APRIL 2016 M I L L E R E T A L . 1009 Quantitative Assessment of Human Wind Speed Overestimation 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 at- tributed 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.