Search Results

You are looking at 31 - 36 of 36 items for

  • Author or Editor: Charles R. Sampson x
  • Refine by Access: All Content x
Clear All Modify Search
Charles R. Sampson, Edward M. Fukada, John A. Knaff, Brian R. Strahl, Michael J. Brennan, and Timothy Marchok

Abstract

The Joint Typhoon Warning Center’s (JTWC) forecast improvement goals include reducing 34-kt (1 kt = 0.514 m s−1) wind radii forecast errors, so accurate real-time estimates and postseason analysis of the 34-kt wind radii are critical to reaching this goal. Accurate real-time 34-kt wind radii estimates are also critical for decisions regarding base preparedness and asset protection, but still represent a significant operational challenge at JTWC for several reasons. These reasons include a paucity of observations, the timeliness and availability of guidance, a lack of analysis tools, and a perceived shortage of personnel to perform the analysis; however, the number of available objective wind radii estimates is expanding, and the topic of estimating 34-kt wind radii warrants revisiting. In this work an equally weighted mean of real-time 34-kt wind radii objective estimates that provides real-time, routine operational guidance is described. This objective method is also used to retrospectively produce a 2-yr (2014–15) 34-kt wind radii objective analysis, the results of which compare favorably to the postseason National Hurricane Center data (i.e., the best tracks), and a newly created best-track dataset for the western North Pacific seasons. This equally weighted mean, when compared with the individual 34-kt wind radii estimate methods, is shown to have among the lowest mean absolute errors and smallest biases. In an ancillary finding, the western North Pacific basin average 34-kt wind radii calculated from the 2014–15 seasons are estimated to be 134 n mi (1 n mi = 1.852 km), which is larger than the estimates for storms in either the Atlantic (95 n mi) or eastern North Pacific (82 n mi) basins for the same years.

Full access
John A. Knaff, Charles R. Sampson, Mark DeMaria, Timothy P. Marchok, James M. Gross, and Colin J. McAdie

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.

Full access
Carolyn A. Reynolds, Justin G. McLay, James S. Goerss, Efren A. Serra, Daniel Hodyss, and Charles R. Sampson

Abstract

The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.

Full access
Charles R. Sampson, Andrea B. Schumacher, John A. Knaff, Mark DeMaria, Edward M. Fukada, Chris A. Sisko, David P. Roberts, Katherine A. Winters, and Harold M. Wilson

Abstract

The Department of Defense uses a Tropical Cyclone Conditions of Readiness (TC-CORs) system to prepare bases and evacuate assets and personnel in advance of adverse weather associated with tropical cyclones (TCs). TC-CORs are recommended by weather facilities either on base or at central sites and generally are related to the timing and potential for destructive (50 kt; 1 kt ≈ 0.5144 m s−1) sustained winds. Recommendations are then considered by base or area commanders along with other factors for setting the TC-CORs. Ideally, the TC-CORs are set sequentially, from TC-COR IV (destructive winds within 72 h), through TC-COR III (destructive winds within 48 h) and TC-COR II (destructive winds within 24 h), and finally to TC-COR I (destructive winds within 12 h), if needed. Each TC-COR, once set, initiates a series of preparations and actions. Preparations for TC-COR IV can be as unobtrusive as obtaining emergency supplies, while preparations and actions leading up to TC-COR I are generally far more costly, intrusive, and labor-intensive activities. The purpose of this paper is to describe an objective aid that provides TC-COR guidance for meteorologists to use when making recommendations to base commanders. The TC-COR guidance is based on wind probability thresholds from an operational wind probability product run at the U.S. tropical cyclone forecast centers. An analysis on 113 independent cases from various bases shows the skill of the objective aid and how well it compares with the operational TC-CORs. A sensitivity analysis is also performed to demonstrate some of the advantages and pitfalls of raising or lowering the wind probability thresholds used by this objective aid.

Full access
Mark DeMaria, John A. Knaff, Michael J. Brennan, Daniel Brown, Richard D. Knabb, Robert T. DeMaria, Andrea Schumacher, Christopher A. Lauer, David P. Roberts, Charles R. Sampson, Pablo Santos, David Sharp, and Katherine A. Winters

Abstract

The National Hurricane Center Hurricane Probability Program, which estimated the probability of a tropical cyclone passing within a specific distance of a selected set of coastal stations, was replaced by the more general Tropical Cyclone Surface Wind Speed Probabilities in 2006. A Monte Carlo (MC) method is used to estimate the probabilities of 34-, 50-, and 64-kt (1 kt = 0.51 m s−1) winds at multiple time periods through 120 h. Versions of the MC model are available for the Atlantic, the combined eastern and central North Pacific, and the western North Pacific. This paper presents a verification of the operational runs of the MC model for the period 2008–11 and describes model improvements since 2007. The most significant change occurred in 2010 with the inclusion of a method to take into account the uncertainty of the track forecasts on a case-by-case basis, which is estimated from the spread of a dynamical model ensemble and other parameters. The previous version represented the track uncertainty from the error distributions from the previous 5 yr of forecasts from the operational centers, with no case-to-case variability. Results show the MC model provides robust estimates of the wind speed probabilities using a number of standard verification metrics, and that the inclusion of the case-by-case measure of track uncertainty improved the probability estimates. Beginning in 2008, an older operational wind speed probability table product was modified to include information from the MC model. This development and a verification of the new version of the table are described.

Full access
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.

Full access