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James P. Kossin

Abstract

The relationship between minimum central surface pressure and the maximum sustained surface wind in tropical cyclones has been studied for many years, motivated by the fact that minimum pressure is generally easier to measure, but maximum wind is a much more relevant metric when considering tropical cyclone risk and potential impacts. It is well understood that tropical cyclone wind is closely related to the radial gradient of pressure through gradient or cyclostrophic balance assumptions, and not to a single point value of the minimum pressure near the storm center. But it is often the case that the maximum wind must be inferred from this single value. To accomplish this, a number of statistical relationships have been documented, such as those used in the Dvorak technique for estimating tropical cyclone intensity from satellite imagery. Here, the relationship between tropical cyclone maximum wind and minimum pressure is explored during eyewall replacement cycles (ERCs) that have been observed in North Atlantic hurricanes. It is shown that the wind–pressure relationship (WPR) can vary substantially during an ERC and generally moves away from the statistically fitted WPR used by the Dvorak technique in that basin. The changes in WPR during an ERC can be quite different depending on the intensity of the hurricane at the start of the ERC.

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James P. Kossin and Matthew Sitkowski

Abstract

Eyewall replacement cycles are commonly observed in tropical cyclones and are well known to cause fluctuations in intensity and wind structure. These fluctuations are often large and rapid and pose a significant additional challenge to intensity forecasting, yet there is presently no objective operational guidance available to forecasters that targets, quantifies, and predicts these fluctuations. Here the authors introduce new statistical models that are based on a recently documented climatology of intensity and structure changes associated with eyewall replacement cycles in Atlantic Ocean hurricanes. The model input comprises environmental features and satellite-derived features that contain information on storm cloud structure. The models predict the amplitude and timing of the intensity fluctuations, as well as the fluctuations of the wind structure, and can provide real-time operational objective guidance to forecasters.

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James P. Kossin and Mark DeMaria

Abstract

Eyewall replacement cycles (ERCs) are fairly common events in tropical cyclones (TCs) of hurricane intensity or greater and typically cause large and sometimes rapid changes in the intensity evolution of the TC. Although the details of the intensity evolution associated with ERCs appear to have some dependence on the ambient environmental conditions that the TCs move through, these dependencies can also be quite different than those of TCs that are not undergoing an ERC. For example, the Statistical Hurricane Prediction Scheme (SHIPS), which is used in National Hurricane Center operations and provides intensity forecast skill that is, on average, equal to or greater than deterministic numerical model skill, typically identifies an environment that is not indicative of weakening during the onset and subsequent evolution of an ERC. Contrarily, a period of substantial weakening does typically begin near the onset of an ERC, and this disparity can cause large SHIPS intensity forecast errors. Here, a simple model based on a climatology of ERC intensity change is introduced and tested against SHIPS. It is found that the application of the model can reduce intensity forecast error substantially when applied at, or shortly after, the onset of ERC weakening.

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Christopher M. Rozoff and James P. Kossin

Abstract

The National Hurricane Center currently employs a skillful probabilistic rapid intensification index (RII) based on linear discriminant analysis of the environmental and satellite-derived features from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset. Probabilistic prediction of rapid intensity change in tropical cyclones is revisited here using two additional models: one based on logistic regression and the other on a naïve Bayesian framework. Each model incorporates data from the SHIPS dataset over both the North Atlantic and eastern North Pacific Ocean basins to provide the probability of exceeding the standard rapid intensification thresholds [25, 30, and 35 kt (24 h)−1] for 24 h into the future. The optimal SHIPS and satellite-based predictors of rapid intensification differ slightly between each probabilistic model and ocean basin, but each set of optimal predictors incorporates thermodynamic and dynamic aspects of the tropical cyclone’s environment (such as vertical wind shear) and its structure (such as departure from convective axisymmetry). Cross validation shows that both the logistic regression and Bayesian probabilistic models are skillful relative to climatology. Dependent testing indicates both models exhibit forecast skill that generally exceeds the skill of the present operational SHIPS-RII and a simple average of the probabilities provided by the logistic regression, Bayesian, and SHIPS-RII models provides greater skill than any individual model. For the rapid intensification threshold of 25 kt (24 h)−1, the three-member ensemble mean improves the Brier skill scores of the current operational SHIPS-RII by 33% in the North Atlantic and 52% in the eastern North Pacific.

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John A. Knaff, James P. Kossin, and Mark DeMaria

Abstract

This study introduces and examines a symmetric category of tropical cyclone, which the authors call annular hurricanes. The structural characteristics and formation of this type of hurricane are examined and documented using satellite and aircraft reconnaissance data. The formation is shown to be systematic, resulting from what appears to be asymmetric mixing of eye and eyewall components of the storms involving either one or two possible mesovortices. Flight-level thermodynamic data support this contention, displaying uniform values of equivalent potential temperature in the eye, while the flight-level wind observations within annular hurricanes show evidence that mixing inside the radius of maximum wind likely continues. Intensity tendencies of annular hurricanes indicate that these storms maintain their intensities longer than the average hurricane, resulting in larger-than-average intensity forecast errors and thus a significant intensity forecasting challenge. In addition, these storms are found to exist in a specific set of environmental conditions, which are only found 3% and 0.8% of the time in the east Pacific and Atlantic tropical cyclone basins during 1989–99, respectively. With forecasting issues in mind, two methods of objectively identifying these storms are also developed and discussed.

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John A. Knaff, Thomas A. Cram, Andrea B. Schumacher, James P. Kossin, and Mark DeMaria

Abstract

Annular hurricanes are a subset of intense tropical cyclones that have been shown in previous work to be significantly stronger, to maintain their peak intensities longer, and to weaken more slowly than average tropical cyclones. Because of these characteristics, they represent a significant forecasting challenge. This paper updates the list of annular hurricanes to encompass the years 1995–2006 in both the North Atlantic and eastern–central North Pacific tropical cyclone basins. Because annular hurricanes have a unique appearance in infrared satellite imagery, and form in a specific set of environmental conditions, an objective real-time method of identifying these hurricanes is developed. However, since the occurrence of annular hurricanes is rare (∼4% of all hurricanes), a special algorithm to detect annular hurricanes is developed that employs two steps to identify the candidates: 1) prescreening the data and 2) applying a linear discriminant analysis. This algorithm is trained using a dependent dataset (1995–2003) that includes 11 annular hurricanes. The resulting algorithm is then independently tested using datasets from the years 2004–06, which contained an additional three annular hurricanes. Results indicate that the algorithm is able to discriminate annular hurricanes from tropical cyclones with intensities greater than 84 kt (43.2 m s−1). The probability of detection or hit rate produced by this scheme is shown to be ∼96% with a false alarm rate of ∼6%, based on 1363 six-hour time periods with a tropical cyclone with an intensity greater than 84 kt (1995–2006).

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Christopher M. Rozoff, Christopher S. Velden, John Kaplan, James P. Kossin, and Anthony J. Wimmers

Abstract

The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004–13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of “perfect” observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive.

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Kimberly J. Mueller, Mark DeMaria, John Knaff, James P. Kossin, and Thomas H. Vonder Haar

Abstract

Geostationary infrared (IR) satellite data are used to provide estimates of the symmetric and total low-level wind fields in tropical cyclones, constructed from estimations of an azimuthally averaged radius of maximum wind (RMAX), a symmetric tangential wind speed at a radius of 182 km (V182), a storm motion vector, and the maximum intensity (VMAX). The algorithm is derived using geostationary IR data from 405 cases from 87 tropical systems in the Atlantic and east Pacific Ocean basins during the 1995–2003 hurricane seasons that had corresponding aircraft data available. The algorithm is tested on 50 cases from seven tropical storms and hurricanes during the 2004 season. Aircraft-reconnaissance-measured RMAX and V182 are used as dependent variables in a multiple linear regression technique, and VMAX and the storm motion vector are estimated using conventional methods. Estimates of RMAX and V182 exhibit mean absolute errors (MAEs) of 27.3 km and 6.5 kt, respectively, for the dependent samples. A modified combined Rankine vortex model is used to estimate the one-dimensional symmetric tangential wind field from VMAX, RMAX, and V182. Next, the storm motion vector is added to the symmetric wind to produce estimates of the total wind field. The MAE of the IR total wind retrievals is 10.4 kt, and the variance explained is 53%, when compared with the two-dimensional wind fields from the aircraft data for the independent cases.

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James P. Kossin, John A. Knaff, Howard I. Berger, Derrick C. Herndon, Thomas A. Cram, Christopher S. Velden, Richard J. Murnane, and Jeffrey D. Hawkins

Abstract

New objective methods are introduced that use readily available data to estimate various aspects of the two-dimensional surface wind field structure in hurricanes. The methods correlate a variety of wind field metrics to combinations of storm intensity, storm position, storm age, and information derived from geostationary satellite infrared (IR) imagery. The first method estimates the radius of maximum wind (RMW) in special cases when a clear symmetric eye is identified in the IR imagery. The second method estimates RMW, and the additional critical wind radii of 34-, 50-, and 64-kt winds for the general case with no IR scene–type constraint. The third method estimates the entire two-dimensional surface wind field inside a storm-centered disk with a radius of 182 km. For each method, it is shown that the inclusion of infrared satellite data measurably reduces error. All of the methods can be transitioned to an operational setting or can be used as a postanalysis tool.

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John Kaplan, Christopher M. Rozoff, Mark DeMaria, Charles R. Sampson, James P. Kossin, Christopher S. Velden, Joseph J. Cione, Jason P. Dunion, John A. Knaff, Jun A. Zhang, John F. Dostalek, Jeffrey D. Hawkins, Thomas F. Lee, and Jeremy E. Solbrig

Abstract

New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.

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