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Stephanie C. Herring, Andrew Hoell, Martin P. Hoerling, James P. Kossin, Carl J. Schreck III, and Peter A. Stott
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Stephanie C. Herring, Andrew Hoell, Martin P. Hoerling, James P. Kossin, Carl J. Schreck III, and Peter A. Stott

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2015 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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Wayne H. Schubert, Michael T. Montgomery, Richard K. Taft, Thomas A. Guinn, Scott R. Fulton, James P. Kossin, and James P. Edwards

Abstract

Hurricane eyewalls are often observed to be nearly circular structures, but they are occasionally observed to take on distinctly polygonal shapes. The shapes range from triangles to hexagons and, while they are often incomplete, straight line segments can be identified. Other observations implicate the existence of intense mesovortices within or near the eye region. Is there a relation between polygonal eyewalls and hurricane mesovortices? Are these phenomena just curiosities of the hurricane’s inner-core circulation, or are they snapshots of an intrinsic mixing process within or near the eye that serves to determine the circulation and thermal structure of the eye?

As a first step toward understanding the asymmetric vorticity dynamics of the hurricane’s eye and eyewall region, these issues are examined within the framework of an unforced barotropic nondivergent model. Polygonal eyewalls are shown to form as a result of barotropic instability near the radius of maximum winds. After reviewing linear theory, simulations with a high-resolution pseudospectral numerical model are presented to follow the instabilities into their nonlinear regime. When the instabilities grow to finite amplitude, the vorticity of the eyewall region pools into discrete areas, creating the appearance of polygonal eyewalls. The circulations associated with these pools of vorticity suggest a connection to hurricane mesovortices. At later times the vorticity is ultimately rearranged into a nearly monopolar circular vortex. While the evolution of the finescale vorticity field is sensitive to the initial condition, the macroscopic end-states are found to be similar. In fact, the gross characteristics of the numerically simulated end-states are predicted analytically using a generalization of the minimum enstrophy hypothesis. In an effort to remove some of the weaknesses of the minimum enstrophy approach, a maximum entropy argument developed previously for rectilinear shear flows is extended to the vortex problem, and end-state solutions in the limiting case of tertiary mixing are obtained.

Implications of these ideas for real hurricanes are discussed.

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Stephanie C. Herring, Nikolaos Christidis, Andrew Hoell, James P. Kossin, Carl J. Schreck III, and Peter A. Stott

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2016 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.

Open access
Peter A. StotT, Nikos Christidis, Stephanie C. Herring, Andrew Hoell, James P. Kossin, and Carl J. Schreck III
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Stephanie C. Herring, Nikolaos Christidis, Andrew Hoell, James P. Kossin, Carl J. Schreck III, and Peter A. Stott
Open access
Christopher D. Bosma, Daniel B. Wright, Phu Nguyen, James P. Kossin, Derrick C. Herndon, and J. Marshall Shepherd

Abstract

Recent tropical cyclones (TCs) have highlighted the hazards that TC rainfall poses to human life and property. These hazards are not adequately conveyed by the commonly used Saffir–Simpson scale. Additionally, while recurrence intervals (or, their inverse, annual exceedance probabilities) are sometimes used in the popular media to convey the magnitude and likelihood of extreme rainfall and floods, these concepts are often misunderstood by the public and have important statistical limitations. We introduce an alternative metric—the extreme rain multiplier (ERM), which expresses TC rainfall as a multiple of the climatologically derived 2-yr rainfall value. ERM allows individuals to connect (“anchor,” in cognitive psychology terms) the magnitude of a TC rainfall event to the magnitude of rain events that are more typically experienced in their area. A retrospective analysis of ERM values for TCs from 1948 to 2017 demonstrates the utility of the metric as a hazard quantification and communication tool. Hurricane Harvey (2017) had the highest ERM value during this period, underlining the storm’s extreme nature. ERM correctly identifies damaging historical TC rainfall events that would have been classified as “weak” using wind-based metrics. The analysis also reveals that the distribution of ERM maxima is similar throughout the eastern and southern United States, allowing for both the accurate identification of locally extreme rainfall events and the development of regional-scale (rather than local-scale) recurrence interval estimates for extreme TC rainfall. Last, an analysis of precipitation forecast data for Hurricane Florence (2018) demonstrates ERM’s ability to characterize Florence’s extreme rainfall hazard in the days preceding landfall.

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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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Kenneth R. Knapp, Steve Ansari, Caroline L. Bain, Mark A. Bourassa, Michael J. Dickinson, Chris Funk, Chip N. Helms, Christopher C. Hennon, Christopher D. Holmes, George J. Huffman, James P. Kossin, Hai-Tien Lee, Alexander Loew, and Gudrun Magnusdottir

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them that no central archive of geostationary data for all international satellites exists, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multisatellite climate studies. The International Satellite Cloud Climatology Project (ISCCP) set the stage for overcoming these issues by archiving a subset of the full-resolution geostationary data at ~10-km resolution at 3-hourly intervals since 1983. Recent efforts at NOAA's National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in Network Common Data Format (netCDF) using standards that permit a wide variety of tools and libraries to process the data quickly and easily. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone.

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