth of all landfalling cyclones. Intense hurricanes have also shown a strong downward trend during the last few decades. It is these storms that show the largest concurrent association with Africa's western Sahelian June-September rainfall for the years 1949–90.

Though the Sahel is currently experiencing a multidecadal drought, the relationship between Atlantic tropical cyclones and western Sahelian rainfall is not dependent on the similar downward trends in both datasets. A detrended analysis confirms that a strong association still exists, though reduced somewhat in variance explained. Additionally, independent data from the years 1899 to 1948 substantiate the existence of the tropical cyclone-western Sabelian rainfall association.

The fact that the Sahel periodically experiences multidecadal wet and dry regimes suggests that the current Sahelian drought, which began in the late 1960s, could be a temporary condition that may end in the new future. When this occurs, the Atlantic hurricane basin—especially the Caribbean islands and the United States East Coast—will likely see a large increase in intense hurricane activity associated with abundant Sahelian rainfall similar to the period of the late 1940s through the 1960s.

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Philip J. Klotzbach and Christopher W. Landsea

Abstract

Ten years ago, Webster et al. documented a large and significant increase in both the number as well as the percentage of category 4 and 5 hurricanes for all global basins from 1970 to 2004, and this manuscript examines whether those trends have continued when including 10 additional years of data. In contrast to that study, as shown here, the global frequency of category 4 and 5 hurricanes has shown a small, insignificant downward trend while the percentage of category 4 and 5 hurricanes has shown a small, insignificant upward trend between 1990 and 2014. Accumulated cyclone energy globally has experienced a large and significant downward trend during the same period. The primary reason for the increase in category 4 and 5 hurricanes noted in observational datasets from 1970 to 2004 by Webster et al. is concluded to be due to observational improvements at the various global tropical cyclone warning centers, primarily in the first two decades of that study.

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