Andrea B. Schumacher

Tropical Cyclone Intensity Change before U.S. Gulf Coast Landfall

Tropical cyclone intensity change remains a forecasting challenge with important implications for such vulnerable areas as the U.S. coast along the Gulf of Mexico. Analysis of 1979‐2008 Gulf tropical cyclones during theirfinal twodays before U.S. landfall identifies patterns of behavior that are of interest to operational forecasters and researchers. Tropical storms and depressions strengthened on average by about 7 kt for every 12 h over the Gulf, except for little change during their final 12 h before landfall. Hurricanes underwent a different systematic evolution. In the net, category 1‐2 hurricanes strengthened, while category 3‐5 hurricanes weakened such that tropical cyclones approach the threshold of major hurricane status by U.S. landfall. This behavior can be partially explained by consideration of the maximum potential intensity modified by the environmental vertical wind shear and hurricane-induced sea surface temperature reduction near the storm center associated with relatively low oceanic heat content levels. Linear least squares regression equations basedoninitialintensityandtimetolandfallexplainatleasthalfthevarianceofthehurricaneintensitychange. Applied retrospectively, these simple equations yield relatively small forecast errors and biases for hurricanes. Characteristics of most of the significant outliers are explained and found to be identifiable a priori for hurricanes, suggesting that forecasters can adjust their forecast procedures accordingly.

Objective Estimation of the 24-h Probability of Tropical Cyclone Formation

A new product for estimating the 24-h probability of TC formation in individual 5 83 58 subregions of the North Atlantic, eastern North Pacific, and western North Pacific tropical basins is developed. This product uses environmental and convective parameters computed from best-track tropical cyclone (TC) positions, National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) analysis fields, and water vapor (;6.7 mm wavelength) imagery from multiple geostationary satellite platforms. The parameters are used in a two-step algorithm applied to the developmental dataset. First, a screening step removes all data points with environmental conditions highly unfavorable to TC formation. Then, a linear discriminant analysis (LDA) is applied to the screened dataset. A probabilistic prediction scheme for TC formation is developed from the results of the LDA. Coefficients computed by the LDA show that the largest contributors to TC formation probability are climatology, 850-hPa circulation, and distance to an existing TC. The product was evaluated by its Brier and relative operating characteristic skill scores and reliability diagrams. These measures show that the algorithmgenerated probabilistic forecasts are skillful with respect to climatology, and that there is relatively good agreement between forecast probabilities and observed frequencies. As such, this prediction scheme has been implemented as an operational product called the National Environmental Satellite, Data, and Information Services (NESDIS) Tropical Cyclone Formation Probability (TCFP) product. The TCFP product updates every 6 h and displays plots of TC formation probability and input parameter values on its Web site. At present, the TCFP provides real-time, objective TC formation guidance used by tropical cyclone forecast offices in the Atlantic, eastern Pacific, and western Pacific basins.

Improvements to the Operational Tropical Cyclone Wind Speed Probability Model

AbstractThe 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 uncertai...

Incorporating Hurricane Forecast Uncertainty into a Decision-Support Application for Power Outage Modeling

A variety of decision-support systems, such as those employed by energy and utility companies, use the National Hurricane Center (NHC) forecasts of track and intensity to inform operational decision making as a hurricane approaches. Track and intensity forecast errors, especially just prior to landfall, can substantially impact the accuracy of these decision-support systems. This study quantifies how forecast errors can influence the results of a power outage model, highlighting the importance of considering uncertainty when using hurricane forecasts in decision-support applications. An ensemble of 1,000 forecast realizations is generated using the Monte Carlo wind speed probability model for Hurricanes Dennis, Ivan, and Katrina. The power outage model was run for each forecast realization to predict the spatial distribution of power outages. Based on observed power outage data from a Gulf Coast utility company, the authors found that in all three cases the ensemble average was a better predictor of power...

Multidisciplinary Analysis of an Unusual Tornado: Meteorology, Climatology, and the Communication and Interpretation of Warnings*

Abstract On 22 May 2008, a strong tornado—rated EF3 on the enhanced Fujita scale, with winds estimated between 136 and 165 mi h−1 (61 and 74 m s−1)—caused extensive damage along a 55-km track through northern Colorado. The worst devastation occurred in and around the town of Windsor, and in total there was one fatality, numerous injuries, and hundreds of homes significantly damaged or destroyed. Several characteristics of this tornado were unusual for the region from a climatological perspective, including its intensity, its long track, its direction of motion, and the time of day when it formed. These unusual aspects and the high impact of this tornado also raised a number of questions about the communication and interpretation of information from National Weather Service watches and warnings by decision makers and the public. First, the study examines the meteorological circumstances responsible for producing such an outlier to the regional severe weather climatology. An analysis of the synoptic and mes...

Objective Guidance for Use in Setting Tropical Cyclone Conditions of Readiness

TheDepartmentofDefenseusesaTropicalCycloneConditionsofReadiness(TC-CORs)systemtoprepare basesandevacuateassetsandpersonnelinadvanceofadverseweatherassociatedwithtropicalcyclones(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 21 ) 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.

Wave Probabilities Consistent with Official Tropical Cyclone Forecasts

AbstractDevelopment of a 12-ft-seas significant wave height ensemble consistent with the official tropical cyclone intensity, track, and wind structure forecasts and their errors from the operational U.S. tropical cyclone forecast centers is described. To generate the significant wave height ensemble, a Monte Carlo wind speed probability algorithm that produces forecast ensemble members is used. These forecast ensemble members, each created from the official forecast and randomly sampled errors from historical official forecast errors, are then created immediately after the official forecast is completed. Of 1000 forecast ensemble members produced by the wind speed algorithm, 128 of them are selected and processed to produce wind input for an ocean surface wave model. The wave model is then run once per realization to produce 128 possible forecasts of significant wave height. Probabilities of significant wave height at critical thresholds can then be computed from the ocean surface wave model–generated si...

Improving tropical cyclone intensity forecasts with PRIME

AbstractThe Prediction of Intensity Model Error (PRIME) forecasting scheme uses various large-scale meteorological parameters as well as proxies for initial condition uncertainty and atmospheric flow stability to provide operational forecasts of tropical cyclone intensity forecast error. PRIME forecasts of bias and absolute error are developed for the Logistic Growth Equation Model (LGEM), Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), Hurricane Weather Research and Forecasting Interpolated Model (HWFI), and Geophysical Fluid Dynamics Laboratory Interpolated Hurricane Model (GHMI). These forecasts are evaluated in the Atlantic and east Pacific basins for the 2011–15 hurricane seasons. PRIME is also trained with retrospective forecasts (R-PRIME) from the 2015 version of each model. PRIME error forecasts are significantly better than forecasts that use error climatology for a majority of forecast hours, which raises the question of whether PRIME could provide more than error guidance. PRIME...