Quantitative Assessment of Human Wind Speed Overestimation

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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 4 September 2015, in ﬁnal form 10 February 2016)

ABSTRACT

Human wind reports are a vital supplement to the relatively sparse network of automated weather stations in the United States, especially for localized convective winds. In this study, human wind estimates recorded in Storm Data between 1996 and 2013 were compared with instrumentally observed wind speeds from the Global Historical Climatology Network (GHCN). Nonconvective wind events in areas of flat terrain within the continental United States served as the basis for this analysis because of the relative spatial homogeneity of wind fields in these meteorological and geographic settings. The distribution of 6801 GHCN-measured gust factors (GF), defined here as the ratio of the daily maximum gust to the daily average wind, provided the reference upon which human gust reports were judged. GFs were also calculated for each human estimate by dividing the estimated gust by the GHCN average wind speed on that day. Human-reported GFs were dis- proportionately located in the upper tail of the observed GF distribution, suggesting that humans demonstrate a tendency to report statistically improbable wind gusts. As a general rule of thumb, humans overestimated nonconvective wind GFs by approximately one-third.

1. Introduction wind, marine hazards, dust storms, dense fog, and any directly weather-related injuries or fatalities (NWS 2011). The National Weather Service (NWS) issues several Beyond trained spotters, professionals serving the public types of watches, warnings, and advisories to alert the such as emergency managers and law enforcement also public during hazardous weather events. In noncon- report noteworthy weather events to the NWS. vective and convective events, operational meteorolo- While valuable, these human reports are not without gists routinely consult human observers to corroborate error. In relaying wind events, spotters are asked to re- other data for improving warning accuracy, timeliness, port estimated or measured wind speed and any ob- and credibility (McCarthy 2002). Organized storm spot- served damage. Since ﬁeld observers typically lack ter networks began during World War II with the goal of instrumentation, most of their reported wind speeds are providing advanced warning of hazardous weather to estimated. For example, 99% of Storm Data’s fatal military installations (Doswell et al. 1999). Since 1971, the thunderstorm wind events that provided speed infor- SKYWARN program has trained an estimated 29 000 mation offered an estimate rather than a measured value volunteers (Klenow and Reibestein 2014) to report se- (Black and Ashley 2011). Compounding the problem, vere convective weather, winter weather, nonconvective estimation of wind speed is very difﬁcult (Weiss et al. 2002; Trapp et al. 2006), with the main challenge attributed to humans’ lack of experience with high winds Corresponding author address: Paul Miller, Dept. of Geography, The University of Georgia, Rm. 204, 210 Field St., Athens, GA (Doswell et al. 2005). 30602. While several studies mention the tendency for humans E-mail: [email protected] to overestimate wind speeds (Doswell et al. 2005; Smith

DOI: 10.1175/JAMC-D-15-0259.1

Ó 2016 American Meteorological Society Unauthenticated | Downloaded 09/27/21 02:48 PM UTC 1010 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 55 et al. 2013), none have tested this assumption or quanti- TABLE 1. GHCN-Daily wind quantities, their abbreviations, and fied the magnitude of the overestimation. The goal of this dimensions. study is to determine how nonconvective wind gust esti- Quantity Abbreviation Dimension mates from humans compare to actual observed gusts 2 Avg daily wind speed AWND 1/10 m s 1 from Global Historical Climatology Network (GHCN) Direction of fastest 1-min wind WDF1 8 stations. In short, do humans actually overestimate wind Direction of fastest 2-min wind WDF2 8 gusts, and if so, by what magnitude? The comparison is Direction of fastest 5-s wind WDF5 8 facilitated by calculating the gust factor (e.g., Durst 1960; Direction of peak wind gust WDFG 8 8 Davis and Newstein 1968) for each Storm Data report Direction of highest WDFI instantaneous wind and for a nearby reference GHCN station. This analysis Fastest mile wind direction WDFM 8 focuses on nonconvective winds as they are typically 24-h wind movement WDMV km 2 driven by synoptic-scale processes (Knox et al. 2011)and Fastest 1-min wind speed WSF1 1/10 m s 1 2 Fastest 2-min wind speed WSF2 1/10 m s 1 are generally homogeneous over a large spatial domain 2 Fastest 5-s wind speed WSF5 1/10 m s 1 (Pryor et al. 2014). Given the crucial role of human ob- 2 Peak gust wind speed WSFG 1/10 m s 1 2 servations in the warning and verification process of Highest instantaneous wind speed WSFI 1/10 m s 1 2 hazardous weather, it is critical to understand the biases Fastest mile wind speed WSFM 1/10 m s 1 in human-reported wind gusts. Section 2 will describe the data sources utilized in this study, and section 3 will detail the methods used to complete the analysis. Subsequently, compare with Storm Data’s human wind reports. GHCN section 4 will present the results, and section 5 will discuss stations that measure wind can provide the data in several the implications of the findings. formats (Table 1); however, only the AWND, WSF1, WSF2, WSF5, WSFG, and WSFI measurements were 2. Data relevant for this study. GHCN data are quality controlled by NCDC (Menne et al. 2012a) and provide a reliable Two datasets were used extensively in this analysis: standard by which human estimates can be judged. Com- Storm Data and the GHCN-Daily dataset. Storm Data,a pared to convective gusts, nonconvective winds are typi- resource published by the National Climatic Data Center cally generated by synoptic-scale processes and occur (NCDC, now known as the National Centers for Envi- with similar intensity over a large area (Pryor et al. 2014). ronmental Information), catalogs many types of signifi- Given the spatial autocorrelation between nonconvective cant weather across the United States. Nonconvective AWND and maximum gusts, GHCN stations provide a wind events were collected from Storm Data for the pe- meaningful baseline by which to judge the likelihood of riod 1996–2013, based on the availability of Storm Data Storm Data nonconvective wind reports. in digital form starting in 1996. Storm Data has been ap- ó plied in the study of fatal lightning strikes (L pez et al. 3. Methods 1995; Ashley and Gilson 2009), blizzard climatologies (Schwartz and Schmidlin 2002), and nonconvective wind A paired database of GHCN wind speeds and Storm fatalities (Ashley and Black 2008), although it is perhaps Data events was compiled using the methods outlined by most commonly used in studies of severe convective Miller et al. (2016), who employed GHCN wind mea- weather hazards (e.g., Ashley 2007; Black and Ashley surements to identify nonconvective wind speeds asso- 2010). However, this dataset has received criticism for ciated with human-reported events in Storm Data.To spatial and temporal discrepancies of reports (e.g., Witt create this database, the date, time, and NWS forecast et al. 1998a,b; Williams et al. 1999; Trapp et al. 2006), zone associated with each Storm Data entry were used to underreporting of fatalities (López et al. 1993; Black and pair the event to a wind-observing GHCN station within Mote 2015), and irregularities in the preparation process the same NWS forecast zone. Finer-scale geographic (Gall et al. 2009). Storm Data reports can originate from information (i.e., latitude and longitude) commonly in- human sources such as law enforcement and the general cluded with convective wind reports is not provided for public or from automated meteorological stations that nonconvective wind events in Storm Data. In the cases observe weather conditions meeting or exceeding estab- where there was not a GHCN station within the NWS lished criteria. Although the NWS attempts to use the forecast zone, the event was discarded. Whenever GHCN most accurate information available, the quality of the measurements coincided temporally and spatially with reports is not guaranteed (NCDC 2013). Storm Data reports, the maximum daily wind gust and Daily wind observations were retrieved from the AWND from the GHCN station were paired to the Storm NCDC’s GHCN-Daily dataset (Menne et al. 2012b)to Data event, and the resulting dataset was analyzed.

Unauthenticated | Downloaded 09/27/21 02:48 PM UTC APRIL 2016 M I L L E R E T A L . 1011 a. Establishing a standard of comparison that encompass only part of a day. Whenever multiple stations were located within an NWS zone and/or mul- For each event in the dataset, a gust factor (GF) was tiple days were encompassed by the Storm Data event, a computed by dividing the maximum daily wind mea- mean GF was calculated using all the relevant GFs. surement by the daily average wind. The GF Very few Storm Data entries before 2003 record contextualizes a gust in terms of the average wind con- whether the report reflects a wind gust or a sustained ditions over a longer period of time (e.g., Ishizaki 1983; wind. Any reports explicitly referring to ‘‘sustained Krayer and Marshall 1992), although the length of this winds’’ were removed from consideration since their period can vary by GF definition. Our decision to use the ‘‘GFs’’ would be smaller than events reflecting gusts. daily average wind speed was partially driven by the Any unspecified reports (i.e., no ‘‘gust’’ or ‘‘sustained’’ choice of quantities in Table 1, and partially by this indicator) were assumed to represent gusts. Since during value’s successful application in a previous gust likeli- the era of consistent labeling (i.e., after 2003) fewer than hood study (Weggel 1999). Equation (1) shows the GF 15% of nonconvective wind reports referenced sus- calculation with the MAX function indicating the largest tained winds, this assumption is reasonable and prevents of the available values within the parentheses served as the exclusion of 6029 events. Of the original 63 302 the dividend: Storm Data events, GHCN GFs could be calculated for 15 493. This substantial decrease was almost entirely 5 GF MAX(WSF1, WSF2, WSF5, dictated by the locations of GHCN stations that re- WSFG, WSFI)/AWND. (1) corded the necessary wind measurements. b. Determining the likelihood of Storm Data wind A day’s AWND is key to assessing the likelihood of a reports Storm Data gust report. For instance, a reported non- 2 2 convective wind gust of 75 mi h 1 (33.5 m s 1) might be The advantage of considering nonconvective wind 2 considered more likely if the AWND was 40 mi h 1 events is the relatively homogeneous wind field resulting 2 2 2 2 (17.9 m s 1)[i.e.,75mih1 (33.5 m s 1)/40 mi h 1 from the synoptic-scale pressure gradient. GFs for Storm 2 (17.9 m s 1) 5 a GF of 1.9], but less likely if the AWND Data entries were calculated by dividing the reported 2 2 2 2 were only 15 mi h 1 (6.7 m s 1)[i.e.,75mih 1 (33.5 m s 1)/ ‘‘magnitude’’ value by the GHCN AWND measured 2 2 15 mi h 1 (6.7 m s 1) 5 a GF of 5.0]. Larger GFs represent during the same time and in the same NWS forecast more exceptional wind gusts given the mean wind speed zone. The relative spatial uniformity of nonconvective for that day. GFs are also advantageous for the purpose wind fields allows the GHCN AWND to be applied to of establishing the magnitude of overestimation, if any. the nearby Storm Data report with reasonable confi- By scaling a gust according to the AWND, the GF ex- dence. In summary, each Storm Data entry identified an presses how unusual the gust may have seemed to a event leading to the calculation of two GFs: one com- human observer relative to the background wind. GFs puted solely from GHCN wind measurements and an- therefore capture a cognitive, experiential component other from the Storm Data magnitude and GHCN to wind speed estimation that would go unaddressed if AWND. Prior to 2003, many Storm Data reports did not considering gust speed alone. include a ‘‘magnitude’’ value. These events still con- A GF was calculated for each GHCN station within tributed GHCN GFs to increase the number of non- the NWS forecast zone for each day either partially or convective GF data points, but no Storm Data GFs could entirely encompassed by the Storm Data event. Since be calculated. the AWND is calculated over a longer period than the The distribution of all GHCN GFs offers a standard of typical Storm Data event persisted (mean Storm Data– comparison for human-reported GFs from Storm Data indicated event duration equal to 6.16 h), the AWND (Fig. 1). However, studies have documented increased may be weighted toward misleadingly calmer conditions GFs in areas of complex terrain (Ashcroft 1994; for shorter-lived events. However, the Storm Data– Ágústsson and Ólafsson 2004). This intuitive finding indicated duration and AWND in the final dataset are could potentially disrupt the relative homogeneity of weakly correlated with one another (R2 5 0.021), sug- nonconvective wind fields upon which this study relies. gesting the AWND is not systematically biased by event The intermixing of large GFs resulting from both gen- duration. This result is reasonable given that the period uine terrain influences and inaccurate human judgment of elevated, pressure-gradient-driven winds likely would diminish the power of the statistical analyses to influenced the AWND beyond the most noteworthy discern any human overestimation. Consequently, all hours that were cataloged in Storm Data. Consequently, statistical analyses were restricted to events occurring in no attempt is made to adjust GFs for Storm Data events relatively flat topography where terrain-driven GFs

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p values. An excess of small p-value estimates (i.e., a fraction of p values of ,0.05 that is greater than that observed by automated stations) were considered evidence of systematic human overestimation. 4. Results

The distribution of Storm Data GF p values is slightly skewed toward small values (i.e., large GFs; Fig. 4a). However, this result is not necessarily surprising. Storm Data magnitudes ideally reflect the most exceptional wind gusts occurring during an event, resulting in a systematic bias toward larger GFs (i.e., smaller p values). The ‘‘source’’ field can be used to isolate p values for Storm Data reports attributed to Automated FIG. 1. Distribution of GHCN GFs with contributions from areas of flat terrain shown in dark gray. With terrain influences mini- Surface Observing System (ASOS) and Automated mized, the GF distribution is shifted toward smaller GF values. Not Weather Observing System (AWOS) stations, or official shown are 83 events with GFs greater than 10. NWS observations. These Storm Data entries were measured by sophisticated, calibrated instruments, and were assumed to be minimal. Flat areas were defined as provide a standard by which to assess the plausibility of the ‘‘Interior Plains,’’ ‘‘Laurentian Uplands,’’ and ‘‘Atlantic human-sourced reports. Plain’’ physiographic regions identified by Fenneman (1928; Figure 4b shows the Storm Data GF p value distri- Fig. 2), which are still used in contemporary geography. bution for all ASOS, AWOS, or official NWS observa- Figure 1 illustrates how removing areas of complex ter- tion reports. Instrument-sourced reports demonstrate a rain reduces the proportion of large GHCN GFs and more even distribution of p values than the aggregation shifts the distribution toward smaller GFs. Since local of all reports, but maintain a slight preference for large terrain features (including bodies of water) within flat GFs. Nonetheless, since ASOS anemometers are accu- 2 regions can generate small-scale high-GF-producing cir- rate to within 2 kt (1 kt 5 0.51 m s 1) or 5% (whichever culations, ‘‘flat’’ is used in a relative sense as compared to is greater) for wind speeds below 125 kt (NOAA 1998), more mountainous regions. Fig. 4b sets the standard for how the distribution of re- Flat regions contribute 6801 of the 15 493 events na- liable Storm Data GF p values should appear. Distri- tionwide between 1996 and 2013 for which GHCN GFs butions from Storm Data sources that are more strongly could be calculated. Given the large sample size, a skewed toward small p values than Fig. 4b would in- nonparametric method (e.g., Higgins 2004) was chosen dicate an excess of unlikely gust reports; estimates from for statistical analysis. Each human-estimated GF was these sources should be scrutinized. assigned an empirical p value based on the estimated Figure 4c shows the p value distribution for human- GF’s percentile within the observed GHCN GF distri- sourced1 reports. There is a clear p value maximum in bution. The p value is equal to the fraction of GHCN the bin containing the most infrequent GFs. Roughly GFs greater than or equal to the Storm Data GF being 23% of human wind reports reside in the top 5% of all considered (i.e., one minus the percentile). Each esti- GHCN GFs associated with nonconvective wind events mated GF’s p value, used here in the conceptual sense whereas only 8.5% of ASOS, AWOS, and official-NWS- of the term (Biau et al. 2010), represents the likelihood observation reports fall in this same range. When that an accurately measured GF would equal or exceed human sources are disaggregated and examined by con- the estimated GF in question; smaller p values indicate stituent groups, similar trends are maintained. Figure 5 less-likely GFs. This nonparametric approach is prefera- depicts p value histograms for reports stratified by law ble to a parametric method because it assumes little about the GHCN GF distribution (Higgins 2004) and eliminates potential sources of error. After calculating the p values, 1 ‘‘Human sources’’ include the following groups: 911 call center, the nonparametric requirement is relaxed to perform a airplane pilot, amateur radio, coast guard, COOP observer, county regression analysis. Figure 3 depicts a workflow schematic official, emergency manager, fire department/rescue, meteorolo- of the methodology described in this section, including gist (non NWS), NWS employee, government official, insurance company, law enforcement, mariner, public, social media, storm the curve used to assign Storm Data GF p values. Human chaser, trained spotter, and utility company. Two arguably human estimates will collectively be referred to as ‘‘(un)likely,’’ groups, broadcast media and newspapers, were excluded from the ‘‘(im)probable,’’ or ‘‘(im)plausible’’ based on their human category and treated separately. See text for explanation.

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FIG. 2. NWS zones as assigned to the physiographic divisions of Fenneman (1928). ‘‘Flat’’ terrain was defined as the union of the Interior Plains, Laurentian Uplands, and Atlantic Plain regions. enforcement, emergency managers, trained spotters, reports fall into this extreme bin, indicating a larger pro- the general public, and media outlets. The increased portion of improbable wind gusts. Alternatively, newspa- skewness of the distributions in Figs. 4c and 5 relative per and broadcast media reports are more characteristic of to Fig. 4b is interpreted as a general bias of wind speed Fig. 4b with only 13.2% residing in the top 5% of GFs. overestimation by humans.2 Media reports may be more accurate since their stories can Although the distribution of each human constituent reference locally measured values. Additionally, some group generally resembles the aggregated histogram, broadcast media outlets may also purchase access to pro- subtle differences are observed. For instance, the fractions prietary mesonetworks (e.g., WeatherBug stations—note of reports from law enforcement (22.9%), the general that WeatherBug is a registered trademark), allowing public (22.1%), and trained spotters (21.2%) falling within them to report original wind measurements independent the top 5% of GHCN GFs are similar to the all-humans of NWS ASOS observations. Because of the suspicion that value of 23%. However, 32.6% of emergency manager media reports included a large number of measurements, they were excluded from the ‘‘human’’ category. An ordinary least squares regression line between the human-reported GFs (dependent variable) and the GHCN 2 Storm Data contains a ‘‘magnitude type’’ field that distin- GFs (independent variable) also captures the overestima- guishes estimated reports from measured reports. However, since nearly 5000 reports from automated stations (in the raw, all- tion trend (Fig. 6a). Equation (2) expresses the regression inclusive dataset of 63 302 events) are designated as ‘‘estimates,’’ relationship, and Eq. (3) shows the same relationship solved the credibility of this field is questioned. As a result, all reports for the ratio of estimated to measured GFs: from human sources are assumed to represent estimates although it is possible some of these reports were informed by in- GF 520:085 1 1:312GF and (2) strumentation. Cases where this assumption is invalid would not Storm Data GHCN alter the conclusions of this paper since measured human reports GF 20:085 would only serve to help shape the distributions in Figs. 5a–e more Storm Data 5 1 1:312. (3) similarly to Fig. 4b. GFGHCN GFGHCN

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FIG. 3. Workflow schematic used to derive p values for Storm Data–reported magnitudes. The GHCN GF probability curve used to compute p values for Storm Data GFs for flat-terrain events (n 5 6801) is pictured at the bottom of the figure. A p value is calculated for the shaded row as an example.

With a coefficient of determination equal to 0.56, there depicts an acceptably random pattern, lending credi- is a clear positive association between the observed and bility to the regression equation. The near-zero y in- reported GFs. The variation in estimated GF that is not tercept produced by the regression analysis is not explained by measured GF may be attributed to dis- significant, with the 95% confidence interval for the y crepancies in the method of wind estimation (e.g., by intercept including zero (Table 2). For the sake of feel or visual cues) and a number of circumstantial fac- developing a simple relationship for human estimation, tors (e.g., direction of impact, height at which an esti- the y intercept term will be dropped from Eqs. (2) and mate was formed). An analysis-of-variance procedure (3). The simplified relationship is presented below and yields an F ratio of 1605, and a residual plot (Fig. 6b) reveals that when considered within the context of the

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FIG. 4. Histograms of Storm Data GF p values for (a) all events originating in ﬂat terrain (n 5 5722) and the subsets of these events that were attributed to (b) an ASOS, AWOS, or ofﬁcial NWS observation (n 5 1629) and (c) a human source (n 5 1170). daily average, human wind estimates exceed measured yielding an average GF overestimation between 25% gusts by 31.2% on average: and 38%.

GF Storm Data 5 : 1 312. (4) 5. Discussion GFGHCN Several architectural and environmental engineering Equation (4) can only be derived by expressing gusts as studies have examined human discomfort associated GFs, meaning gust overestimation is clearly observed with wind perception (Hunt et al. 1976; Jackson 1978; when scaled according to the daily wind conditions. Melbourne 1978), but the study of perceived wind speed Although Eq. (4) is evidence of gust overestimation, has received less attention. A majority of the studies humans do not necessarily overestimate the wind gusts examining this phenomenon adopt sensory-based ex- themselves by 31.2%. Since the GF most directly rep- planations for gust overestimation. Within this frame- resents how much stronger than average a gust would work, wind estimates are believed to be informed solely have seemed on a given day (and only indirectly how through the tactile sensation experienced by the force of strong it was), a regression analysis between GustStorm Data the wind upon the skin. Since Newtonian force balances and GustGHCN will not yield the same relationship. The prove wind force is a function of wind speed squared, implications and significance of this result will be ad- previous work features a quadratic model for wind dressedinthesection 5. With the above limitations in speed overestimation. Even though this quadratic re- mind, the relationship in Eq. (4) will nonetheless be lationship exists in a mathematical sense, does an indi- referred to as ‘‘gust overestimation’’ hereinafter. As a vidual perceive wind speed in the same way? In a general rule of thumb, humans overestimate wind controlled wind tunnel experiment, Agdas et al. (2012) gusts, within the context of the daily average wind, by determined that a significant linear and quadratic re- one-third on average. The 95% confidence interval for lationship exists between actual wind speed and wind the ratio in Eq. (4) ranges from 1.248 to 1.376 (Table 2), speed perception. Our study offers a cognitive explanation

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FIG.5.Storm Data GF p values for all events originating in flat terrain that (a) were attributed to law enforcement (n 5 258), (b) emergency managers (n 5 193), (c) trained spotters (n 5 478), (d) the public (n 5 95), and (e) newspapers or broadcast media (n 5 547). for overestimation in which wind estimates are at least human source experiences a wind gust on an otherwise partially informed based on an individual’s experience. calm day, this would lead to an overestimation and the The strong correlation between estimated and measured conclusion that the gust ‘‘must have been really strong.’’ GFs suggests that considering a gust’s context can yield Moreover, experience with tropical systems, risk per- additional insight that may be overlooked by consider- ception (Agdas et al. 2012), wind direction, and exper- ing the gust alone. Though this analysis yields no definitive tise in estimating wind speed and direction (Pluijms evidence that cognitive processes are the only overesti- et al. 2015) all affect wind speed perception. Therefore, mation mechanisms present, a cognitive theory provides a unlike the theoretical relationship with force, perceptual better explanation for the results observed here. estimation of wind speed is also closely tied to complex Psychological studies have tested humans’ ability to cognitive processes. numerically estimate in various scenarios, resulting in Regardless of how estimates are formed, the trans- two separate cognitive estimation biases, or errors in ferability of this study’s results is relevant for both aca- judgment arising from the processing of information demics and operational forecasters who rely on human (Sherif et al. 1958; Coren and Miller 1974; Tversky and reports to conduct severe weather research or verify Kahneman 1974; Strack and Mussweiler 1997; Simmons severe weather warnings. Unlike nonconvective wind et al. 2010). Of the two cognitive biases associated with events, severe thunderstorms are accompanied by omi- numerical estimation, the contrast effect (Wundt 1980) nous audial and visual cues (Dewitt et al. 2015), which and anchoring (Tversky and Kahneman 1974), the may exacerbate the discrepancy between the actual and contrast effect is most clearly evident in this study. This perceived wind speeds. Previous psychological studies bias is described as being exposed to an initial stimulus have examined the effects of fear and stress on an in- (i.e., a steady breeze) followed by a second stimulus of dividual’s perceptions, and have discovered an increase differing magnitude (i.e., a strong gust). When the in- in stimulus estimation during fearful situations (Hekmat dividual is exposed to the second stimulus, their per- 1987; Rachman and Cuk 1992). While no study has ception of that stimulus is pushed in the opposite specifically examined speed, others have documented direction of the initial stimulus. For example, when a the overestimation of time (Grommet et al. 2011),

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FIG. 6. (a) Scatterplot used to form the regression relationship in Eq. (2). The human-

reported GF (GFStorm Data; y axis) possesses a clear positive association with the GHCN GF (GFGHCN; x axis). The 95% confidence interval for the regression equation is shaded in light gray. (b) Scatterplot of the residuals between the data points in (a) and the values predicted by Eq. (2). vertical heights (Stefanucci and Proffitt 2009), and false-alarm ratios, a perennial challenge of NWS warn- geographical slants/inclines (Proffitt et al. 1995) due to a ing issuance (Barnes et al. 2007). heightened sense of fear. While this study’s results To maintain the integrity of NWS skill metrics as well cannot be definitively extrapolated to thunderstorm- as hazardous weather archives, greater scrutiny must be related winds, evidence suggests that estimates would be applied to human wind estimates. However, any effort less accurate in convective situations that are more to vet suspect human reports must be part of a wider strongly feared by the public. NWS warning verification policy (Office of Inspections This analysis also illustrates how the wind speed and Program Evaluations 1998). Currently, the desire to thresholds used for defining ‘‘severe’’ gusts or ‘‘high’’ use human wind reports to verify weather warnings deters winds are easily susceptible to human overestimation. NWS offices from rejecting all but the most egregious Inflated human reports may not only contribute to the overestimates. Additionally, the warning verification incorrect verification of a wind-related warning (though process indirectly drives the inclusion of wind events in unintentionally so), but they can also be recorded within Storm Data, affecting researchers who use it as a haz- the annals of Storm Data indefinitely. Even if a future ardous weather database. One possible protocol for researcher treats the ‘‘magnitude’’ field with skepticism, handling human estimates involves calculating a GF 2 for example, by excluding the reported 58 mi h 1 using the human-reported gust. If the GF exceeds a 2 (25.9 m s 1) value from analysis, the mere presence of certain threshold, then the subsequent Storm Data re- the entry in Storm Data will still yield the false impres- port could be recorded with a quality flag similar to the sion of a severe weather occurrence. Efforts to construct archive structure of NCDC instrumental observations. thunderstorm wind/severe weather climatologies will be The entry would not be modified or rejected, but future biased by these entries without careful consideration to account for them (e.g., Smith et al. 2013). Additionally, TABLE 2. Error estimations and significances for regression many experimental severe thunderstorm forecasting parameters in Eq. (2). Prob indicates probability. protocols rely on Storm Data records to calibrate their Parameter Estimate t ratio Prob . jtj 95% lower 95% upper tools (e.g., Witt et al. 1998b; Schultz et al. 2011; Miller et al. 2015). The development of new procedures using y intercept 20.085 20.77 0.44 20.30 0.13 , incorrect training data would predispose them to large Slope 1.312 40.06 0.001 1.248 1.376

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Storm Data consumers would be aware that the esti- provided the reference upon which human gust reports mate’s accuracy was suspect. were judged. Reports that yielded large GFs, located in Another key finding is that human estimation errors the upper tail of the observed GF distribution, were are present among groups that are considered ‘‘weather considered less likely than reports with GFs residing savvy’’ or may even have completed some sort of me- closer to the mean of the distribution. teorological training. The tendency to report statistically Our results show that humans report exceptional gusts improbable gusts is not limited to the general public, but more frequently than they are detected by automated pervades even sources that might be considered au- instrumentation during the same events. Additionally, thorities on hazardous weather. One possible explana- subgroupings of the ‘‘human’’ category each demon- tion for this trend could lie in the weather education that strate the same propensity for overestimation. This is not these groups receive. For instance, the Beaufort wind to say that every human gust estimate is inaccurate, but force scale, included in UCAR’s online SKYWARN rather, taken in sum, humans report exceptional wind spotter training (UCAR 2011),3 associates broken or gusts more frequently than automated stations observe 2 uprooted trees with wind speeds of roughly 58 mi h 1 them. Human-estimated GFs exceeded measured GFs by 2 (25.9 m s 1). This tool was originally developed in 1805 approximately one-third on average. We do not suggest for maritime application (Curtis 1897) with the land- that forecasters dismiss all nonconvective wind estimates based wind speed indicators not being added until 1906. as inaccurate, but instead view them within the context of However, these new additions were informed by a single the background wind conditions of the day. weather observer’s anecdotal observations from North Human wind reports are a vital supplement to the Shields, England (Simpson 1906), and have never been relatively sparse network of automated weather stations corroborated by scientific investigation. In contrast, in the United States. However, despite their best in- Miller et al. (2016) found that 92% of all human- tentions, humans demonstrate a tendency to report reported nonconvective wind events were character- statistically improbable wind gusts. Future research 2 2 ized by peak gusts weaker than 58 mi h 1 (25.9 m s 1)— should consider the influence of high wind warning is- with at least one-quarter of these being associated with suance and emotional discomfort (i.e., fear) on estima- tree failure. If a SKYWARN spotter or emergency tion accuracy. In the interim, NWS warning verification manager had used these fallen trees to inform a procedures should seek to include a formal treatment 2 2 58 mi h 1 (25.9 m s 1) estimate as recommended during for human wind reports. Meteorologists and psycholo- his or her formal training, then a large, statistically im- gists might collaborate to better understand the cogni- probable GF would have resulted.4 In addition to the tive processes involved in wind speed estimation with cognitive biases inherent to human wind perceptions, the eventual goal of developing a human-wind-estimate legacy tools from the early days of weather observation correction factor. Alternatively, researchers and NWS may further predispose even the savviest weather en- outreach coordinators might consider partnering in the thusiasts to overestimate wind speed. development of an empirically derived landscape-cue- based estimation scale [modeled after the Beaufort scale 6. Conclusions but for weaker wind speeds than the enhanced Fujita (EF) scale] that broadly constrains human estimates. Instrumentally observed relationships between maximum daily gusts and daily average wind speeds were Acknowledgments. The authors thank Lynne Seymour used to assess the likelihood of human wind reports from for her statistical guidance as well as Kyle Mattingly Storm Data. Nonconvective wind events in areas of flat for his helpful comments on an earlier version of the terrain served as the basis for this analysis because of the manuscript. The authors also thank Roger Edwards and relative spatial homogeneity of wind fields in these two anonymous reviewers for their comments, which meteorological and geographic settings. The distribution greatly strengthened the manuscript. CAW acknowl- of GHCN-measured gust factors, the ratio of the daily edges the support of a National Science Foundation maximum gust to the daily average wind [Eq. (1)], Graduate Research Fellowship in the completion of this work (Grant DGE-1443117).

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