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Christopher Velden, Timothy Olander, Derrick Herndon, and James P. Kossin

Abstract

In recent years, a number of extremely powerful tropical cyclones have revived community debate on methodologies used to estimate the lifetime maximum intensity (LMI) of these events. And how do these storms rank historically? In this study, the most updated version of an objective satellite-based intensity estimation algorithm [advanced Dvorak technique (ADT)] is employed and applied to the highest-resolution (spatial and temporal) geostationary satellite data available for extreme-intensity tropical cyclones that occurred during the era of these satellites (1979–present). Cases with reconnaissance aircraft observations are examined and used to calibrate the ADT at extreme intensities. Bias corrections for observing properties such as satellite viewing angle and image spatiotemporal resolution, and storm characteristics such as small eye size are also considered.

The results of these intensity estimates (maximum sustained 1-min wind) show that eastern North Pacific Hurricane Patricia (2015) ranks as the strongest storm in any basin (182 kt), followed by western North Pacific Typhoons Haiyan (2013), Tip (1979), and Gay (1992). The following are the strongest classifications in other basins—Atlantic: Gilbert (1988), north Indian Ocean basin: Paradip (1999), south Indian Ocean: Gafilo (2004), Australian region: Monica (2006), and southeast Pacific basin: Pam (2015). In addition, ADT LMI estimates for four storms exceed the maximum allowable limit imposed by the operational Dvorak technique. This upper bound on intensity may be an unnatural constraint, especially if tropical cyclones get stronger in a warmer biosphere as some theorize. This argues for the need of an extension to the Dvorak scale to allow higher intensity estimates.

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James P. Kossin, Brian D. McNoldy, and Wayne H. Schubert

Abstract

A collection of images depicting various swirling patterns within low-level cloud decks in hurricane eyes is presented and described. A possible causal mechanism for the presence of these cloud patterns is suggested by comparison of the observed cloud patterns with the evolution of passive tracers in a simple 2D barotropic model. The model is initialized with a barotropically unstable flow field that imitates the observed flows in hurricanes, and numerical integration of this field simulates vigorous mixing between eye and eyewall. During the mixing process, passive tracers initially embedded in the flow form swirling patterns in the eye that are strikingly similar to cloud patterns often observed in the eyes of hurricanes.

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James P. Kossin, Wayne H. Schubert, and Michael T. Montgomery

Abstract

Intense tropical cyclones often exhibit concentric eyewall patterns in their radar reflectivity. Deep convection within the inner, or primary, eyewall is surrounded by a nearly echo-free moat, which in turn is surrounded by an outer, or secondary ring of deep convection. Both convective regions typically contain well-defined tangential wind maxima. The primary wind maximum is associated with large vorticity just inside the radius of maximum wind, while the secondary wind maximum is usually associated with relatively enhanced vorticity embedded in the outer ring. In contrast, the moat is a region of low vorticity. If the vorticity profile across the eye and inner eyewall is approximated as monotonic, the resulting radial profile of vorticity still satisfies the Rayleigh necessary condition for instability as the radial gradient twice changes sign.

Here the authors investigate the stability of such structures and, in the case of instability, simulate the nonlinear evolution into a more stable structure using a nondivergent barotropic model. Because the radial gradient of vorticity changes sign twice, two types of instability and vorticity rearrangement are identified: 1) instability across the outer ring of enhanced vorticity, and 2) instability across the moat. Type 1 instability occurs when the outer ring of enhanced vorticity is sufficiently narrow and when the circulation of the central vortex is sufficiently weak (compared to the outer ring) that it does not induce enough differential rotation across the outer ring to stabilize it. The nonlinear mixing associated with type 1 instability results in a broader and weaker vorticity ring but still maintains a significant secondary wind maximum. The central vortex induces strong differential rotation (and associated enstrophy cascade) in the moat region, which then acts as a barrier to inward mixing of small (but finite) amplitude asymmetric vorticity disturbances. Type 2 instability occurs when the radial extent of the moat is sufficiently narrow so that unstable interactions may occur between the central vortex and the inner edge of the ring. Because the vortex-induced differential rotation across the ring is large when the ring is close to the vortex, type 2 instability typically precludes type 1 instability except in the case of very thin rings. The nonlinear mixing from type 2 instability perturbs the vortex into a variety of shapes. In the case of contracting rings of enhanced vorticity, the vortex and moat typically evolve into a nearly steady tripole structure, thereby offering a mechanism for the formation and persistence of elliptical eyewalls.

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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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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2014 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott
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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott
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Eric A. Hendricks, Wayne H. Schubert, Richard K. Taft, Huiqun Wang, and James P. Kossin

Abstract

The asymmetric dynamics of potential vorticity mixing in the hurricane inner core are further advanced by examining the end states that result from the unforced evolution of hurricane-like vorticity rings in a nondivergent barotropic model. The results from a sequence of 170 numerical simulations are summarized. The sequence covers a two-dimensional parameter space, with the first parameter defining the hollowness of the vortex (i.e., the ratio of eye to inner-core relative vorticity) and the second parameter defining the thickness of the ring (i.e., the ratio of the inner and outer radii of the ring). In approximately one-half of the cases, the ring becomes barotropically unstable, and there ensues a vigorous vorticity mixing episode between the eye and eyewall. The output of the barotropic model is used to (i) verify that the nonlinear model approximately replicates the linear theory of the fastest-growing azimuthal mode in the early phase of the evolution, and (ii) characterize the end states (defined at t = 48 h) that result from the nonlinear chaotic vorticity advection and mixing. It is found that the linear stability theory is a good guide to the fastest-growing exponential mode in the numerical model. Two additional features are observed in the numerical model results. The first is an azimuthal wavenumber-2 deformation of the vorticity ring that occurs for moderately thick, nearly filled rings. The second is an algebraically growing wavenumber-1 instability (not present in the linear theory because of the assumed solution) that is observed as a wobbling eye (or the trochoidal oscillation for a moving vortex) for thick rings that are stable to all exponentially growing instabilities. Most end states are found to be monopoles. For very hollow and thin rings, persistent mesovortices may exist for more than 15 h before merging to a monopole. For thicker rings, the relaxation to a monopole takes longer (between 48 and 72 h). For moderately thick rings with nearly filled cores, the most likely end state is an elliptical eyewall. In this nondivergent barotropic context, both the minimum central pressure and maximum tangential velocity simultaneously decrease over 48 h during all vorticity mixing events.

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Kenneth R. Knapp, Jessica L. Matthews, James P. Kossin, and Christopher C. Hennon

Abstract

The Cyclone Center project maintains a website that allows visitors to answer questions based on tropical cyclone satellite imagery. The goal is to provide a reanalysis of satellite-derived tropical cyclone characteristics from a homogeneous historical database composed of satellite imagery with a common spatial resolution for use in long-term, global analyses. The determination of the cyclone “type” (curved band, eye, shear, etc.) is a starting point for this process. This analysis shows how multiple classifications of a single image are combined to provide probabilities of a particular image’s type using an expectation–maximization (EM) algorithm. Analysis suggests that the project needs about 10 classifications of an image to adequately determine the storm type. The algorithm is capable of characterizing classifiers with varying levels of expertise, though the project needs about 200 classifications to quantify an individual’s precision. The EM classifications are compared with an objective algorithm, satellite fix data, and the classifications of a known classifier. The EM classifications compare well, with best agreement for eye and embedded center storm types and less agreement for shear and when convection is too weak (termed no-storm images). Both the EM algorithm and the known classifier showed similar tendencies when compared against an objective algorithm. The EM algorithm also fared well when compared to tropical cyclone fix datasets, having higher agreement with embedded centers and less agreement for eye images. The results were used to show the distribution of storm types versus wind speed during a storm’s lifetime.

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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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