scholarly journals Comments on “Structure and Evolution of a Possible U.S. Landfalling Tropical Storm in 2006”

2010 ◽  
Vol 138 (1) ◽  
pp. 279-281 ◽  
Author(s):  
John L. Beven ◽  
Lixion A. Avila ◽  
Eric S. Blake ◽  
Hugh D. Cobb ◽  
Richard J. Pasch
Keyword(s):  
Tropical Cyclone ◽  
Companion Paper ◽  
Closed Surface ◽  
Conclusive Evidence ◽  
Eastern United States ◽  
Kinematic Parameters ◽  
System A ◽  
Surface Circulation ◽  
Surface Data ◽  
National Hurricane Center

Abstract The Best Track Change Committee of the National Hurricane Center evaluates proposed changes to the Hurricane Database (HURDAT) in the Atlantic and eastern North Pacific basins. In the companion paper, Gruskin documents a possible tropical cyclone that affected portions of the eastern United States on 27–28 June 2006 and proposes that it be added to HURDAT. The committee reviewed the aircraft, radar, rawinsonde, satellite, and surface data available on this system and found it to be a challenging and complex system. A reconnaissance aircraft flying in the system in real time failed to find a closed circulation before landfall, and kinematic parameters suggest the system was more likely to have the structure of an open wave, with any surface circulation at best being poorly defined. Because of the lack of conclusive evidence regarding the existence of a closed surface circulation before landfall, the committee has decided not to add this system to HURDAT as a tropical cyclone.

scholarly journals Operational Forecasting of Tropical Cyclone Rapid Intensification at the National Hurricane Center

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 683
Author(s):  
Mark DeMaria ◽  
James L. Franklin ◽  
Matthew J. Onderlinde ◽  
John Kaplan
Keyword(s):  
Tropical Cyclone ◽  
Absolute Error ◽  
Baseline Period ◽  
Probability Of Detection ◽  
Rapid Intensification ◽  
Improvement Program ◽  
Deterministic Models ◽  
Operational Forecasting ◽  
National Hurricane Center ◽  
Made In

Although some recent progress has been made in operational tropical cyclone (TC) intensity forecasting, the prediction of rapid intensification (RI) remains a challenging problem. To document RI forecast progress, deterministic and probabilistic operational intensity models used by the National Hurricane Center (NHC) are briefly reviewed. Results show that none of the deterministic models had RI utility from 1991 to about 2015 due to very low probability of detection, very high false alarm ratio, or both. Some ability to forecast RI has emerged since 2015, with dynamical models being the best guidance for the Atlantic and statistical models the best RI guidance for the eastern North Pacific. The first probabilistic RI guidance became available in 2001, with several upgrades since then leading to modest skill in recent years. A tool introduced in 2018 (DTOPS) is currently the most skillful among NHC’s probabilistic RI guidance. To measure programmatic progress in forecasting RI, the Hurricane Forecast Improvement Program has introduced a new RI metric that uses the traditional mean absolute error but restricts the sample to only those cases where RI occurred in the verifying best track or was forecast. By this metric, RI forecasts have improved by ~20–25% since the 2015–2017 baseline period.

Statistical Tropical Cyclone Wind Radii Prediction Using Climatology and Persistence

2007 ◽  
Vol 22 (4) ◽  
pp. 781-791 ◽  
Author(s):  
John A. Knaff ◽  
Charles R. Sampson ◽  
Mark DeMaria ◽  
Timothy P. Marchok ◽  
James M. Gross ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Department Of Defense ◽  
Parametric Model ◽  
Forecast Method ◽  
Operational Model ◽  
Wind Structure ◽  
East Pacific ◽  
Operational Forecasts ◽  
National Hurricane Center ◽  
Joint Typhoon Warning Center

Abstract An operational model used to predict tropical cyclone wind structure in terms of significant wind radii (i.e., 34-, 50-, and 64-kt wind radii, where 1 kt = 0.52 m s−1) at the National Oceanic and Atmospheric Administration/National Hurricane Center (NHC) and the Department of Defense/Joint Typhoon Warning Center (JTWC) is described. The statistical-parametric model employs aspects of climatology and persistence to forecast tropical cyclone wind radii through 5 days. Separate versions of the model are created for the Atlantic, east Pacific, and western North Pacific by statistically fitting a modified Rankine vortex, which is generalized to allow wavenumber-1 asymmetries, to observed values of tropical cyclone wind radii as reported by NHC and JTWC. Descriptions of the developmental data and methods used to formulate the model are given. A 2-yr verification and comparison with operational forecasts and an independently developed wind radii forecast method that also employs climatology and persistence suggests that the statistical-parametric model does a good job of forecasting wind radii. The statistical-parametric model also provides reliable operational forecasts that serve as a baseline for evaluating the skill of operational forecasts and other wind radii forecast methods in these tropical cyclone basins.

End-user Preference for and Understanding of Hurricane Forecast Graphs

2021 ◽  
Vol 65 (1) ◽  
pp. 606-610
Author(s):  
Barbara Millet ◽  
Sharanya J. Majumdar ◽  
Alberto Cairo ◽  
Carolina Diaz ◽  
Qinyu Ding ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Risk Communication ◽  
Prior Experience ◽  
User Preference ◽  
Laboratory Setting ◽  
End User ◽  
Temporal Uncertainty ◽  
Spatio Temporal ◽  
The Impact ◽  
National Hurricane Center

Hurricane forecast graphics have the challenging task of communicating information about spatio-temporal uncertainty. This study assesses the impact of graph literacy and graph format on user preference and understanding. In a laboratory setting, we compared user responses to official National Hurricane Center advisory maps and alternative visualizations. Results indicate that prior experience with a visualization drives preference and that graph literacy, visualization format, and tropical cyclone characteristics, in combination, influence interpretations of hurricane forecast track. The findings from this study are expected to inform redesign efforts of hurricane risk communication products.

scholarly journals An Examination of Model and Official National Hurricane Center Tropical Cyclone Size Forecasts

2016 ◽  
Vol 31 (4) ◽  
pp. 1293-1300 ◽  
Author(s):  
John P. Cangialosi ◽  
Christopher W. Landsea
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Statistical Methods ◽  
Dynamical Models ◽  
Initial Size ◽  
Prediction Techniques ◽  
Forecast Time ◽  
National Hurricane Center

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).

scholarly journals Prediction of Consensus Tropical Cyclone Track Forecast Error

2007 ◽  
Vol 135 (5) ◽  
pp. 1985-1993 ◽  
Author(s):  
James S. Goerss
Keyword(s):  
Tropical Cyclone ◽  
Regression Models ◽  
Average Distance ◽  
Forecast Error ◽  
Dependent Data ◽  
Track Forecast ◽  
Consensus Model ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
National Hurricane Center

Abstract The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.

A New Method for Estimating Tropical Cyclone Wind Speed Probabilities

2009 ◽  
Vol 24 (6) ◽  
pp. 1573-1591 ◽  
Author(s):  
Mark DeMaria ◽  
John A. Knaff ◽  
Richard Knabb ◽  
Chris Lauer ◽  
Charles R. Sampson ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Probability Distributions ◽  
Forecast Error ◽  
Probability Model ◽  
Skill Score ◽  
Wind Structure ◽  
Operational Forecasts ◽  
Error Distributions ◽  
Center Track ◽  
National Hurricane Center

Abstract The National Hurricane Center (NHC) Hurricane Probability Program (HPP) was implemented in 1983 to estimate the probability that the center of a tropical cyclone would pass within 60 n mi of a set of specified points out to 72 h. Other than periodic updates of the probability distributions, the HPP remained unchanged through 2005. Beginning in 2006, the HPP products were replaced by those from a new program that estimates probabilities of winds of at least 34, 50, and 64 kt, and incorporates uncertainties in the track, intensity, and wind structure forecasts. This paper describes the new probability model and a verification of the operational forecasts from the 2006–07 seasons. The new probabilities extend to 120 h for all tropical cyclones in the Atlantic and eastern, central, and western North Pacific to 100°E. Because of the interdependence of the track, intensity, and structure forecasts, a Monte Carlo method is used to generate 1000 realizations by randomly sampling from the operational forecast center track and intensity forecast error distributions from the past 5 yr. The extents of the 34-, 50-, and 64-kt winds for the realizations are obtained from a simple wind radii model and its underlying error distributions. Verification results show that the new probability model is relatively unbiased and skillful as measured by the Brier skill score, where the skill baseline is the deterministic forecast from the operational centers converted to a binary probabilistic forecast. The model probabilities are also well calibrated and have high confidence based on reliability diagrams.

scholarly journals Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

2020 ◽  
Vol 33 (5) ◽  
pp. 1803-1819 ◽  
Author(s):  
Joshua C. Bregy ◽  
Justin T. Maxwell ◽  
Scott M. Robeson ◽  
Jason T. Ortegren ◽  
Peter T. Soulé ◽  
...  
Keyword(s):  
United States ◽  
Tropical Cyclone ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatiotemporal Variability ◽  
Eastern United States ◽  
Percentage Contribution ◽  
Rain Gauges ◽  
The North ◽  
Climate Interactions

AbstractTropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km−1. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States.

scholarly journals A Novel Financial Market for Mitigating Hurricane Risk. Part II: Empirical Validation

2014 ◽  
Vol 6 (3) ◽  
pp. 318-330 ◽  
Author(s):  
Robert J. Meyer ◽  
Michael Horowitz ◽  
Daniel S. Wilks ◽  
Kenneth A. Horowitz
Keyword(s):  
Companion Paper ◽  
Secondary Market ◽  
Eastern United States ◽  
Market Prices ◽  
Prior Probabilities ◽  
Option Trading ◽  
Market Participants ◽  
Individual Outcomes ◽  
Coastal County ◽  
Using Data

Abstract This paper explores the empirical features of a novel commodity option trading instrument described in the companion paper (Part I) that allows market participants to hedge against the risk that a coastal county or region in the eastern United States will experience a hurricane landfall. In this instrument investors can speculate on whether a landfall event will occur in any one of a number of coastal counties or regions, with option prices being determined by an adaptive control algorithm that reflects previous purchasing decisions of other market participants. In this paper, the authors report the results of an experiment designed to test the empirical robustness of this mechanism using data from traders buying landfall options over the course of a simulated hurricane season. In the experiment traders are given the opportunity to buy landfall options in the primary market as well as sell and buy options in a conventional bilateral secondary market. The data show that aggregate market prices quickly converge to rational (efficient) levels among market participants after limited amounts of trading experience. Some systematic anomalies are observed in the trading of options for individual outcomes, however, with the most notable being an initial tendency to overvalue landfall options that have the highest prior probabilities and for valuations of the “No Landfall” option to be inflated immediately after a storm threat passes without making landfall.

A Novel Method of Smooth Fitting for a Class of the Spatial Convex Cavities in the Multiply Connected Domain

2013 ◽  
Vol 765-767 ◽  
pp. 244-247
Author(s):  
Jia Lian Cao ◽  
Chao Yan Wan ◽  
Wen Zhong Zhao
Keyword(s):  
Connected Domain ◽  
Closed Surface ◽  
Entropy Function ◽  
Minimum Entropy ◽  
Multiply Connected Domain ◽  
Rectangular Coordinate System ◽  
Multiply Connected ◽  
System A ◽  
Novel Method ◽  
Maximum Entropy Function

According to a class of closed surfaces fitting problem which cant be solved by using maximum entropy function under the rectangular coordinate system, a new method of smooth fitting for a class of the spatial convex cavities in the multiply connected domain by some planes: the envelope algorithm of minimum entropy function is promoted to the spherical coordinates system, for every closed areas by which the border of spatial convex cavities construct, separately the suitable control parameter is chosen, the minimum entropy function is used to smooth the spatial convex cavities in the multiply connected domain. The smooth fitting graph can be drawn based on the function. This method can be used in soma fields such as closed surface modeling, mold designing, mold manufacturing and reverse engineering.

scholarly journals Structure and Evolution of a Possible U.S. Landfalling Tropical Storm in 2006

2010 ◽  
Vol 138 (1) ◽  
pp. 265-278 ◽  
Author(s):  
Zachary Gruskin
Keyword(s):  
Air Force ◽  
Cross Sections ◽  
Closed Surface ◽  
Tropical Storm ◽  
Doppler Velocity ◽  
Velocity Data ◽  
Warm Core ◽  
Surface Circulation ◽  
Convective Process ◽  
Surface Observations

Abstract A tropical disturbance made landfall near Morehead City, North Carolina, on 27 June 2006. Surface observations, Air Force reconnaissance, and Doppler velocity data suggest that the disturbance had a closed surface circulation at landfall, with maximum 1-min surface winds >18 m s−1, the threshold of tropical storm strength. A cyclostrophic wind calculation using Doppler velocity data and surface observations indicates that the circulation of the disturbance likely caused the tropical storm force winds observed, rather than an environmental pressure gradient or short-lived convective process. Doppler velocity cross sections of the disturbance further suggest that the disturbance was warm core, and an analysis of the disturbance’s environment reveals that latent heat of condensation was likely a large source of energy for the disturbance, though there was some baroclinic forcing. These observations and analyses make a compelling case for the upgrade of the disturbance to a tropical storm in the best-track database.

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