RETHINKING SNOWSTORMS AS SNOW EVENTS a Regional Case Study from Upstate New York
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RETHINKING SNOWSTORMS AS SNOW EVENTS A Regional Case Study from Upstate New York BY DAVID A. CALL Whether predicting or planning for snowstorms, forecasters, government, and the general pub- lic should consider both meteorological and human-caused variations inherent in snow events. n March 1888, an epic snowstorm disrupted nor- Why did these two significant snowstorms have mal life in much of the Northeastern United States. such dramatically different effects on society? In I The Blizzard of 1888 dumped more than 3 ft (91 1993, factors such as massive government interven- cm) of snow in much of the Hudson Valley and tion, cooperative behavior by the general public, and Connecticut, while winds as high as 36 m s–1 created advances in forecasting ability and forecast dissemi- drifts of more than 20 ft (6 m); (Kocin 1983; Cable nation allowed for adequate preparation, appropri- 1988). One hundred and five years later, another ate actions during the storm, and efficient cleanup epic blizzard struck the Northeast. The Blizzard of operations afterward (Uccellini et al. 1995). While 1993 was equally impressive, with incredible snow the mitigating influence of these factors may seem accumulation—such as 42 in. (107 cm) in Syracuse, intuitive, surprisingly few researchers—either within New York —strong winds, and a paralyzing impact. or outside the meteorological community—have However, despite the larger area and greater popula- examined how nonmeteorological factors affect a tion affected by this storm, fewer human fatalities snowstorm’s impact. Although, Hart and Grumm were attributed to the Blizzard of 1993 than to the (2001) and Zielinski (2002) have devised scales rating Blizzard of 1888. Furthermore, within a few days winter storms, only Rooney (1967) and Kocin and after the Blizzard of 1993 ended, many businesses Uccellini (2004b) have created snowfall scales that and schools resumed normal operations. incorporate any nonmeteorological factors. Rooney’s five-category scale focused on snow’s effects on transportation networks, while Kocin and Uccellini AFFILIATION: CALL—Department of Geography, Syracuse considered population in their Northeast snowfall University, Syracuse, New York impact scale. CORRESPONDING AUTHOR: David Call, 144 Eggers Hall, Dept. This author agrees with Kocin and Uccellini of Geography, Syracuse University, Syracuse, NY 13244 (2004b) that creating an easily understood scale that E-mail: [email protected] DOI:10.1175/BAMS-86-12-1783 incorporates the range of snowstorm variability may be difficult. Nonetheless, an understanding of In final form 18 July 2005 ©2005 American Meteorological Society the significant factors that affect a snowstorm’s im- pact—both meteorological and nonmeteorological AMERICAN METEOROLOGICAL SOCIETY DECEMBER 2005 | 1783 sified under four metafactors: meteorological varia- tions, governmental response, actions of the general public, and meteorologists and the media. A sum- mary table grouping the factors into major or minor categories will also be presented. Finally, this article will conclude with a call to action for both forecasters and the community at large. Ultimately, meteorolo- gists and broader society should revise the concept of snowstorms into one of snow events, a richer term that reminds us that not only are meteorological fac- tors responsible for variations in snowstorm impacts, but numerous human-created factors also play a role. In other words, while the Blizzards of 1888 and 1993 were both significant snowstorms, they were dramati- cally different snow events. FIG. 1. Locations of the case study cites within New York State. METHODOLOGY. Snowstorms that occurred in four major cities in Upstate New York—Buffalo, ones—will aid those who wish to create such a scale. Rochester, Syracuse, and Albany—between 1888 and Furthermore, an awareness of such factors benefits 2003 were studied to evaluate the significance of all affected by snowstorms, especially those charged factors influencing snow events. Figure 1 shows the with warning government and the general public. locations of the four cities within New York State. This article will introduce the various factors that These cities were selected because of their similar influence the impact of snowstorms and illustrate their significance. While numerous factors account TABLE 2. Dates, amounts (in.), and a qualitative for variation in snowstorm impacts, most can be clas- assessment of the disruption for the case study storms for Rochester. Note that disruption—a measure of the snow event—is not correlated with TABLE 1. Dates, amounts (in.), and a qualitative amount. assessment of the disruption for the case study Date(s) of storm Amount Disruption storms for Buffalo. Note that disruption—a mea- sure of the snow event—is not correlated with 28 Feb–Mar 1900* 43.5 Medium amount. Question mark indicates a lack of suffi- 11–12 Dec 1944 21.5 High cient data. 15–19 Feb 1958 30.2 Medium Date(s) of storm Amount Disruption 14 Feb 1960 18.4 Medium 21–22 Jan 1902 17.4 Low? 19–20 Feb 1960 21.6 Medium 17 Mar 1936 19.0 High 23 Jan 1966 18.2 High 14–16 Dec 1945 36.0 Low 30–31 Jan 1966 26.7 High 28–29 Nov 1955 19.9 Medium 5 Dec 1977 10.8** High 29-30 Dec 1961 24.5 Low 6–7 Feb 1978 25.0 Low 30 Nov–2 Dec 1976 39.8 Low 8–10 Dec 1981 25.1 None 28–30 Jan 1977 * Extreme 28 Feb–1 Mar 1984 29.0 Medium 30 Nov–Dec 1979 22.4 Medium 11–12 Mar 1992 21.9 Low 10–11 Jan 1982 28.8 Low 13–14 Mar 1993 23.2 Low 27–29 Feb 1984 28.3 Medium 3 Jan 1996 23.0 Low 19–21 Jan 1985 33.2 High 12–15 Jan 1999 29.2 Medium 9–10 Dec 1995 41.2 Medium 4 Mar 1999 22.3 High 18–20 Nov 2000 38.9 High 6 Mar 1999 18.4 Medium 24 Dec 2001–1 Jan 2002 81.6 High *Two-day storm; no 29 Feb 1900. *Amount of accumulation unkown. **Another 13.1 in. fell 6–9 Dec 1977. 1784 | DECEMBER 2005 size and attributes, such as climate, elevation, and of how a single storm differentially impacted multiple economy. Another reason these cities were selected cities were rarely done. was to examine differences between lake-effect For each case study, newspaper accounts from versus synoptic-scale snow events. Largely due to two days before the storm began until news cover- variations in lake-effect snow, average annual snow- age ended were read; most storms disappeared from fall ranges from 63 in. (160 cm) in Albany to 120 in. news coverage within a week after the last flakes fell. (305 cm) in Syracuse. Prior to 1888, these cities had To learn more about governmental response to snow, little involvement in snow mitigation and gener- the author interviewed “Commissioners of Snow” ally waited for warmer weather to alleviate snow in Buffalo, Rochester, and Syracuse, and examined problems. Thus, the first case study in the sample budgets and expense reports for all four cities. Finally, was the Blizzard of 1888. However, because sparse local broadcast meteorologists were asked to respond news coverage and weak governmental response to a set of interview questions. This was done to get hampered efforts to study storms early in the sam- a sense of both their involvement in snow events and pling period, most of the cited examples are from their beliefs about the influence of the meteorology the 1930s through 2003. community. For each city, a list of the largest snowstorms within the study period was compiled; details of this METEOROLOGICAL VARIATIONS. Meteoro- process are in the appendix. The 10 largest storms and logical variations are perhaps the most obvious cause selected storms ranked 11–20 were then selected as for differences in snow events (see Changnon 1969; the case studies. Lower-ranked storms were chosen Kocin and Uccellini 2004a). This is largely because either to fill temporal gaps in data or because they oc- meteorological parameters such as total snow accu- curred very close in time to a “top 10” storm. In total, mulation are widely available and easily understood. 59 case studies, for an average of nearly 15 per city, While the total amount of snow is important, varia- were studied. Complete lists of case study storms are tions in other parameters of a snowstorm, such as shown in Tables 1–4. Because the largest snowstorms snowfall rate (intensity), snow density, air tempera- for each city were determined strictly by snowfall for that city and not in consideration of whether other TABLE 4. Dates, amounts (in.), and a qualitative cities received significant accumulation, comparisons assessment of the disruption for the case study storms for Albany. Note that disruption—a measure of the snow event—in not correlated with the amount. Question mark indicates a lack of suf- TABLE 3. Dates, amounts (in.), and a qualitative ficient data. assessment of the disruption for the case study storms for Syracuse. Note that disruption—a Date(s) of storm Amount Disruption measure of the snow event—in not correlated with the amount. Question mark indicates a lack of suf- 11–14 Mar 1888 46.7 High ficient data. 22–25 Feb 1893 18.2 Low? Date(s) of storm Amount Disruption 14 Feb 1914 23.5 Medium 18–20 Jan 1936 17.9 Low 6–8 Mar 1932 18* Medium 8–12 Mar 1941 17.8 Low 8–9 Feb 1958 25.3 Medium 8–9 Feb 1958 21.1 Medium 16–17 Feb 1958 29.2 High 15–16 Feb 1958 17.9 Medium 30 Jan–1 Feb 1966 42.3 High 24–25 Dec 1966 18.3 Low 5–10 Dec 1977 24.2 Medium? 22 Dec 1969 12.3 Medium 28 Feb–3 Mar 1984 30.9 Low 25–28 Dec 1969 26.4 High 15–17 Dec 1989 25.2* Low 24–25 Nov 1971 22.5 None 14–21 Jan 1992 38.6 Low 15–16 Jan 1983 24.5 Low 11–15 Mar 1992 31.7 Low 13–14 Mar 1993 26.6 Medium 13–14 Mar 1993 42.0 Medium 25 Dec 2002 21.0 Medium 4-9 Jan 1994 42.2 High 3–4 Jan 2003 20.8 Medium 30–31 Dec 1997 25.9 Medium 6–7 Dec 2003 18.0 Low *Based on newspaper reports.