APRIL 2016 F O R E C A S T E R S ’ F O R U M 451

FORECASTERS’ FORUM

Maximum Wind Gusts Associated with Human-Reported Nonconvective Wind Events and a Comparison to Current Warning Issuance Criteria

PAUL W. MILLER Department of Geography, The University of Georgia, Athens, Georgia

ALAN W. BLACK IIHR–Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa

CASTLE A. WILLIAMS AND JOHN A. KNOX Department of Geography, The University of Georgia, Athens, Georgia

(Manuscript received 3 September 2015, in final form 31 December 2015)

ABSTRACT

Nonconvective high winds are a deceptively hazardous meteorological phenomenon. Though the (NWS) possesses an array of products designed to alert the public to nonconvective wind po- tential, documentation justifying the choice of issuance thresholds is scarce. Measured wind speeds from the GlobalHistoricalClimatologyNetwork (GHCN)-Daily dataset associated with human-reported nonconvective wind events from Storm Data are examined in order to assess the suitability of the current gust criteria for the NWS and high wind warning. Nearly 92% (45%) of the nonconvective wind events considered from Storm 2 Data were accompanied by peak gusts beneath the high wind warning (wind advisory) threshold of 58 mi h 1 2 2 2 (25.9 m s 1)[46mih 1 (20.6 m s 1)], and greater than 74% (28%) of all fatal and injury-causing events were associated with peak gusts below these same product gust criteria. NWS wind products were disproportionately issued in areas of complex terrain where wind climatologies include a greater frequency of high wind warning 2 threshold-level gusts, irrespective of observed impacts. For many areas of the eastern United States, a 58 mi h 1 2 (25.9 m s 1) gust of convective, tropical, or nonconvective origin falls within the top 0.5% of all observed daily maximum wind gusts, nearly eliminating the possibility of a nonconvective gust meeting the issuance criterion.

1. Introduction across larger spatial and temporal scales and produce a more homogeneous wind field as compared to winds as- Hazardous high winds can be generated by a number of sociated with or tornadoes (Lacke et al. meteorological mechanisms including thunderstorms and 2007; Ashley and Black 2008; Pryor et al. 2014). tropical cyclones. However, the steep gradients of sur- The National Weather Service (NWS) possesses an face atmospheric pressure responsible for windstorms array of products designed to communicate the risk of can also be generated by extratropical cyclones and hazardous nonconvective winds to the public. Such even terrain differences [e.g., downslope and gap winds; products include the somewhat familiar ‘‘wind advisory’’ Knox et al. (2011)]. In these instances, winds are termed and ‘‘high wind warning,’’ as well as a multitude of ma- nonconvective and may occur without the presence of rine/coastal wind products (e.g., , small clouds or precipitation. Nonconvective winds tend to occur craft advisory for winds, brisk wind advisory, warning, , hurricane force wind warning) and an additional suite of products tailored for specific Corresponding author address: Paul Miller, Dept. of Geography, The University of Georgia, Rm. 204, 210 Field St., Athens, GA circumstances in which wind is one of several criteria 30602. (e.g., warning, warning, etc.). The E-mail: [email protected] issuance criteria for wind advisories and high wind

DOI: 10.1175/WAF-D-15-0112.1

Ó 2016 American Meteorological Society Unauthenticated | Downloaded 09/26/21 03:57 AM UTC 452 WEATHER AND FORECASTING VOLUME 31

TABLE 1. Issuance criteria for NWS wind advisories and high high wind warning, it is important that the thresholds 21 21 wind warnings [mi h (m s )] (National Weather Service 2015a). actually capture the wind speeds that lead to losses. The These are baseline criteria that can vary from region to region. goal of this research is to identify the wind speeds that However, no comprehensive documentation of the regional defi- nitions could be found. result in nonconvective losses, and assess the suitability of wind advisory and high wind warning gust thresholds. It is Criterion Wind advisory High wind warning hoped that this research will initiate a discussion about Sustained 25–39 (11.2–17.4) $40 (17.9) the suitability of the current NWS wind advisory and high Gust 46–57 (20.6–25.5) $58 (26.0) wind warning criteria, ultimately serving to improve their utility and reduce nonconvective wind losses. warnings can be achieved by either sustained winds or 2. Data wind gusts (Table 1). Though the justification for the wind speed thresholds Two datasets were used extensively in this analysis, used to define a wind advisory and high wind warning is performed using data from the period 1996–2013 for the unknown, other details of the products’ histories are more continental United States. The first dataset, the Storm certain. As part of the NWS modernization during the Events Database (National Centers for Environmental early 1990s, the agency significantly restructured the for- Information 2015), contains the records used to create mat (S. Nelson, NWS Peachtree City, 2015, personal the official Storm Data publication, which is our second communication) and variety (National Weather Service dataset. The Storm Events Database and Storm Data 2015b) of the weather products that it issued. This expan- are maintained by the National Centers for Environ- sion led to the development of the ‘‘nonprecipitation’’ mental Information (NCEI) and catalog the occurrence weather products, including the wind products mentioned of severe, hazardous, or unusual weather of many types above (National Weather Service 2015b). Following an across the United States. The Storm Data publication exhaustive review of sources documenting the moderni- compiles nonconvective wind events beginning in 1959, zation, including an exchange with the NWS director who but records have only been digitized within the Storm oversaw the modernization program (J. Friday, NWS, Events Database for the period from 1996 to the pres- 2015, personal communication), an explanation for the ent. Commonly used in severe weather research, Storm wind speed criteria could not be discovered. Data and the Storm Events Database are the most In the absence of clear documentation, the issuance comprehensive datasets for hail, wind, criteria’s origins can only be speculated upon. The and tornadoes, although they have also been applied to thresholds may possibly be based upon wind speeds be- other weather hazards such as fatal lightning strikes lieved to affect the 1950s aviation industry, or the wind (López et al. 1995; Ashley and Gilson 2009), blizzard force category of the associated with tree climatologies (Schwartz and Schmidlin 2002), and failure. For example, the high wind warning gust threshold nonconvective wind fatalities (Ashley and Black 2008), 2 2 of 58 mi h 1 (25.9 m s 1) is identical to the NWS threshold among others. Storm Data and the Storm Events Da- used to define severe wind gusts from thunderstorms. tabase contain measured and estimated reports from a According to Galway (1989), this selection was influenced number of sources [e.g., Automated Surface Observ- 2 2 by the U.S. Air Force’s use of 58 mi h 1 (25.9 m s 1)asthe ing System (ASOS) data, trained spotters, the general lower limit of their severe thunderstorm wind gust public, or social media]. However, researchers have 2 2 definition. The 58 mi h 1 (25.9 m s 1) threshold may criticized both sources for documented errors in the simply have been carried over from thunderstorm to times and locations given for severe weather events nonconvective wind products within the NWS. (e.g., Witt et al. 1998a,b; Williams et al. 1999; Trapp However, the application of aviation thresholds to the et al. 2006), as well as for the underreporting of fatalities warning criteria for nonconvective wind products may (López et al. 1993; Black and Mote 2015). not be appropriate. In the United States, nonconvective Nonconvective winds can appear under a number of winds kill an average of 24 people annually (Ashley and categories within a Storm Data publication (Ashley Black 2008), but Black and Ashley (2010) found no and Black 2008), especially prior to the 2005 issuance deaths from nonconvective wind-related aviation crashes of NWS directives on Storm Data that standardized from 1977 to 2007. As such, any nonconvective wind many meteorological event categories (National product threshold should originate in observed impacts Oceanic and Atmospheric Administration 2007). among the general population. Since these wind speed However, nonconvective wind reports in the Storm thresholds determine whether the NWS alerts the public Events Database for the 1996–2013 period have been of dangerous weather conditions via a wind advisory or standardized in accordance with these instructions,

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC APRIL 2016 F O R E C A S T E R S ’ F O R U M 453

FIG. 1. Histograms of (a) Storm Data reported gusts for the 17 151 events that included a magnitude value and (b) GHCN-Daily peak gust measurements for the same events shown in (a).

eliminating the need for a subjective analysis to de- for an explanation of these acronyms). Data from termine which events are nonconvective in nature, as in GHCN stations is quality controlled by NCEI (Menne Ashley and Black (2008). Within the Storm Events Da- et al. 2012a) and can be considered more reliable than tabase, nonconvective wind events are categorized as ei- estimates based on human judgments despite their ther ‘‘high wind’’ or ‘‘strong wind,’’ and these events were spatial displacement from where the report may have extracted for our analysis. For the sake of consistency originated. Given the synoptic scale of these non- with other studies that use hazardous weather event data convective wind events, nearby GHCN stations can from Storm Data, we refer to the data extracted from the Storm Events Database as Storm Data hereafter. TABLE 2. Daily GHCN station wind products, their codes, Theoretically, the values within Storm Data’s mag- explanations, and units. nitude field can be analyzed to discern the strength of the wind gusts that trigger significant wind events. Measurement However, the distribution of Storm Data wind magni- code Explanation Units 2 tudes clearly indicates a systematic trend in the entry AWND Avg daily wind speed 1/10 m s 1 method that would bias any analysis (Fig. 1). The spikes WDF1 Direction of fastest 1-min wind 8 21 21 WDF2 Direction of fastest 2-min wind 8 in Fig. 1a at 45–47.4 mi h (20.1–21.2 m s ) and 57.5– 8 21 21 WDF5 Direction of fastest 5-s wind 59.9 mi h (25.7–26.8 m s ) correspond to the default WDFG Direction of peak wind gust 8 minimum NWS gust thresholds for wind advisory and WDFI Direction of highest instantaneous 8 high wind warning criteria (Table 1), respectively. For wind this reason, daily wind observations were retrieved WDFM Fastest mile wind direction 8 WDMV 24-h wind movement km from NCEI’s Global Historical Climatology Network 2 WSF1 Fastest 1-min wind speed 1/10 m s 1 2 (GHCN)-Daily dataset (Menne et al. 2012b). GHCN WSF2 Fastest 2-min wind speed 1/10 m s 1 2 stations that measure wind observations can provide the WSF5 Fastest 5-s wind speed 1/10 m s 1 2 data in a variety of formats (Table 2). For this study, WSFG Peak gust wind speed 1/10 m s 1 2 WSFI Highest instantaneous wind speed 1/10 m s 1 only the AWND, WSF1, WSF2, WSF5, WSFG, and 21 WSFI measurements were considered (refer to Table 2 WSFM Fastest mile wind speed 1/10 m s

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC 454 WEATHER AND FORECASTING VOLUME 31 offer a reasonable depiction of the intensity of an event. more than once within a windy period were aggregated Comparing the histograms of GHCN maximum gusts into a single event. For each aggregated event, the versus Storm Data reported gusts shows that the GHCN maximum GHCN-Daily average wind and gust from observations create a smoother, fuller, and more sym- that zone during the entire period were retained. These metric histogram than do the Storm Data magnitudes aggregated events were not given a ‘‘source’’ since the (Fig. 1b). For this reason, Storm Data results are used constituent reports during the windy period often orig- only to provide the date and forecast zone for which a inated from different sources. nonconvective wind event occurred, with the actual After correcting for spatial and temporal issues be- magnitude of the wind coming from the GHCN-Daily tween the datasets, all Storm Data events were joined dataset. to a wind-observing GHCN station within the affected zone, if one existed. GHCN peak gusts could be paired with 17 151 Storm Data events. For each pair, the max- 3. Methods imum daily wind measurement (i.e., WSF1, WSF2, GHCN wind measurements were paired to Storm WSF5, WSFG, and WSFI) recorded by any station Data events with which they coincided temporally and within the Storm Data event zone at any time during any spatially. Each Storm Data event analyzed has in- day either partially or entirely encompassed by the formation on the date, time, and NWS forecast zone Storm Data event was extracted. This was driven by the where the high wind occurred. To successfully pair the GHCN-Daily archive structure that records only maxi- greatest number of Storm Data events with GHCN mum wind gusts on a given day, with the time of the gusts, the spatial details of each Storm Data entry were maximum gust rarely included. Thus, the maximum gust adjusted to reflect a current NWS forecast zone. If the on days only partially included in a Storm Data event NWS zone had changed during the period of record, was assumed to have occurred during the event. Since other information in the Storm Data entry or narrative Storm Data records periods of exceptional weather im- was used to determine the equivalent modern-day NWS pact, it is very reasonable to assume that the peak gust zone. If multiple GHCN stations existed within the for a given day contributed to the event being refer- NWS zone, then the Storm Data event was assigned enced in Storm Data. multiple daily wind observations. As Fig. 1b indicates, most GHCN peak gust obser- There were also data consistency challenges when vations fell within reasonable expectations except for 2 2 combining the GHCN wind observations and Storm 49 gusts that exceeded 100 mi h 1 (44.7 m s 1). Though 2 2 Data. The time and date of high wind events in Storm gusts greater than 100 mi h 1 (44.7 m s 1) are possible, Data were recorded in two distinct forms. The first, especially given our focus of significant nonconvective and most frequent, method treats Storm Data events high wind events, the integrity of the observations was as temporally continuous with a nonzero duration nonetheless investigated. Upon manual inspection, 38 period (i.e., the begin time does not equal the end of these 49 events were recorded at the Mount Wash- time). In these cases, all instances of high wind gusts ington Observatory in New Hampshire (station ID or damage during the windy period had been aggre- USW00014755). These observations were judged not to gated together and given a single narrative and a be representative of the wind speeds regularly experi- single representative wind magnitude for the event, enced by humans in the northern New Hampshire area, resulting in one Storm Data entry for the entire and all Mount Washington observations were dis- windy period. carded. The other 11 observations originated from 10 The second, less frequent, method (consisting of ap- GHCN stations but were given no quality flags by proximately 6% of events) treats Storm Data events as NCEI; these observations were allowed to remain in discrete points in time (i.e., begin time equals end time). the dataset. The same NWS zone could then appear multiple times The distribution of GHCN maximum daily gusts within a windy period, with each entry in Storm Data (Fig. 1b), though more realistic than the distribution of representing an individual instance of a high wind gust in Storm Data magnitudes (Fig. 1a), displays two curious that zone and given its own reported magnitude specific features. One spike is apparent near, but slightly less to that instance. This method results in a single windy than, the wind advisory gust criterion and another is period having multiple entries in Storm Data, and cre- found within the bin exactly capturing the high wind ates problems for the statistical analysis because the warning gust threshold. This result is interpreted as an wind observation from a nearby GHCN station would artifact of the warning issuance criteria. Even though then be unequally represented within the dataset. Thus, Fig. 1b is composed of independently recorded GHCN any Storm Data events with their NWS zone appearing wind observations, NWS employees ultimately choose

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC APRIL 2016 F O R E C A S T E R S ’ F O R U M 455

FIG. 2. Workflow schematic for assembling the final dataset presented in section 4. Solid strikethroughs indicate an event was removed from consideration because the report originated from an automated station. Values with gray backgrounds were used to create the histogram in Fig. 3. when to submit an event to Storm Data. Since many descriptions simply state that the wind was detected by Storm Data entries reference an ASOS, an Automated an instrument [i.e., ‘‘Wind gust of 52 knots (60 mph) was Weather Observation System (AWOS), a mesonetwork, measured at ORF’’]. or an official NWS observation as the source of the re- Thus, Fig. 1b was reproduced with reports only from 2 port, it is believed that the spike at 57.5–59.9 mi h 1 human sources.1 For clarity, these values were still 2 (25.7–26.8 m s 1) is a result of automated station ob- retrieved from GHCN stations (which include ASOS servations (which are also recorded in the GHCN net- stations), but only if Storm Data cited a human, not an work) serving as the ‘‘source’’ for subsequent Storm ASOS, as the source of the report. By eliminating Data reports. Given that the purpose of this study is to ‘‘reports’’ easily accessible to NWS employees identify the wind speeds at which human activity is im- through the Advanced Weather Interactive Process- pacted, Storm Data reports arising simply from gusts ing System (AWIPS) display, the influence of policy- exceeding NWS wind product criteria at these auto- relatedthresholdsisexpectedtobeminimized.The mated stations in the absence of human impact are un- large decrease in sample size resulting from the desired. Though it is possible that instrument-reported elimination of automated reports is not desirable; events could have been accompanied by human impacts, however, given the increased likelihood that these the lack of detail in Storm Data’s events descriptions entries were prompted by genuine human impacts, make such a determination difficult. While fewer than this subset will be used throughout the remainder of half of Storm Data’s events contain an event de- the analysis. scription, descriptions are more often included for To summarize, the final dataset consists of 4271 human-reported (45.9%) than for instrument- GHCN peak daily wind gusts associated with Storm reported (36.4%) events. Additionally, 71.5% of Data reports that originated from a human source. human-reported events with a description contain words such as ‘‘tree(s),’’ ‘‘vehicle(s),’’ ‘‘thrown,’’ ‘‘blown,’’ ‘‘damage,’’ ‘‘power,’’ etc., whereas only 26.0% 1 Human sources include categories such as trained spotter, law of event descriptions from automated stations contain enforcement, emergency manager, public, fire department/rescue, such language. For automated station events, many utility company, etc.

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC 456 WEATHER AND FORECASTING VOLUME 31

peak gust results and relate them to previous re- search. Figure 3 depicts the distribution of GHCN peak gusts associated with human-reported non- convective wind events. Without contributions from threshold-driven events, the frequency spike at 57.5– 2 2 59.9 mi h 1 (25.7–26.8 m s 1) vanishes, and a clear 2 maximum is evident between 42.5 and 44.9 mi h 1 2 (19.0–20.1 m s 1). This feature draws immediate at- tention as a possible meaningful human wind sensi- tivity threshold. At first glance, this spike seems 2 2 to resemble the 58 mi h 1 (25.9 m s 1) policy-driven relative maximum in Fig. 1b, but the spike from the 2 2 low to mid-40 mi h 1 (17.9 m s 1)rangefallsinabin 2 2 less than the 46 mi h 1 (20.6 m s 1) gust criterion for wind advisories. Thus, it is not readily apparent how thisrangeofgustsmightbeanartifactofNWSpolicy. FIG. 3. GHCN gusts for the 4271 Storm Data events originating from human sources. Additionally, the maximum is not a simple reflection of the basic wind gust climatology (see section 4b). Without any justification for discrediting the 42.5– Figure 2 provides a schematic of the workflow used to 2 2 44.9 mi h 1 (19.0–20.1 m s 1) feature, it is assumed arrive at the final dataset. We now examine the results that this represents a physically meaningful maximum from this dataset. of nonconvective wind impact. The events used to create Fig. 3 canalsobeexam- 4. GHCN peak gust results and discussion ined spatially by plotting them according to the NWS county warning area (CWA) in which they occurred. a. Identification of hazardous nonconvective Figure 4 shows that the human-reported non- wind speeds convective wind events are concentrated in the Before any comparisons to NWS thresholds can be northeastern United States. Meanwhile, the central made, it is important to thoroughly justify the GHCN United States is characterized by a much smaller

FIG. 4. Map of human-reported nonconvective wind events from Storm Data between 2006 and 2013, coincident with the wind product issuance maps in Fig. 7 and wind climatologies in Fig. 8. Points were randomly placed within the NWS CWA boundary of the WFO that filed the report. Humans report the greatest number of nonconvective wind impacts in forested regions east of the Mississippi River.

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC APRIL 2016 F O R E C A S T E R S ’ F O R U M 457

FIG. 5. Distribution of maximum daily GHCN gusts for (a) directly fatal human-sourced Storm Data events and (b) events directly associated with an injury.

density of human reports. CWAs in mountainous re- previous research will be the topic of the following gions of the western United States also represent rel- discussion. ative maxima of human reports, but the area of b. Physical reasoning for hazardous wind contiguous, highly impacted CWAs is smaller than in speed distribution the eastern United States. In addition to spatial details, Storm Data also records It is important to theoretically explain the distribution any known injuries or fatalities. Figure 5 illustrates the observed in Fig. 3 so that it can be understood why such distribution of peak wind gusts for nonconvective wind seemingly weak wind gusts result in frequently observed events with which Storm Data documented a direct impacts. It is hypothesized that the range of gusts be- 2 2 fatality or injury. While it could be reasoned that wind tween 40 and 55 mi h 1 (17.9–24.6 m s 1), where the speeds capable of killing or injuring humans must be frequency of Storm Data events is greatest, represents a substantially stronger than the maximum from the low middle ground between climatological frequency and 2 2 to mid-40 mi h 1 (17.9 m s 1)rangeinFig. 3, this is not the damaging force exerted by the wind. Since weaker the case. Figure 5a depicts a relative maximum of fatal wind gusts are more common climatologically (Fig. 6a), 2 2 events between 30 and 34.9 mi h 1 (13.4–15.6 m s 1), their greater frequency increases their likelihood of and Fig. 5b exhibits a secondary injury-causing event occasionally producing damage, even if minor. Mean- 2 2 maximum in the 40–44.9 mi h 1 (17.9–20.1 m s 1) while, the force generated by the wind against a surface range. Both the fatal and injury-causing distributions can be proved proportional to the square of the wind 2 exhibit their absolute maxima in the 50–54.9 mi h 1 speed based on Newtonian force balances. As the gust 2 (22.3–24.6 m s 1) bin. Though the distributions of di- speed increases, the climatological frequency decreases 2 rectly fatal and injury-causing events might be mar- [following a maximum between 15 and 20 mi h 1 (6.7– 2 ginally shifted toward stronger wind gusts from Fig. 3,it 8.9 m s 1); see Fig. 6a], but the force increases quadrat- is apparent that significant bodily harm regularly re- ically. Presumably, there is a range of gust speeds that sults from nonconvective gust speeds that do not re- represents the most problematic coincidence of fre- quire the issuance of warnings. A theoretical quency and force. We can show this idea with a simple justification for this finding and its precedent in formula:

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC 458 WEATHER AND FORECASTING VOLUME 31

FIG. 6. Illustrations of the hypothetical impact calculation for (a) GHCN maximum daily gusts and (b) GHCN daily average wind. From top to bottom, each panel depicts the fre- quency curve of the 18-yr GHCN wind climatology, the force function, the hypothetical impact distribution resulting from Eq. (1), and the observed distribution from Storm Data events. For simplicity, the y axes are labeled qualitatively since section 4b is only concerned with the shapes of the curves and their positions along the x axes.

} 3 FrequencyStorm Data Frequencyclimatology Forcewind . used to represent the wind gust climatology curve while (1) the gust’s force can be represented by a simple quadratic curve. Multiplying the two curves yields a hypothetical Testing this basic conceptual model is straightfor- Storm Data frequency distribution. Figure 6a depicts ward. The entirety of the GHCN-Daily dataset can be the two factors in Eq. (1), the product curve, and the

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC APRIL 2016 F O R E C A S T E R S ’ F O R U M 459 observed Storm Data frequency curve for comparison.2 without suffering damage. In natural systems, trees The width of the hypothesized distribution corresponds demonstrate a multitude of mechanisms for surviving well to the observed Storm Data frequency curve. substantial wind loads [for a thorough discussion refer to However, the shapes of the distributions are not par- Read and Stokes (2006)], but when exposed to winds ticularly similar, and the hypothetical distribution is outside of their common climatological range, cata- 2 2 shifted roughly 10 mi h 1 (4.5 m s 1) toward weaker strophic damage can occur (Bailey et al. 1997, p. 269). wind speeds. Therefore, human and natural mitigation measures Another consideration not yet addressed is that non- might shift the observed impact curve toward stronger convective wind events are usually accompanied by wind speeds compared to the hypothetical curve. prolonged periods of sustained winds in addition to The results shown here are supported by previous gusts. If the sustained winds primed the environment for research. Several forestry studies have observed winds 2 2 impact and the gusts are simply ‘‘the straw that broke below 40 mi h 1 (17.9 m s 1) to cause windthrow (the the camel’s back,’’ then Fig. 3 might also be partially breaking or uprooting of trees by the wind) of healthy reflective of the daily average winds accompanying the trees (Oliver and Mayhead 1974; Francis and Gillespie gusts. Thus, the same procedure described above was 1993; Gardiner et al. 2000; Peltola 2006). Oliver and repeated for the GHCN-Daily average wind between Mayhead (1974) found that during windstorms, wind- 2 2 0 and 40 mi h 1 (0–17.9 m s 1; Fig. 6b). (Of the 4271 throw occurred at speeds well below what was calculated Storm Data events that were paired with GHCN gusts, for tree failure based on winching studies performed in 4212 also recorded the average wind.) The shape and the same forest. Anecdotally, a catastrophic April 2014 width of the average wind hypothetical distribution and windthrow event in Athens, Georgia, described by a Storm Data event distribution are almost identical. local arborist as a ‘‘once in a lifetime’’ occurrence However, although the bulk of the distributions align (J. Ritzler, New Urban Forestry, Athens, Georgia, 2015, much more closely, the hypothetical curve is still shifted personal communication) resulted from maximum gusts 2 2 toward slightly weaker wind speeds than the observed of 44 mi h 1 (19.7 m s 1) accompanied by sustained 2 2 distribution. The conceptual model was also computed winds greater than 30 mi h 1 (13.4 m s 1) for a period of on a regional scale, and approximately the same offset several hours. was observed in all regions. Several explanations for tree failure at these wind speeds can be postulated. One is that the vibrations of a c. Performance of the conceptual model and its tree exposed to unusually strong sustained winds for an precedent in previous research extended period of time can loosen the soil around the Several possibilities could account for the disagree- root system leading to failure at weaker-than-expected ment between the hypothesized distributions and the wind speeds (Oliver and Mayhead 1974). This also Storm Data observed distributions. For instance, the supports why the distribution of the hypothetical non- Storm Data event distribution is composed of GHCN convective wind impact more closely resembles the ob- maximum daily gusts, and it cannot be known whether served wind impact when considering GHCN average the maximum gust prompted the Storm Data report. daily wind. In addition, the 2014 Athens event occurred Also, as referenced above, the combination of the sus- at the beginning of the spring ‘‘greening’’ period when tained winds and wind gusts may be more significant the leaf area of, and consequently the wind loading ex- than the gusts alone. This possibility is supported by the perienced by, the region’s trees was rapidly increasing. closer model alignment using the daily average wind A period of rain earlier that morning also served to distributions rather than the maximum gust distribu- moisten and loosen area soils. Circumstantial factors tions. Finally, Eq. (1) is an intuitive, first-order expla- such as these may be influential in facilitating tree failure nation for nonconvective wind impact, and ignores key at weaker-than-expected wind speeds. considerations like mitigation and adaptive measures by both human and natural systems. For instance, struc- 5. Comparison to current NWS product criteria tural engineers design buildings to withstand the force exerted by climatologically frequent wind speeds As described in the introduction, the lack of wind product threshold documentation raises concerns over the NWS requirements for the products’ issuance. Most troubling is that the majority of deadly, injury-causing, 2 Since the purpose of this exercise is to propose a first-order explanation for the location and shape of the Storm Data frequency and human-reported events occur with peak gusts below distribution, the actual values and units resulting from the curve the current high wind warning gust criterion (Table 3). multiplication are not relevant. Greater than one-quarter of all directly fatal and

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC 460 WEATHER AND FORECASTING VOLUME 31

TABLE 3. Percentages of human source Storm Data events with peak gusts weaker than the NWS wind product gust criteria.

Percent weaker than wind advisory Percent weaker than high wind warning All Storm Data events 44.4 91.8 Storm Data events with injury 32.0 83.3 Storm Data events with death 28.2 74.4 Climatological percentile of the lower threshold 97.9 99.5 directly injury-causing nonconvective wind events wind advisory gust criterion is as common as the 89th (within the 4271 human source events considered here) percentile. These mountainous regions with more fre- occurred below the wind advisory gust threshold, and quent strong gusts correspond well to the wind product greater than 74% occurred below the high wind warning issuance maxima depicted in Fig. 7. However, for much 2 2 gust criteria (Table 3). of the eastern United States a 46 mi h 1 (20.6 m s 1) gust An archive of NWS wind advisories and high wind falls within the upper 2% of all gusts observed in the warnings obtained from the Iowa Mesonet data CWAs (including convective and tropical winds). portal (https://mesonet.agron.iastate.edu/request/ Meanwhile, the majority of percentiles for the high wind gis/watchwarn.phtml) was used to create maps of wind warning gust criterion exceed the rarest 1% of all gusts. 2 2 product issuance across the continental United States Thirteen CWAs witnessed 58 mi h 1 (25.9 m s 1) gusts between 2006 and 2013 (Fig. 7). While NWS directives or stronger once every .2.5 yr while two CWAs allow for individual Weather Forecast Offices (WFOs) (Charleston, South Carolina, and Jackson, Kentucky) to adjust thresholds based on local climatology, Fig. 7 observed these gusts only once every .5 yr (including demonstrates that wind product issuance is still driven convective and tropical). It is clear that the high wind largely by regional wind climatologies. Areas of com- warning threshold represents a climatologically extreme plex terrain in the western United States receive the event for most CWAs in the eastern United States. majority of high wind warnings issued by the NWS, and Additionally, when this threshold is met, it is not nec- some WFOs in the Rocky Mountains region choose not essarily even as a result of nonconvective winds. The to issue wind advisories. In contrast, many WFOs in the true climatological frequency for nonconvective winds eastern United States commonly issue wind advisories, of this magnitude is assumed to be more infrequent than but did not issue a high wind warning, and many others indicated in Fig. 8. issued only one or two during this 7-yr period. Though With their current gust definitions, wind advisories these CWAs suffer losses from nonconvective wind and high wind warnings fail to encompass a significant events (Fig. 4), the current NWS high wind warning number of events associated with nonconvective wind definition fails to capture the wind speeds that generate damage. Figure 8 demonstrates that regional wind cli- damage in these areas. Ideally, the dots in Fig. 4 and matologies vary significantly within the United States, the dark gray/black regions in Fig. 7 would be spatially and should be incorporated to at least some degree in coincident, meaning that the most wind products were the development of nonconvective wind product gust issued in the areas that experienced the most non- thresholds. The circumstantial factors (e.g., soil mois- convective high wind losses; however, this is not the ture and tree leaf area) discussed in section 4c also de- case. The spatial displacement of the areas of frequent serve further consideration in the establishment of high wind warning issuance from the areas of non- revised issuance criteria. However, if a standard national convective wind impact suggests that the current warn- threshold is desired, more empirically justifiable gust 2 2 ing criteria prohibits high wind warning issuance in areas criteria might be 35 and 47 mi h 1 (15.6 and 21.0 m s 1) of frequent human impact. for the wind advisory and high wind warning, re- Although the current gust criteria can be perceived to spectively. These thresholds would capture the majority be reasonable values, they are actually climatologically of the nonconvective wind events while still occurring exceptional events. Figure 8 shows a spatial depiction of infrequently enough to avoid overissuance (Table 4). the climatological percentiles of both the wind advisory However, modifying the wind advisory and high wind (Fig. 8a) and high wind warning gust criteria (Fig. 8b). warning issuance criteria could potentially affect the Since the purpose of Fig. 8 is to contextualize the ab- way in which the general public perceives these alerts. In solute climatological frequency of the wind gusts used to decreasing the threshold, the number of wind products issue these products, no effort was made to filter out issued annually would increase, possibly influencing the gusts associated with convective or tropical systems. In public’s decision to take appropriate protective mea- the high elevations of the western United States, the sures. Prior to understanding the consequences of

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC APRIL 2016 F O R E C A S T E R S ’ F O R U M 461

FIG. 7. Map of the number of (a) wind advisories and (b) high wind warnings issued by the NWS between 2006 and 2013. Boundaries delineate NWS CWAs.

2 2 threshold modification, we must first understand how (28 mi h 1) and a high wind warning (38 mi h 1) com- the public currently perceives these products as a whole. pared to their local WFO’s thresholds. Based on these A forthcoming study by Williams et al. (2016, manu- results, the public could find a reduced threshold more script submitted to Wea. Forecasting) reveals that reasonable. weather-salient participants generally associated sub- An increase in product issuance would potentially be stantially weaker wind speeds with both a wind advisory accompanied by an increase in the false alarm ratio

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC 462 WEATHER AND FORECASTING VOLUME 31

FIG. 8. Map of the climatological percentile of the gust criterion for (a) wind advisories and (b) high wind warnings by NWS CWAs. Percentiles were derived using GHCN-Daily wind gusts for the same 8-yr period for which the wind products in Fig. 7 were available. Ideally, the areas of concentrated black dots in Fig. 4 and the regions of dark gray/black in Figs. 7 and 8 would be relatively coincident, indicating that warnings were issued most frequently in areas where wind impacts were experienced most frequently.

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC APRIL 2016 F O R E C A S T E R S ’ F O R U M 463

TABLE 4. Percentages of human-reported Storm Data events with peak gusts weaker than the revised wind advisory and high wind 2 2 2 2 warning gust criteria suggested by this study [35–46 mi h 1 (15.6–20.6 m s 1) and $47 mi h 1 ($21.0 m s 1), respectively] (cf. with Table 3).

Percent weaker than the Percent weaker than the proposed proposed wind advisory range high wind warning threshold All Storm Data events 10.5 55.9 Events with injury 9.0 41.0 Events with death 17.9 41.0 Climatological percentile of the lower threshold 89.0 98.1

(FAR) associated with wind products. This leads to the criteria is unclear. In this study, Storm Data was used to question, Will an increase in nonconvective wind prod- identify the times and locations of significant non- uct FAR affect the behaviors and intentions of the convective wind events from 1996 to 2013. Daily maxi- public to take protective actions for wind events? The mum wind gusts and daily average wind speeds from distinction between the actual calculated FAR and an GHCN stations were then paired with Storm Data re- individual’s perceived FAR (the frequency at which an ports based on the spatial and temporal characteristics individual perceives a false alarm to occur in their local of the nonconvective wind event. The synoptic scale and area) is critical to answering this question. Previous re- relatively large spatial autocorrelation of nonconvective search has revealed that, compared to the actual FAR wind fields (compared to convective winds) permits this produced by the NWS, the perceived FAR is under- substitution with reasonable confidence. Additionally, estimated by the average respondent (Ripberger et al. Storm Data events were narrowed to include only those 2015). Similarly, Trainor et al. (2015) concluded that the events whose reports originated from human sources in perceived FAR is not influenced by the actual FAR. an attempt to limit the artificial, policy-driven reporting However, the relationship between the actual FAR and pattern resulting from the NWS definition for ‘‘high an individual’s likelihood of taking protection action is winds.’’ still unclear (Dow and Cutter 1998; Benight et al. 2004; Nearly 92% (45%) of all Storm Data nonconvective LeClerc and Joslyn 2015; Ripberger et al. 2015; Trainor wind reports occurred with GHCN peak gusts weaker et al. 2015). While the most recent literature suggests than the high wind warning (wind advisory) criteria. that increasing the actual FAR negatively impacts an Unfortunately, statistics for events associated with in- individual’s likelihood of taking protective action, this jury and death are not reassuring: greater than 28% of research has been largely limited to convective hazards. all injury-causing and fatal events occur below the wind Since it is unclear how well these results translate to advisory threshold, and greater than 74% of all such nonconvective wind events, it is difficult to fully antici- events occur below the high wind warning threshold. pate the societal response to reducing the wind product In light of this analysis, it appears that revised gust cri- issuance thresholds. Though recent work has sought to teria for NWS wind product issuance are needed. 2 compute actual nonconvective wind product FARs Though gust criteria recommendations of 35–46 mi h 1 2 2 (Layer and Colle 2015), future research should attempt (15.6–20.6 m s 1) for wind advisories and $47 mi h 1 2 to determine how the perceived FAR is evaluated for (21 m s 1) for high wind warnings are suggested, com- high wind events. This would help determine the cir- municating weather hazards to the public is an ex- cumstances, if any, when a wind event would be labeled tremely complex process, and any amendments to as a ‘‘false alarm’’ by the general public and how such a advisory/warning criteria deserve additional research in perception would impact their likelihood of taking the future. Specifically, an investigation of the circum- protective action in the future. stances surrounding the impact [e.g., overturned vehi- cles, fallen trees, etc., as in Ashley and Black (2008)]asa function of wind speed could help inform any non- 6. Conclusions convective wind product revisions. Recommended ave- The societal impacts of nonconvective high winds in nues for future research also include the duration of the United States are infrequently acknowledged in the sustained winds on resulting nonconvective wind dam- American research literature or media outlets. While age, and how sustained winds should best be in- the NWS does issue several products designed to com- corporated into any revised issuance criteria. municate the potential for dangerous nonconvective Though severe convective storms and tropical cy- winds to the public, such as wind advisories and high clones might generate more powerful winds, abundant wind warnings, the origin/justification for the issuance research has focused on these systems, their impacts,

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC 464 WEATHER AND FORECASTING VOLUME 31 and on reducing losses from these hazards. Non- of cold-season nonconvective wind events in the Great Lakes convective winds continue to result in damage to life and region. J. Climate, 20, 6012–6022, doi:10.1175/2007JCLI1750.1. property, while receiving considerably less thought and Layer, M., and B. A. Colle, 2015: Climatology and ensemble pre- dictions of nonconvective high wind events in the New York attention. It is hoped that this research will be the first City metropolitan region. Wea. Forecasting, 30, 270–294, step to improving the warning process for nonconvective doi:10.1175/WAF-D-14-00057.1. wind, and will lead to a reduction in the loss of life and LeClerc, J., and S. Joslyn, 2015: The cry wolf effect and weather- property to these winds. related decision making. Risk Anal., 35, 385–395, doi:10.1111/ risa.12336. López, R. E., R. L. Holle, T. A. Heitkamp, M. Boyson, Acknowledgments. The authors thank Jared Rackley, M. Cherington, and K. Langford, 1993: The underreporting of Kelly Harris, Lynne Seymour, Lauren Anderson, John lightning injuries and deaths in Colorado. Bull. Amer. Meteor. Ritzler, and two anonymous reviewers for their com- Soc., 74, 2171–2178, doi:10.1175/1520-0477(1993)074,2171: ments in the early stages of this manuscript’s composi- TUOLIA.2.0.CO;2. tion. CAW acknowledges the support of a National ——, ——, and ——, 1995: Lightning casualties and property damage in Colorado from 1950 to 1991 based on Storm Data. Wea. Science Foundation Graduate Research Fellowship in Forecasting, 10, 114–126, doi:10.1175/1520-0434(1995)010,0114: the completion of this work (Grant DGE-1443117). Any LCAPDI.2.0.CO;2. opinions, findings, conclusions, or recommendations Menne, M. J., I. Durre, R. S. Vose, B. E. Gleason, and T. G. expressed in this manuscript are those of the authors and Houston, 2012a: An overview of the Global Historical Cli- do not necessarily reflect the views of the National Sci- matology Network-Daily database. J. Atmos. Oceanic Tech- nol., , 897–910, doi:10.1175/JTECH-D-11-00103.1. ence Foundation. 29 ——, and Coauthors, 2012b: Global Historical Climatology Network-Daily (GHCN-Daily), version 3.20. NOAA/National REFERENCES Climatic Data Center, accessed 15 March 2015, doi:10.7289/ V5D21VHZ. Ashley, W. S., and A. W. Black, 2008: Fatalities associated with National Centers for Environmental Information, 2015: Storm nonconvective high-wind events in the United States. J. Appl. Events Database. Accessed 22 February 2015. [Available Meteor. Climatol., 47, 717–725, doi:10.1175/2007JAMC1689.1. online at http://www.ncdc.noaa.gov/stormevents/.] ——, and C. W. Gilson, 2009: A reassessment of U.S. lightning National Oceanic and Atmospheric Administration, 2007: Storm mortality. Bull. Amer. Meteor. Soc., 90, 1501–1518, doi:10.1175/ data preparation. National Weather Service Instruction 10- 2009BAMS2765.1. 1605, 97 pp. [Available online at http://www.ncdc.noaa.gov/ Bailey, W. G., T. R. Oke, and W. Rouse, 1997: Surface Climates of stormevents/pd01016005curr.pdf.] Canada. McGill-Queen’s University Press, 369 pp. National Weather Service, 2015a: National Weather Service Glos- Benight,C.,E.Gruntfest,andK.Sparks,2004:Coloradowild- sary. [Available online at http://w1.weather.gov/glossary/.] fires 2002. Quick Response Research Rep. 167, Natural ——, 2015b: Non-precipitation weather hazards. NWS Operations Hazards Center and University of Colorado, 9 pp. [Avail- Manual 92-6, W/OM11. [Available online at http://www.nws. able online at http://www.colorado.edu/hazards/research/qr/ noaa.gov/wsom/manual/archives/NC449206.HTML#d4-1.] qr167/qr167_mod.pdf.] Oliver, H., and G. Mayhead, 1974: Wind measurements in a pine Black, A. W., and W. S. Ashley, 2010: Nontornadic convective wind forest during a destructive gale. Forestry, 47, 185–194, fatalities in the United States. Nat. Hazards, 54, 355–366, doi:10.1093/forestry/47.2.185. doi:10.1007/s11069-009-9472-2. Peltola, H. M., 2006: Mechanical stability of trees under static ——, and T. Mote, 2015: Characteristics of winter-precipitation- loads. Amer. J. Bot., 93, 1501–1511, doi:10.3732/ajb.93.10.1501. related transportation fatalities in the United States. Wea. Pryor, S. C., R. Conrick, C. Miller, J. Tytell, and R. J. Barthelmie, Climate Soc., 7, 133–145, doi:10.1175/WCAS-D-14-00011.1. 2014: Intense and extreme wind speeds observed by anemometer Dow, K., and S. L. Cutter, 1998: Crying wolf: Repeat responses to and seismic networks: An eastern U.S. case study. J. Appl. Me- hurricane evacuation orders. Coastal Manage., 26, 237–252, teor. Climatol., 53, 2417–2429, doi:10.1175/JAMC-D-14-0091.1. doi:10.1080/08920759809362356. Read, J., and A. Stokes, 2006: Plant biomechanics in an ecological Francis, J. K., and A. J. Gillespie, 1993: Relating gust speed to tree context. Amer. J. Bot., 93, 1546–1565, doi:10.3732/ajb.93.10.1546. damage in Hurricane Hugo, 1989. J. Arboric., 19, 368–373. Ripberger, J. T., C. L. Silva, H. C. Jenkins-Smith, D. E. Carlson, Galway, J. G., 1989: The evolution of severe thunderstorm criteria M. James, and K. G. Herron, 2015: False alarms and missed within the Weather Service. Wea. Forecasting, 4, 585–592, events: The impact and origins of perceived inaccuracy in doi:10.1175/1520-0434(1989)004,0585:TEOSTC.2.0.CO;2. warning systems. Risk Anal., 35, 44–56, doi:10.1111/ Gardiner, B., H. Peltola, and S. Kellomäki, 2000: Comparison of risa.12262. two models for predicting the critical wind speeds required to Schwartz, R. M., and T. W. Schmidlin, 2002: Climatology of damage coniferous trees. Ecol. Modell., 129, 1–23, doi:10.1016/ in the conterminous United States, 1959–2000. J. Climate, S0304-3800(00)00220-9. 15, 1765–1772, doi:10.1175/1520-0442(2002)015,1765: Knox,J.A.,J.D.Frye,J.D.Durkee,andC.M.Fuhrmann, COBITC.2.0.CO;2. 2011: Non-convective high winds associated with extra- Trainor, J. E., D. Nagele, B. Philips, and B. Scott, 2015: Tornadoes, tropical cyclones. Geogr. Compass, 5, 63–89, doi:10.1111/ social science, and the false alarm effect. Wea. Climate Soc., 7, j.1749-8198.2010.00395.x. 333–352, doi:10.1175/WCAS-D-14-00052.1. Lacke,M.C.,J.A.Knox,J.D.Frye,A.E.Stewart,J.D.Durkee, Trapp, R. J., D. M. Wheatley, N. T. Atkins, R. W. Przybylinski, C. M. Fuhrmann, and S. M. Dillingham, 2007: A climatology and R. Wolf, 2006: Buyer beware: Some words of caution on

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC APRIL 2016 F O R E C A S T E R S ’ F O R U M 465

the use of severe wind reports in postevent assessment detection algorithm for the WSR-88D. Wea. Forecast- and research. Wea. Forecasting, 21, 408–415, doi:10.1175/ ing, 13, 286–303, doi:10.1175/1520-0434(1998)013,0286: WAF925.1. AEHDAF.2.0.CO;2. Williams, E., and Coauthors, 1999: The behavior of total lightning ——, ——, ——, E. D. W. Mitchell, J. Johnson, and K. W. activity in severe Florida thunderstorms. Atmos. Res., 51, 245– Thomas, 1998b: Evaluating the performance of WSR- 265, doi:10.1016/S0169-8095(99)00011-3. 88D severe storm detection algorithms. Wea. Forecast- Witt,A.,M.D.Eilts,G.J.Stumpf,J.T.Johnson,E.D.W. ing, 13, 513–518, doi:10.1175/1520-0434(1998)013,0513: Mitchell, and K. W. Thomas, 1998a: An enhanced hail ETPOWS.2.0.CO;2.

Unauthenticated | Downloaded 09/26/21 03:57 AM UTC