## 1. Introduction

As a forecast tool, the National Hurricane Center’s (NHC) Tropical Cyclone Wind Speed Probability Forecast Product (WPFP; NHC 2006) is used to support operations and decision making of a growing number of government and private agencies including the emergency management community and the National Weather Service (NWS), as well as the space program–related 45th Weather Squadron (45 WS; Harms et al. 1999) and Spaceflight Meteorology Group (SMG; Brody et al. 1997). The 45 WS provides comprehensive weather support to Cape Canaveral Air Force Station and the National Aeronautics and Space Administration’s (NASA) Kennedy Space Center in Florida, including all launch and prelaunch operations, personnel safety, and resource protection. The SMG provides in-flight weather support for the Space Shuttle and other weather support to Johnson Space Center in Texas. Given the location of the Cape Canaveral Air Force Station and Kennedy Space Center, on Florida’s central east coast, tropical cyclones (TCs) pose a potentially significant threat. When a TC threatens, the 45 WS provides detailed information to launch agencies concerning the storm threat including track, timing, intensity, and size (Winters et al. 2006). The 45 WS relays the WPFP and other TC information to senior managers who then decide if and when to begin actions necessary to protect resources. The NWS, whose prime directive is the protection of life and property, is responsible for issuing inland hurricane wind warnings as well as coastal and inland flooding statements to the general public. Despite the potential catastrophic impacts associated with TCs, the WPFP has yet to be fully evaluated—especially for land-threatening or landfalling TC events. Herein, we address two key questions: 1) how well does the product perform and 2) what probability values are significant for making a yes–no decision?

The WPFP is a Monte Carlo–based scheme (DeMaria et al. 2009) in which a large ensemble of possible paths are created using the statistical errors in path (both the along- and cross-storm paths) and intensity in the official NHC forecast product (Gross et al. 2004). Storm radii and radii error information, from climatology and persistence models, are added to the ensemble of paths with the probabilities for a given location determined from this ensemble dataset. Knaff and DeMaria (2006, hereafter KD) evaluated the performance of the WPFP. The KD study focused on the entire Atlantic basin and incorporated all storms in the 2006 hurricane season. The KD work included the bias score (see Table 6) and the Brier skill score, which measures the improvement of the probabilistic forecast relative to climatology (Stefanova and Krishnamurti 2002), as well as the use of reliability diagrams (see section 3d). KD showed that the NHC product has biases with scores ranging from 0.70 to 0.95 (a bias score equal to 1 indicates that the system has no bias) and that the probability product is more skillful than the deterministic forecasts (abbreviated OFCL, the official NHC forecast) in delineating whether or not an event will occur (Brier skill scores ranged from 0.1 to 0.25; a perfect score is 1 while 0 indicates no skill). Reliability diagrams indicated that the product’s predicted probabilities of an event corresponded closely with the observed frequencies.

In lieu of a basin-wide approach, the performance of the NHC WPFP is evaluated for land-threatening and landfalling storms. Here, “land threatening” refers to TCs that either make landfall or are forecast to impact the U.S. coastline with sustained wind speeds of 34 kt (17 m s^{−1}) or greater. Results are presented for four hurricane seasons from 2004 to 2007 and include all TCs affecting the U.S. coastline from Brownsville, Texas, to Bar Harbor, Maine. The data, methods, and results sections follow.

## 2. Data

### a. NHC product forecasts

The NHC issues a suite of TC wind speed probability products (gridded, graphical, and textual) when a storm is a potential threat (i.e., at least one city in the NHC database must have a nonzero interval probability) to coastal regions of the United States and countries in the Atlantic and eastern Pacific basins. The text product consists of wind speed probabilities, by time interval, for a prescribed number of cities and represents only those for a single storm. The text product is generated by the Atlantic Cyclone Forecast System (ATCF; Sampson and Schrader 2000) and is disseminated by the NHC. The performance of this text-based wind probability product for all points along the U.S. coastline from Brownsville, Texas, to Bar Harbor, Maine, is analyzed here. Every 6 h, the WPFP provides probabilities for surface wind speeds of at least 34, 50, and 64 kt (1 kt = 0.52 m s^{−1}) for different forecast time intervals, including 0–12, 12–24, 24–36, 36–48, 48–72, 72–96, and 96–120 h (NWS 2008). Hereafter, the forecast intervals are referred to by the end forecast hour, for example the 0–12-h forecast will be indicated as the 12-h forecast. An example of the wind probability forecast product for Hurricane Wilma on 22 October 2005 at Cocoa Beach, Florida, is shown in Table 1. Probabilities are given for each of the wind speed criteria and forecast time intervals. The product consists of two data columns per forecast time interval: the left column contains the interval forecasts and the right (parenthetical) column the cumulative. The interval probability (IP) is defined as the probability of first occurrence of a given wind speed within a forecast time interval. The cumulative probability is the summation of the interval probabilities and thus reflects whether or not an event will occur during the forecast period (NHC 2006). For this study, the IP for each storm from the 2004 to 2007 hurricane seasons that threatened the U.S. coastline is used.

### b. HURREVAC

An emergency management tool referred to as the Hurricane Evacuation (HURREVAC; FEMA 1995; Sea Island Software Inc. 2006) software package was mined for verification data including position (i.e., observed track), intensity, and size. The HURREVAC geographical information system (GIS) display software output includes the radii for 34-, 50-, and 64-kt wind speeds. This validation database was used in order to maintain consistency with previous studies (Shafer et al. 2007; Shafer 2008). Caveats associated with the use of this software include 1) that the verification is based on the real-time initial best forecast issued by the NHC and thus subsequent refined analyses are not incorporated into the product and 2) that the wind radius in each quadrant is based on the maximum extent of the NHC wind radii in each quadrant. In terms of the latter, the HURREVAC winds can be an overestimate of the actual conditions. Here, this is not considered to be problematic considering the spatial distances between verification locations and the scale of the observational error due to the overestimation. The average spacing for the verification sites is 109 km while the error in the NHC official forecast 0-h wind radii, which are directly used by HURREVAC, is conservatively estimated at 15 km based on the average quadrant error for the least accurate radii (34 kt) as noted by Knaff et al. (2007). In other words, a high probability exists that the wind speed, if overestimated by HURREVAC, would actually be verified by other nearby observations if they existed. Hence, the continuous coverage of the HURREVAC wind radii may be a better verification than the relatively widely spaced surface observations. Additionally, the HURREVAC wind estimates, which are constructed using the initial best-forecast data, represent what is considered to be the conditions at the time the event occurred. It is this information that real-time operations managers respond to, as opposed to reanalyzed datasets.

## 3. Methods

### a. Defining the data subset

Although the study includes 32 coastal cities contained within the NHC forecast matrix, any given forecast consists of only those cities that are potentially impacted by a storm. Hence, the NHC product, which reports an X for probabilities less than 1% (e.g., Table 1), is amended such that the remaining cities and forecast time intervals are assigned a zero probability (i.e., an X). From a forecast verification standpoint these null probabilities can influence measures of forecast skill if they dominate the forecasts (e.g., Doswell et al. 1990). For example, if all the null forecasts are included, it is possible to “saturate” the dataset with “correct negative” forecasts (i.e., “did not occur” events that were correctly forecast; Table 2). However, if ignored, the spurious correct negative forecasts are removed but the “miss” forecasts (i.e., “occur” events that were incorrectly forecast; Table 2) are also eliminated. As a result of the impacts of these null forecasts (see section 3c), a buffer zone is introduced such that locations with null forecasts in proximity to nonzero probabilities are included. For the purposes of this study, proximity is defined to be within two adjacent cities, approximately 220 km, from a forecast location. The two-city buffer reflects a compromise between extremes, that is, an all forecast versus a null free dataset. From the full set of 403 872 forecasts, 12% of the data are retained, while only a relatively modest fraction of the total missed forecasts are discarded (40 out of 114) and all false alarms are retained. The data retention is segregated by wind category and forecast interval in Table 3. Missed forecasts have a higher retention rate in the earlier forecast intervals as might be expected due to decreased forecast precision, that is, larger forecast track uncertainty, with time. Due to the small numbers of missed forecasts in both the full and filtered datasets and the relatively larger numbers in the remaining forecast categories, the skill scores that depend on missed forecasts are minimally impacted. Table 4 lists the number of filtered forecasts and named storms as a function of forecast time interval and wind speed criteria. While 68 named systems occurred during the 2004–07 seasons, many were not land threatening for the validation region and the maximum number of named storms used for any given time interval–wind speed criteria is 30.

### b. Verification statistics

To assess various forecast skill scores, it is necessary to first convert the WPFP probabilities into binary (yes–no) forecasts based on a user-determined decision threshold. To accomplish this, HURREVAC output is used to separate the WPFP probabilities into occurred and did not occur events for all wind speed criteria and forecast intervals (see section 2b). Unfortunately, it is not possible to unambiguously determine the first occurrence for the initial forecast interval (i.e., 0–12 h) because the onset of a given wind speed criteria may have occurred at an earlier time. For these cases, the probabilities are treated as an occurred event for all subsequent 0- to 12-h forecasts. Once these probabilities are converted to binary forecasts, it is possible to calculate various verification statistics. For this study, WPFP forecasts are categorized as a hit, miss, false alarm (FA), or correct negative (CN; Table 2). The classification depends on the selection of a decision threshold, which is used to determine whether or not an event is *forecast* to occur (Table 5). A given performance metric is calculated for all probabilities (0–100) in 1% increments with the probability corresponding to the maximum metric value designated as the decision threshold. This decision threshold is defined such that if the product forecasts a probability greater (less) than or equal to the decision threshold value, the event is forecast to occur (not to occur). The performance metrics used here include the Heidke skill score (HSS), true skill statistic (TSS), and bias score (Table 6). In addition to using the HSS, TSS, and bias as threshold selection methods, they are also used as skill scores for the verification of the WPFP. The probability of detection (PoD), probability of false detection (PoFD), false alarm ratio (FAR), critical success index (CSI), and accuracy are also calculated (Table 6). The PoD and PoFD are presented within the context of the relative operating characteristic (ROC) diagram (see section 3e).

### c. Null forecasts and the use of a buffer zone

The number of null forecasts is significant within the WPFP dataset and influences some of the common verification measures, including those that are often used for converting the probability forecast into a binary forecast. Figure 1 shows the impacts on some of these measures for a geographic subset of the WPFP that was the original focus area for this study (Shafer 2008). The value of these various metrics is calculated for a range of forecast decision thresholds varying from 0% to 100%. Note that the probability of false detection is substantially higher in the dataset where the null cases are filtered, which leads to a decrease in both the accuracy and TSS. Also, the decision threshold value that produces the maximum TSS is higher in the filtered dataset, but the decision thresholds for maximum HSS and CSI remain the same. The TSS is nearly identical to the PoD in the unfiltered dataset—a finding that is consistent with other rare-event studies (Doswell et al. 1990). The impacts of the null data on the decision threshold selection are shown, for all forecast hours and wind radii, in Table 7. The HSS is rather insensitive to the choice of dataset. The TSS has also been shown to overforecast for rare events (Marzban 1998) and thus filtering the null cases should help mitigate this problem. Given these results and the known issues associated with correct negative (correct null) forecasts, the WFPF null forecasts were thinned in the manner described earlier.

### d. Reliability and frequency distributions

Reliability diagrams are frequently used for assessing probability forecasts for binary events (Wilks 2006). They are constructed by plotting the observed frequencies versus the forecast probabilities; the case where the two are equal defines a perfect forecast (Hartmann et al. 2002). In general, if the forecast probability is greater (less) than the observed frequency, the system is over- (under-) forecasting. For this study, to ensure sampling significance, the diagrams are constructed using a bin interval width of 10%. The forecast probabilities are defined using a bin mean that is based on the data distribution rather than the bin center. Frequency distributions (FDs), also known as sharpness diagrams (Jolliffe and Stephenson 2003), are constructed to evaluate whether or not estimates of the reliability are robust.

### e. ROC curves

The ROC diagram is created by calculating a unique PoD and PoFD for each probability (using increments of 1% for probability values ranging from 0% to 100%). The numbers of hits, misses, FAs, and CNs are calculated for each individual probability where the sample space consists of all data within a particular forecast interval and wind speed criteria. Hence, each data point in an ROC diagram represents an estimate of a PoFD–PoD pair for a given probability threshold.

### f. Decision threshold selection

To convert the WPFP probabilities to either an event *forecast* to occur or not to occur, it is first necessary to establish a probability threshold. The decision threshold is somewhat arbitrary in that it is metric dependent. For example, an optimal forecast might be one that maximizes the number of hits while simultaneously minimizing FAs. Here, separate decision thresholds are identified using the TSS, HSS, and bias; all of which are calculated for each of the 1% probability increments. These thresholds are defined by the probabilities associated with their maximum values. Table 8 lists these decision thresholds for each wind speed criterion and forecast time interval. The decision threshold values generally decrease with time and increasing wind speed. TSS threshold values appear to be quite low in comparison to the HSS and bias-based thresholds. A decision threshold that is too low produces too many forecasts for event occurrence, a finding that is consistent with the overforecasting observed using the TSS-based thresholds (section 4c).

## 4. Results

### a. ROC diagrams

ROC diagrams are constructed for each wind speed criteria for the entire U.S. coastline. When the hit rate (PoD) exceeds the false alarm rate (PoFD), the forecast is determined to have skill (Mason and Graham 1999). The ROC diagrams presented herein include a measure of uncertainty that is estimated by employing the bootstrap technique (e.g., Efron and Tibshirani 1993) using the R Project Statistical Computing Verification Package contributed by the National Center for Atmospheric Research (NCAR). PoD and PoFD values are calculated for each of a 1000 subsamples from which 95% confidence intervals (*α* = 0.05) in PoD and PoFD are estimated.

ROC diagrams and associated confidence intervals for the 34-, 50-, and 64-kt wind speed criteria are shown in Fig. 2. Each diagram contains seven forecast time intervals (12, 24, 36, 48, 72, 96, and 120 h). In general, the product exhibits skill that diminishes as the forecast time interval increases. To determine whether the forecast degradation is statistically significant, an estimate of the uncertainty in the ROC curve is incorporated. As previously mentioned, a bootstrap technique is used to produce confidence intervals in PoFD and PoD. As seen in Fig. 2, the ROC curves for the 12- to 48-h forecast products are generally indistinguishable within each wind speed criteria, with the exception that the 12-h interval for the 34-kt forecast exhibits greater skill compared to the remaining forecast hours for low PoFD. In addition, a progressive deterioration in skill is indicated from 72–120 h for all wind speed criteria.

### b. Heidke and true skill scores

In light of the demonstrated skill in the forecast product, two commonly used verification statistics (HSS and TSS) are examined. The TSS is a measure of how well the product distinguishes observed events from nonobserved events while the HSS is a measure of the percent improvement over the skill expected due to random chance (Table 6). Both metrics vary from −1 to +1, with 0 indicating no skill and +1 perfect skill. Figures 3 and 4 depict the TSS and HSS values, determined using the TSS and HSS decision thresholds given in Table 8, as a function of forecast time interval. As one might expect, the TSS decreases as the forecast interval time increases. In Fig. 3a, the TSS values are greater than 0.50 through 72 h − values that are considered to be “good” based on previous work that examined the TSS for extratropical transition (Beven 2008). In contrast, Fig. 3b indicates diminished skill for the TSS with values falling below 0.5 for all wind radii beyond the 48-h interval. The lower HSS-based TSS skill is an artifact of much lower PoD, especially at the longer lead forecasts (not shown). This results from using the higher HSS thresholds even though the PoFD is consistently low. The TSS-based PoD is relatively high and constant throughout the forecast period and results in a higher TSS even though the PoFD is relatively high.

A study by Chu et al. (2007) indicates that an HSS greater than 0.3 is generally considered to be skillful. Here, all wind radii are below this level after the 48-h (72 h) interval for the TSS- (HSS-) based method. Values of HSS decrease as forecast time increases with a steep decrease between the 12- and 24-h forecast time intervals (Fig. 4). The steep decrease is a result of an increase in the ratio of the events that did not occur to the events that did occur (i.e., *C*_{ratio}; Table 6) and a reduction in the TSS between the 12- and 24-h intervals.

### c. Reliability and frequency

A perfectly reliable forecast product will result in a forecast probability equal to that of the observed frequency (Hartmann et al. 2002). To examine whether or not the forecast product contains bias, reliability diagrams are constructed for all wind speed and forecast time intervals. Figure 5a shows the reliability for the full dataset, that is, all forecast intervals and wind speed categories combined. The associated frequency diagram shown in Fig. 5b, which depicts sample size versus probability, may be used to infer the statistical significance of the reliability diagram. The reliability of the WPFP is nearly that expected from a perfect forecast system for probabilities less than 60%, but indicates lower resolution (i.e., an inability of the forecast system to map observed events into unique probabilities) and overforecasting between 60% and 90%. The overforecasting is associated with probability bins that contain the lowest number of forecasts. However, although the reliability may be impacted by small sample size, there are no indications of erratic changes across the probability spectrum as one might expect (e.g., Wilks 1995, Fig. 7.9).

An indirect measure of whether the system over- or underforecasts is presented in the form of a bias score for categorical forecasts (Table 6) determined by using the decision thresholds given in Table 8. For the HSS-based thresholds, bias scores range from 1.00 (perfect) to 2.00 (overforecasts) and remain relatively flat for all forecast time intervals (Fig. 6b). In contrast, the bias scores based on the TSS threshold method trend upward with relatively large values during the latter forecast time intervals (Fig. 6a). The HSS-based bias scores also indicate a tendency to overforecast event probability while the TSS-based bias scores are unrepresentatively high; that is, the reliability diagram does not support this level of bias. The lower decision threshold values obtained by optimizing TSS produce a large number of false alarms, which, in turn, leads to large biases in the TSS-based scores. The WPFP probability forecasts are generated using a large number of randomly distributed TC locations and intensities about the NHC official forecast track that are based on historical distributions of forecast errors for each forecast interval and thus would be expected to be unbiased. In general, this is reasonably validated by the reliability diagram and the HSS-based bias scores.

### d. Forecast bias

Given that the system has an overall tendency to overforecast event probability, what happens to the skill if bias is removed? To examine this, the bias score is used as a decision threshold selection method where the probability corresponding to a bias score equal to one is used to identify the threshold. Figure 7 depicts the TSS and HSS based on the bias score decision threshold (Table 8). When the bias is removed from the product, the TSS decreases (Fig. 7a) and is now lower than the TSS obtained via either the TSS- or HSS-based threshold methods (Fig. 3). This is especially true for the early forecast intervals (12–48 h). In contrast, the bias score–based HSS (Fig. 7b) is only slightly less than the HSS-based one(Fig. 4b). The greater sensitivity to the bias removal observed in the TSS skill is consistent with previous results indicating larger bias when using TSS-based thresholds (Fig. 6).

Given the sensitivity of the skill scores to the decision threshold selection, an additional method is used to evaluate forecast system bias. For each wind speed category and forecast interval, the number of “expected” events is calculated using the assumption that the WPFP forecasts are perfectly reliable. The number of expected events, the number of actual events, and the ratio of the number of expected events to the number of actual events is listed in Table 9. Ratios greater (less) than 1 indicate over- (under-) forecasting of the event probability. The results are consistent with the reliability and bias scores with overforecasting in the 34-kt wind speed category, and a tendency to underforecast in the later forecast intervals for both the 50- and 64-kt wind speed categories. Because of the large number of events in the 34-kt wind speed category, the forecast system as a whole has a slight tendency to overforecast the event probability with a total of 3287 expected events and 2871 actual events (a ratio of 1.14). This result is consistent with the reliability diagram (Fig. 5a).

It is worth pointing out that if the verification product, a potential source of forecast evaluation error, has a tendency to overestimate winds (see section 2b), this would result in a tendency to underforecast probabilities relative to a perfect forecast system as events would be erroneously identified as occurrences. Given the overall observed tendency of the WPFP to overforecast, a procedure to reduce the wind radii (i.e., to compensate for product bias) would actually degrade the overall WPFP skill by decreasing the number of hits. Also, the mixed results in the bias by wind category (i.e., the probabilities are overforecast for 34 kt and underforecast for 50–64 kt) indicate an inconsistent bias, while an error in the verification procedure would be expected to produce a consistent bias. Thus, while error in the verification product cannot be dismissed, error in the verification procedure alone would not appear to explain these results.

### e. Decision thresholds and forecast probability

Heretofore, decision thresholds have been applied to the TSS, HSS, and bias score in order to gauge the performance of the NHC WPFP. However, it is not clear what these thresholds represent within the context of the probabilities forecast for a given time interval and wind speed. Table 10 represents the maximum probability forecast as a function of wind speed and time interval. Clearly, the maximum probability forecast decreases as the forecast time interval increases with a greater decrease for the higher wind speeds. A direct comparison with Table 8 indicates small probabilities may actually be important within the context of decision making. For example, consider a 5% forecast probability for the 64-kt wind speed at the 12- and 120-h intervals. In the former, the maximum probability forecast is 100% while it is 6% for the latter. For the HSS, the decision thresholds are 19% and 3% for the 12- and 120-h intervals, respectively. Therefore, the 5% forecast probability is meaningful for decision making at the 120-h interval.

## 5. Conclusions and future work

The performance of the National Hurricane Center’s Tropical Cyclone Wind Speed Probability Forecast Product is evaluated for U.S. land-threatening and landfalling storms. Results are composited from the 2004 to 2007 hurricane seasons and include all tropical cyclones affecting the U.S. coastline. Decision thresholds are estimated using the true skill statistic and the Heidke skill score for three wind speed criteria (34, 50, and 64 kt) and seven forecast time intervals (12, 24, 36, 48, 72, 96, and 120 h) to convert the probabilities into binary (yes–no) forecasts. Overall, the WPFP performed well. As expected, the skill of the forecast system decreases as the forecast time interval increases. However, both the HSS and TSS demonstrate skill out to the 48- and 72-h intervals, respectively (Beven 2008; Chu et al. 2007).

In addition to the threshold-based verification statistics, reliability diagrams were constructed to assess the performance of the forecast system based on binary events. Reliability diagrams and bias scores constructed using all wind speed categories and forecast intervals combined, and comparisons with perfectly reliable forecasts, indicate that the NHC product has an overall tendency to overforecast event occurrence for forecast probabilities greater than 60%. When stratified by wind speed and forecast interval, the results indicate that the overforecasting is primarily due to the 34-kt wind speed category and that the system actually underforecasts in the later forecast intervals for the 50- and 64-kt wind speed categories.

The decision thresholds, which range from 1% to 55% depending on the selection method, are given perspective within the context of the maximum probabilities forecast. It was shown that small forecast probabilities may actually be meaningful at higher wind speeds and for later forecast periods. The HSS is preferred as a method for selecting decision thresholds over the TSS for this dataset as the TSS thresholds produce large bias errors.

The evaluation of the performance of the WPFP is intended, in part, to aid in operational decisions at Cape Canaveral Air Force Station and Kennedy Space Center. Applications obviously extend beyond the original intent of this work and can be tailored to fit the needs of a particular user in order to determine the optimum strategy for a specific application. One limitation of this research is that WPFP results were available for only 4 yr (2004–07). The results should be improved by adding future hurricane seasons to the analysis. The results can be used to provide objective first-guess guidance of tropical cyclone watches and warnings to forecasters (e.g., Mainelli et al. 2008) by making use of the optimal decision thresholds. In addition, product users would benefit from guidance on what the significant probabilities are for each wind speed and forecast interval. This could be done in the form of a table that converts a range of forecast probabilities into a risk of occurrence category, such as very low, low, medium, high, and very high risk, for each of the three wind speed thresholds and seven forecast intervals. As mentioned earlier, there may be inconsistencies between the HURREVAC verification and the surface observations. Future work could quantify the frequency and impact of using the HURREVAC verification rather than actual surface observations. The study could be repeated with surface observations if the need is indicated.

## Acknowledgments

This research was initially supported by funding under the Scitor Corporation Summer Intern Program with supervision provided by Mr. David Froiseth of Scitor Corporation and the 45 WS. The project was later extended with support from the Kennedy Space Center (Grant NNM06AA10A, Supplement 2). The authors gratefully acknowledge the contributions of Mr. Michael McAleenan of the 45 WS for his guidance and HURREVAC expertise, Ms. Kathy Winters of 45 WS for her forecast guidance and expertise on Space Shuttle operations, and Dr. Mark DeMaria of NESDIS for providing after-the-fact probability forecasts for the 2004 hurricane season. Also, Matt Pocernich of the NCAR Research Applications Program provided modifications to the R Verification software that expedited the analysis of ROC confidence intervals. Comments from two anonymous reviewers led to a significant improvement of this manuscript.

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The 0300 UTC wind speed probability product for Cocoa Beach, FL, issued during Hurricane Wilma on 22 Oct 2005. Here, an X denotes a forecast probability of less than 1.0%. See text for more details.

Classifications assigned to the probability forecasts.

Data retention in the filtered dataset for all forecasts and missed forecasts. Total numbers of observations retained for each forecast interval are set in parentheses in the final column.

Numbers of forecasts and tropical cyclone events used in the study for each wind speed criteria (kt) and forecast time interval (h).

Contingency for the classification of probability forecasts.

Verification statistics.

Decision thresholds (%) for each wind speed criteria (kt) and forecast time interval (h) that maximizes TSS, and HSS using the WPFP data subset along the east coast of FL. Thresholds using the same dataset with null forecasts removed are in parentheses.

Decision thresholds (%) for each wind speed criteria (kt) and forecast time interval (h) based on the TSS, HSS, and bias score selection methods.

The number of “expected” events for each wind speed criteria (kt) and forecast time interval (h) assuming perfect reliability in the WPFP, the actual number of events, and their ratio (expected–actual).

Maximum probability (%) forecast as a function of wind speed criteria (kt) and time interval (h).