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 public should consider both meteorological and human-caused variations inherent in snow events.
I
n March 1888, an epic snowstorm disrupted normal life in much of the Northeastern United States.
The Blizzard of 1888 dumped more than 3 ft (91
cm) of snow in much of the Hudson Valley and
Connecticut, while winds as high as 36 m s–1 created
drifts of more than 20 ft (6 m); (Kocin 1983; Cable
1988). One hundred and five years later, another
epic blizzard struck the Northeast. The Blizzard of
1993 was equally impressive, with incredible snow
accumulation—such as 42 in. (107 cm) in Syracuse,
New York —strong winds, and a paralyzing impact.
However, despite the larger area and greater population affected by this storm, fewer human fatalities
were attributed to the Blizzard of 1993 than to the
Blizzard of 1888. Furthermore, within a few days
after the Blizzard of 1993 ended, many businesses
and schools resumed normal operations.
AFFILIATION : CALL—Department of Geography, Syracuse
University, Syracuse, New York
CORRESPONDING AUTHOR : David Call, 144 Eggers Hall, Dept.
of Geography, Syracuse University, Syracuse, NY 13244
E-mail: [email protected]
DOI:10.1175/BAMS-86-12-1783
In final form 18 July 2005
©2005 American Meteorological Society
AMERICAN METEOROLOGICAL SOCIETY
Why did these two significant snowstorms have
such dramatically different effects on society? In
1993, factors such as massive government intervention, cooperative behavior by the general public, and
advances in forecasting ability and forecast dissemination allowed for adequate preparation, appropriate actions during the storm, and efficient cleanup
operations afterward (Uccellini et al. 1995). While
the mitigating influence of these factors may seem
intuitive, surprisingly few researchers—either within
or outside the meteorological community—have
examined how nonmeteorological factors affect a
snowstorm’s impact. Although, Hart and Grumm
(2001) and Zielinski (2002) have devised scales rating
winter storms, only Rooney (1967) and Kocin and
Uccellini (2004b) have created snowfall scales that
incorporate any nonmeteorological factors. Rooney’s
five-category scale focused on snow’s effects on
transportation networks, while Kocin and Uccellini
considered population in their Northeast snowfall
impact scale.
This author agrees with Kocin and Uccellini
(2004b) that creating an easily understood scale that
incorporates the range of snowstorm variability
may be difficult. Nonetheless, an understanding of
the significant factors that affect a snowstorm’s impact—both meteorological and nonmeteorological
DECEMBER 2005
| 1783
sified under four metafactors: meteorological variations, governmental response, actions of the general
public, and meteorologists and the media. A summary 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, meteorologists 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 factors 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 dramatically different snow events.
FIG . 1. Locations of the case study cites within New
York State.
ones—will aid those who wish to create such a scale.
Furthermore, an awareness of such factors benefits
all affected by snowstorms, especially those charged
with warning government and the general public.
This article will introduce the various factors that
influence the impact of snowstorms and illustrate
their significance. While numerous factors account
for variation in snowstorm impacts, most can be clas-
TABLE 1. Dates, amounts (in.), and a qualitative
assessment of the disruption for the case study
storms for Buffalo. Note that disruption—a measure of the snow event—is not correlated with
amount. Question mark indicates a lack of sufficient data.
Date(s) of storm
METHODOLOGY. Snowstorms that occurred
in four major cities in Upstate New York—Buffalo,
Rochester, Syracuse, and Albany—between 1888 and
2003 were studied to evaluate the significance of
factors influencing snow events. Figure 1 shows the
locations of the four cities within New York State.
These cities were selected because of their similar
TABLE 2. Dates, amounts (in.), and a qualitative
assessment of the disruption for the case study
storms for Rochester. Note that disruption—a
measure of the snow event—is not correlated with
amount.
Date(s) of storm
Amount
Disruption
28 Feb–Mar 1900*
43.5
Medium
11–12 Dec 1944
21.5
High
15–19 Feb 1958
30.2
Medium
14 Feb 1960
18.4
Medium
Amount
Disruption
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
28–29 Nov 1955
19.9
Medium
29-30 Dec 1961
24.5
Low
30 Nov–2 Dec 1976
39.8
Low
*
Extreme
30 Nov–Dec 1979
22.4
Medium
10–11 Jan 1982
28.8
Low
27–29 Feb 1984
28.3
Medium
19–21 Jan 1985
33.2
High
9–10 Dec 1995
41.2
Medium
18–20 Nov 2000
38.9
24 Dec 2001–1 Jan 2002
81.6
28–30 Jan 1977
*Amount of accumulation unkown.
1784 |
DECEMBER 2005
26.7
High
10.8**
High
6–7 Feb 1978
25.0
Low
8–10 Dec 1981
25.1
None
28 Feb–1 Mar 1984
29.0
Medium
11–12 Mar 1992
21.9
Low
13–14 Mar 1993
23.2
Low
3 Jan 1996
23.0
Low
12–15 Jan 1999
29.2
Medium
4 Mar 1999
22.3
High
High
6 Mar 1999
18.4
Medium
High
*Two-day storm; no 29 Feb 1900.
**Another 13.1 in. fell 6–9 Dec 1977.
5 Dec 1977
size and attributes, such as climate, elevation, and
economy. Another reason these cities were selected
was to examine differences between lake-effect
versus synoptic-scale snow events. Largely due to
variations in lake-effect snow, average annual snowfall ranges from 63 in. (160 cm) in Albany to 120 in.
(305 cm) in Syracuse. Prior to 1888, these cities had
little involvement in snow mitigation and generally waited for warmer weather to alleviate snow
problems. Thus, the first case study in the sample
was the Blizzard of 1888. However, because sparse
news coverage and weak governmental response
hampered efforts to study storms early in the sampling period, most of the cited examples are from
the 1930s through 2003.
For each city, a list of the largest snowstorms
within the study period was compiled; details of this
process are in the appendix. The 10 largest storms and
selected storms ranked 11–20 were then selected as
the case studies. Lower-ranked storms were chosen
either to fill temporal gaps in data or because they occurred very close in time to a “top 10” storm. In total,
59 case studies, for an average of nearly 15 per city,
were studied. Complete lists of case study storms are
shown in Tables 1–4. Because the largest snowstorms
for each city were determined strictly by snowfall for
that city and not in consideration of whether other
cities received significant accumulation, comparisons
TABLE 3. Dates, amounts (in.), and a qualitative
assessment of the disruption for the case study
storms for Syracuse. Note that disruption—a
measure of the snow event—in not correlated with
the amount. Question mark indicates a lack of sufficient data.
Date(s) of storm
6–8 Mar 1932
Amount
Disruption
18*
Medium
8–9 Feb 1958
25.3
Medium
16–17 Feb 1958
29.2
High
30 Jan–1 Feb 1966
42.3
High
5–10 Dec 1977
24.2
Medium?
28 Feb–3 Mar 1984
30.9
Low
15–17 Dec 1989
25.2*
Low
14–21 Jan 1992
38.6
Low
11–15 Mar 1992
31.7
Low
13–14 Mar 1993
42.0
Medium
4-9 Jan 1994
42.2
High
30–31 Dec 1997
25.9
Medium
*Based on newspaper reports.
AMERICAN METEOROLOGICAL SOCIETY
of how a single storm differentially impacted multiple
cities were rarely done.
For each case study, newspaper accounts from
two days before the storm began until news coverage ended were read; most storms disappeared from
news coverage within a week after the last flakes fell.
To learn more about governmental response to snow,
the author interviewed “Commissioners of Snow”
in Buffalo, Rochester, and Syracuse, and examined
budgets and expense reports for all four cities. Finally,
local broadcast meteorologists were asked to respond
to a set of interview questions. This was done to get
a sense of both their involvement in snow events and
their beliefs about the influence of the meteorology
community.
METEOROLOGICAL VARIATIONS. Meteorological variations are perhaps the most obvious cause
for differences in snow events (see Changnon 1969;
Kocin and Uccellini 2004a). This is largely because
meteorological parameters such as total snow accumulation are widely available and easily understood.
While the total amount of snow is important, variations in other parameters of a snowstorm, such as
snowfall rate (intensity), snow density, air temperaTABLE 4. Dates, amounts (in.), and a qualitative
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 sufficient data.
Date(s) of storm
Amount
Disruption
11–14 Mar 1888
46.7
High
22–25 Feb 1893
18.2
Low?
14 Feb 1914
23.5
Medium
18–20 Jan 1936
17.9
Low
8–12 Mar 1941
17.8
Low
8–9 Feb 1958
21.1
Medium
15–16 Feb 1958
17.9
Medium
24–25 Dec 1966
18.3
Low
22 Dec 1969
12.3
Medium
25–28 Dec 1969
26.4
High
24–25 Nov 1971
22.5
None
15–16 Jan 1983
24.5
Low
13–14 Mar 1993
26.6
Medium
25 Dec 2002
21.0
Medium
3–4 Jan 2003
20.8
Medium
6–7 Dec 2003
18.0
Low
DECEMBER 2005
| 1785
ture, wind, and duration can have just as much, if not
a greater, effect on the associated snow event. Indeed,
the term blizzard implies both windy (>15 m s–1) and
snowy conditions leading to a reduction in visibility
(<0.4 km; Branick 1997). Timing of a storm is also
important, although this is mainly because of variations in human factors, such as traffic volume.
For Buffalo, Rochester, and Syracuse, intensity is
often more important than total snowfall amount.
Because these cities individually average more than
90 in. (229 cm) of snow per season, snow mitigation
operations are efficient, members of the general
public are experienced in dealing with snow, and forecasters are skilled at snow prediction. Thus, a large
but unexceptional amount of snow may not cause
much disruption of the normal routine. However, if
the snow falls with extraordinary intensity, normal
life can quickly grind to a halt, even if for just a few
hours. When 25 in. (63 cm) of snow fell on “Gridlock
Monday 2000” (20 November), thousands of Buffalo
commuters and students were stranded downtown in
what the Buffalo News (21 November 2000) termed
the “worst storm since . . . 1977.” In Rochester, an
intense (and largely underforecast) storm deposited more than 22 in. (56 cm) of snow on Thursday
4 March 1999. Local governmental authorities closed
all interstate highways because of accidents, causing
massive gridlock. Finally, when snow fell at a rate
exceeding 4 in. (13 cm) h–1 on Tuesday 4 January 1994
(the Syracuse “Snowburst”), Syracuse area schools
and offices dismissed students and workers early,
resulting in widespread gridlock.
An equally significant meteorological factor is
when a snowstorm occurs, although timing is important primarily because of variations in traffic volume.
Timing as an attribute influencing snow events can
be further subdivided into different scales. On the
seasonal scale, early or late season snowstorms may
lead to less significant snow events because of relatively warm ground, higher sun angles, and longer
day length; all of these reduce snow accumulations
on roadways.1 Similarly, snowstorms that occur on
holidays, weekends, or during school breaks are often lesser snow events than what might be expected
1
1786 |
Early or late-season snowstorms may be problematic because
of the potential for downed power lines and unprepared
motorists. However, few reported problems with downed
trees and lines were observed in this study, perhaps because
of the focus on cities or maybe due to a lack of case-study
storms before late November. As for unprepared motorists,
their disruptive effect was also negligible, even when a storm
was the first of the season.
DECEMBER 2005
based on the meteorological data. For example, the
December 2001 “Superstorm” dumped more than
80 in. (203 cm) of snow at the Buffalo airport. Because
it occurred between Christmas and New Year’s Day—
when schools were closed and many workers were on
vacation—disruption for most residents of the City
of Buffalo and its suburbs was arguably less than that
of the Gridlock Monday 2000 storm. Likewise, the
Blizzard of 1993 (studied in Rochester, Syracuse, and
Albany) had a relatively small impact compared to
other record-setting snowstorms, such as the Blizzard
of 1977 in Buffalo or the Blizzard of 1996 in Syracuse,
simply because it occurred on a weekend. Even when
a snowstorm occurs during the hectic shopping weekends before Christmas (examples include Buffalo
in 1995 and Syracuse in 1989), the general public
largely chooses to stay home rather than venture
out. Conversely, all three aforementioned gridlock
storms occurred on weekdays. Additionally, the most
intense snow fell between 1000 and 1400 LT. Thus, in
contrast to popular opinion, storms that are intense
at midday are often more disruptive than those that
strike at rush hour. Midday storms cause a super–rush
hour with schools and workplaces being dismissed
simultaneously. Comparatively, storms that peak in
the overnight or early morning hours may compel
workers and students to remain at home; for storms
occurring late in the day, students are already home
or in the process of returning home, and worker
dismissals are staggered. In response to the Syracuse
Snowburst 1994 storm, the City of Syracuse and
downtown businesses created a nonbinding plan to
dismiss workers on a gradual basis, with the aim of
preventing a super–rush hour and widespread gridlock (J. Wright 2003, personal communication).
If two snowstorms occur close together and the
period between them is cold, the second storm is
often a more disruptive snow event. In February
1958, two synoptic-scale storms affected Syracuse
and Albany in just over a week’s time. Although the
first storm dumped more snow on Syracuse—21 in.
(53 cm) versus 17 in. (43 cm) with the second—the
second storm was more disruptive. In Albany, the
second storm was more disruptive than expected
when compared with similar storms in 1941 and 1966.
Snow remaining from the first storm and equipment
failure largely explain why a second storm is a more
disruptive event. When two 15-in. (38 cm) lake-effect
snowstorms affected Buffalo in 1985, more than half
of the city’s snow mitigation equipment was out of
service the day after the second storm (Buffalo News,
22 January 1985). Even if no major snowstorms occur,
a prolonged period of snow and cold can have increas-
ingly negative effects. A record 78.1 in. (198 cm) of
snow fell in Syracuse during January 2004. Two fatal
accidents, on two separate days, occurred late in the
month when drivers skidded across a long bridge on
Interstate 81, hit packed snow along the guardrail,
flew off the highway, and fell 15 m to the ground
below. Prior to the accidents, measurable snow had
fallen every day for more than 2 weeks, and although
plow drivers for New York State had been salting and
plowing daily, they had not had time to physically remove the snow along the guardrail. Similar incidents,
thankfully with less deadly results, occurred in Rochester following a similar period in January 1999.
Air temperature is another meteorological factor
that affects snow events. Neither cold nor warm temperatures are necessarily worse; instead, each presents
a different set of complications for snow mitigation
crews. At temperatures below –9°C, road salt is largely
ineffective (Moran et al. 1992). However, the drier
and thus less dense snow at these temperatures is
easier for plow crews to push and cars to drive over
(P. Noto 2003, personal communication). While temperatures near 0°C increase salt’s effectiveness, snow
has a higher water content and greater density. This
heavier and more clumpy snow puts a greater strain
on snow mitigation equipment—assuming the equipment is able to break through the slush. More than
161 km of Rochester streets were left impassible when
seven contractors failed to clear their routes after a
storm in February/March 1984. Several interviewed
by the newspaper blamed the unusually dense snow
for causing equipment failure and their breach of
contract. One contactor termed the snow “cast iron”
(Rochester Democrat and Chonicle,1 March 1984), an
appropriate metaphor for the way dense snow fuses
together when cold weather follows a storm. Thus, if
a city is slow to respond to a storm with dense snow,
removal can become impossible. The March 1936
“St. Patrick’s Day” storm dumped 19 in. (48 cm) of
snow and sleet on Buffalo; the precipitation mixture
had an usually low snow-to-water ratio of ~7:1. Snow
removal crews were called late and found frozen slush
that was then impossible to clear, and the city was
paralyzed for a week.
Wind is another meteorological factor that affects
the snow events, although its significance is less than
might be expected. Within the cities studied, there is
little open space for snow to drift. Additionally, such
open space is essentially constant; thus, mitigation
operations have been adjusted to give drift-prone
areas extra attention if needed [P. Noto 2003, personal
communication; J. Wright 2003, personal communication; Albany (NY) Bureau of Streets Annual Report
AMERICAN METEOROLOGICAL SOCIETY
1986]. Nonetheless, exceptionally strong winds,
coupled with light snow, can cause widespread drifting, and that is a major problem. Such was the case in
Buffalo with both the Blizzard of 1977, when winds
gusted to 31 m s–1, and after the January 1985 storm,
when winds gusted to 22 m s–1.
To summarize, meteorological variations in snowstorms can greatly affect their impact and thus cause
dramatically different snow events. However, snow
events are also affected by many nonmeteorological
factors, such as governmental response, and problems
in this area can create major snow events out of minor
snowstorms.
GOVERNMENTAL RESPONSE. Although meteorological variations in snowstorms—especially intensity—are important in understanding differences
in snow events, social factors are equally important.
As shown already, timing is significant largely because of social reasons, such as variations in traffic
volume or holiday periods. Behavior of the government is another important social factor. Smart planning, competent workers, and reliable contractors aid
government and minimize disruption, but failure in
any of these areas can create a snow disaster.
Effective snow mitigation begins with good leadership at the highest level of local government. Thus,
when political fights spill into snow mitigation operations, minor snowstorms quickly become major snow
events. Among the cities studied, Buffalo has experienced the most politically enhanced snow events. A
budget dispute between Mayor George Zimmerman
and Buffalo Common Council was already simmering when the St. Patrick’s Day 1936 storm hit the city.
Because the snow budget was exhausted, Zimmerman
did not order crews to begin plowing until the evening, well after the snow was already settling into
a dense, slushy mess. Council blamed the lack of
funds on fiscal mismanagement by the mayor, while
the mayor-appointed public works director accused
the council of failing to provide sufficient funds for
equipment (Buffalo News, 18 March 1936). Travel
throughout the city remained at a standstill for nearly
a week until crews from adjacent Erie County and
melting from warmer temperatures finally cleared
the snow. In 1985, Mayor James “Jimmy” Griffin and
Buffalo Common Council were already fighting over
who should direct snow removal operations when a
severe storm hit. Lacking a snow mitigation plan,
and hampered by equipment failure, workers did an
uneven and generally poor job of snow mitigation,
causing citizens to flood council with complaints,
including discriminatory plowing (Buffalo News,
DECEMBER 2005
| 1787
26 January 1985). Despite these complaints and
similar ones following a snowstorm in January 1984,
Mayor Griffin was reelected three times. Although
there have been some examples to the contrary, such
as Mayor John Lindsey in New York City in 1969
(see McKelvey 1995), problems with snow mitigation are not always fatal to political careers—even
in Buffalo.
Regardless of political leadership, without competent workers and the proper equipment, snow mitigation operations will fail. Nonetheless, it appears that
the workers are rarely a problem—both newspapers
and Commissioners of Snow consistently praise them
as being dedicated and capable. For example, Joseph
N. Giambra, Commissioner of Snow for Buffalo,
termed his employees “super” and noted that “they’ll
work 16 hours for you and not hesitate about coming
in the next morning” (2003, personal communication). Additionally, the size of the workforce is almost
always sufficient. One notable exception occurred
in Rochester in December 1944, when a labor shortage due to World War II exacerbated snow removal
difficulties initially caused by poor leadership (the
workers were called late). Although most cities
since then have employed enough workers, having a
sufficient quantity of working snow removal equipment is another story. In all four cities studied, old
equipment was used well beyond its life expectancy,
and breakdowns hampered operations. Examples of
this include snowstorms in Buffalo 1936, 1945, and
1985; Albany in 1958; and Syracuse in 1958 and 1966.
However, thanks to improvements in snow removal
technology, equipment problems today are less common and less severe than those in the past.
In all four cities, contractors are an important
component of the snow mitigation force. Use of contractors allows cities to save money on equipment
purchases and labor, yet they can still increase their
workforce when an extraordinary storm occurs.
Unfortunately, contractors sometimes fail to appear
or fail to use adequate equipment for clearing heavy,
compacted snow. Historically, Rochester has relied on
contractors more than the other cities, and in both
1977 and 1984 no-show contractors slowed recovery after major storms. In these cases, the problem
was exacerbated by the city’s lack of trucks small
enough to squeeze down narrow residential streets.
Consequently, Rochester today has a larger number
of small trucks than in the past, and contractors
are required to bring their equipment to the Public
Works Garage for inspection annually (P. Noto 2003,
personal communication). More generally, all of the
cities studied are less reliant on contractors today
1788 |
DECEMBER 2005
compared to the past (based on city budgets; P. Noto
2003, personal communication; J. Wright 2003,
personal communication).
Finally, outside aid—in the form of Disaster
Declarations and National Guard assistance—is
more common today than in the past. While such
assistance is rarely available until after a snow event
is well underway, the extra labor and governmental
funds help people in cities resume normal activities
more quickly.
ACTIONS OF THE GENERAL PUBLIC.
Equally significant to governmental response are the
actions of the general public. Most important are carrelated actions, both in terms of parking and driving.
Parked cars impede plowing operations, while cars
in driving lanes—especially if stalled, abandoned,
or gridlocked—get in the way. More generally, individual decisions by members of the general public to
remain at home or venture out can collectively affect
a snow event.
The greatest problem that cities face in dealing
with snow is parked cars. One commissioner claimed
that parked cars were his city’s “biggest nemesis,”
while another twice referred to them as a “nightmare”
(P. Noto 2003, personal communication; J. Wright
2003, personal communication). Unlike adjacent
suburbs, which almost always ban nighttime parking
during the snow season, the cities studied are hampered by old neighborhoods with a shortage of offstreet parking. At best, cars parked on the street slow
down plows and restrict curb-to-curb clearing operations. At worst, they can block plows from entering
narrow streets. Problems are not limited to residential
areas, either; illegal downtown parking created many
problems encountered in the mid–twentieth century
(see storms from Rochester, 1966; Syracuse, 1958;
and Albany, 1958). Because of parking problems in
the past, the cities studied have instituted large fines,
strong enforcement, and consistent parking regulations. For example, Syracuse drivers may park on
only one side of the street based on whether the date
is odd or even; the actual weather is irrelevant. As a
result of these parking policy changes, problems with
parked cars are less substantial today, although they
are still significant.
Motorists also disrupt snow removal efforts.
Whether cars are moving, idling, stalled, or abandoned, they slow traffic f low—including snow
plows. In worst-case scenarios, traffic can become
completely gridlocked (e.g., Buffalo Gridlock Monday
2000 storm). Nonetheless, traffic today seems to have
a less visible effect than it did in the past. For example,
commuters’ return to work following major storms
in the 1950s and 1960s caused massive traffic jams in
every city for every storm studied during this time.
Return-to-work traffic jams do not occur today in
the case-study cities. The cause for this change is
unclear; however, the author believes it is probably a
combination of population declines, job losses, and
improvements in parking regulation.
Finally, the general public influences snow events
simply by choosing whether or not to remain at home.
People staying home during the Blizzard of 1993
have been credited with assisting mitigation efforts
(Albany Times-Union, 15 March 1993). On the other
hand, police blamed violators of a driving ban for
slowing down cleanup and removal operations following the January 1985 storm in Buffalo (Buffalo
News, 23 January 1985), and sightseers from the suburbs hindered operations following the 1969 storms
in Albany (Albany Times-Union, 30 December 1969).
Finally, looters caused an extra burden by distracting
police (who often are needed to ticket illegally parked
cars prior to towing) following storms in Syracuse
(1966) and Buffalo (1977 and 1985).
METEOROLOGISTS AND THE MEDIA .
Although meteorologists and the media do not control the weather, drive snowplows, or have a large
collective effect as do members of the general public,
they do have an indirect influence on snow events by
altering the behavior of the government and general
public. Meteorologists influence behavior by issuing
forecasts and warnings; the media influence behavior
through their coverage of storm preparations, the
storm itself, and storm aftermath. It should be noted
that since many meteorologists are employed by
media companies their influences are intertwined;
furthermore, additional media that do not employ
or contract meteorologists also disseminate weather
information.
The power of meteorologists to affect behavior
has grown with time. For the case study storms that
occurred before approximately 1980, it was rare to see
an newspaper article about the storm before it actually began. Since then, however, newspaper articles
warning of an impending storm or discussing storm
preparations have become commonplace. Poststorm
evaluations of governmental response and meteorological forecasts are not uncommon either.
Meteorologists affect governmental response
by providing warnings in advance of a snowstorm.
When a well-forecast storm strikes a well-prepared
city, the streets may have been presalted, plows are
gassed up and ready to go, and city workers are on the
AMERICAN METEOROLOGICAL SOCIETY
job. An early example of this was a lake-effect storm
that struck Buffalo in December 1961; the magnitude
of the snow event was minimal. The Buffalo News
credited Weather Service, Inc., with notifying the
Streets Division early enough for adequate preparation (30 December 1961). Similarly, good forecasts
greatly aided snow mitigation operations in Syracuse
during and after the Blizzard of 1993—and this was
despite one of the largest snow totals on record (J.
Wright 2003, personal communication).
Meteorologists also influence actions of the general public. Again, the Blizzard of 1993 is a prime
example of a storm that had a lesser impact because of
a well-prepared general public (Albany Times-Union,
15 March 1993). In Rochester, the February/March
1984 storm, despite occurring during the middle of
the workweek, had a relatively minor impact initially
because of the general public’s preparation (although
problems with no-show contractors ultimately made
this a very disruptive storm).
Conversely, if meteorologists’ forecasts are incorrect, ire descends from the public. For example, an
early December 1977 storm in Syracuse was much
less significant than predicted, prompting the editorial board of the Syracuse Post-Standard to blame
“misleading” weather reports and “fearsome” film
clips for the unnecessary closing of 50 local school
districts (10 December 1977). On the other end of the
spectrum, when a forecasted minor storm became a
major storm on Thursday 4 March 1999, a massive
traffic jam resulted in Rochester.
Corresponding to the rise in meteorologists’ influence is an increase in the general public’s knowledge
and understanding of meteorology. All five meteorologists interviewed agreed with this statement and
cited reasons such as advances in technology (e.g.,
Doppler radar) and easier access to information
through sources such as The Weather Channel® and
World Wide Web. However, they cautioned that
members of the general public have increasingly high
expectations due to promotion and use of such technology. Furthermore, most broadcast meteorologists
interviewed by the author complained of insufficient
on-air time for a complex discussion of weather possibilities and uncertainties. Some speculated that
communication problems may stem from closer to
home due to an in-house disconnect between them
and their respective news departments.
The media also ref lect and inf luence public
opinion. Newspapers use both editorials and news
articles to illustrate their belief in the ability, or
ineptitude, of government. If government fails to
properly mitigate snow, news coverage takes a negaDECEMBER 2005
| 1789
tive tone (some representative case studies include creations— allows us to better understand why
Buffalo 1936 and Rochester 1944). In Albany, persis- similar snowstorms often have dramatically differtent problems with snow removal in late 1969 turned ent impacts on society. Tables 5 and 6 summarize
news coverage from relatively neutral to antagonistic. the factors that affect snow events; Table 5 shows
News headlines best illustrate this change: while factors that directly influence a snow event, while
initial headlines expressed awe at the magnitude of Table 6 shows indirect influences. All factors shown
the snow problem, such as “Wow, We Are Snowed!” in Table 5 are grouped underneath the meta-fac(Albany Times-Union, 23 December 1969), later head- tors of meteorological variations, governmental
lines expressed increasing frustration, such as “Area response, or actions of the general public. Factors in
Remains Paralyzed: Another Storm is on the Way” Table 5 are further separated by whether variations
(ibid., 29 December 1969).
in them generally have a major or minor influence
The combined influence of meteorologists and the in affecting the magnitude of a snow event’s dismedia is most evident in a phenomenon termed by ruption. Several factors not previously discussed,
this author as the mad rush. Mad rushes occur when such as the clearing of fire hydrants and sidewalks,
members of the general public mob grocery and hard- generally had little effect on snow events in the case
ware stores in search of the essentials, such as bread, study cities (Call 2004), however, these may have
milk, toilet paper, and snow shovels, in advance of an more significance in other cities or noncity areas.
impending snowstorm. In this study, the first mad Indeed, budget-related problems can be especially
rushes were observed in the 1990s, suggesting that significant for smaller municipalities, such as towns
better forecasts and media dissemination are at least (ibid., p. 35). Table 6 includes factors relating to the
partially responsible for such behavior. The mad rush metafactor of meteorologists and the media, as well
also varies spatially. Although eight case study storms as several factors that are either relatively constant,
affected Buffalo and Rochester in the 1990s and such as a city’s climate, or difficult to measure, such
2000s, none of them triggered a mad rush. However, as residents’ level of experience. Because of the inmad rushes were common in Albany during this direct nature of the factors in this table, no further
time; mad rushes have also been observed elsewhere grouping was attempted. While climate and city
in the Northeast corridor and also in connection with layout and terrain have a small influence, at least for
hurricanes. Ironically, mad-rush behavior is generally the case study cities (Call 2004), future researchers
unnecessary in regard to snowstorms. With two ex- may wish to determine the importance and relative
ceptions—both in Buffalo—no recent storm has para- influence of the other factors shown in Table 6.
lyzed any portion of any city studied for more than
Two important limitations of this study are its
few days. Although vulnerable groups of the general regional focus and its emphasis on cities. Rooney
public, such as the elderly or residents of rural areas, (1967), for example, found significant regional difcould be stuck in place for several days, it is unlikely ferences between snow events in the Upper Midwest
that most urban and suburban residents could face and those in the western Great Plains, and this study
such a predicament from all but the worst of storms. found significant differences even among four similar
Unanswered questions regarding the spatial variation cities. For example, Buffalo has had the most politiin the mad rush outside of Upstate
New York and more general quesTABLE. 5 Factors directly affecting snow events in the cities studied.
tions about why this behavior
Meteorological
Governmental
Actions of the
occurs suggest a need for further
variations
response
general public
research. Finally, one reviewer of
this paper suggested that the mad
• Parked cars
Major • Amount of snow • Equipment
rush may actually benefit cities
• Motorists
• Intensity of snow • Preparation
by lessening the need for people
• Use of Contractors • Choosing to stay
• Timing
home, or not
to travel during a snowstorm.
• Politics
• Temperature
Future researchers may also want
(snow density)
to examine this supposition.
CONCLUSIONS. Thinking
of snowstorms—meteorological happenings—in the larger
context of snow events—social
1790 |
DECEMBER 2005
Usually • Wind
minor • Duration
• Labor
• Outside aid
• Budget
• Adherence to driving
bans
• Criminal behavior
• Clearing of hydrants
and sidewalks
TABLE 6. Factors indirectly affecting snow events in
the cities studied.
Meteorologists and the
media
• Accuracy of forecasts
• Lead time
• Tone of coverage
• Amount of coverage
Additional factors
• Climate
• City layout and terrain
• Credibility of government,
meteorologists, and media
• Experience with past
events
cally enhanced events, while Rochester has had the
most problems with contractors. Nonetheless, the
author believes that the metafactors introduced here
could be applied to cities elsewhere in the United
States. An awareness of the nonmeteorological factors affecting the impact of snowstorms could be
especially valuable in regions less accustomed to
snow, because the actions of the government, general
public, meteorologists, and media can significantly
reduce the disruption of snow events. Finally, this
study focused on cities, and caution should be used
when applying the findings to suburban or rural areas. Although the author believes that the factors are
transferable, the significance of them changes greatly
because of dramatic differences in the suburban and
rural landscape. For example, suburban areas near
the case study cities have overnight on-street parking
bans, a lower street density, large drainage ditches,
and other differences that greatly aid plows and
lessen problems with cars (Rochester Democrat and
Chronicle, 3 March 1984). In rural areas, wind causes
significant drifting, and residents can be isolated for
long periods, behooving them to purchase extra supplies before a storm.
To end, this study has shown the importance of
considering nonmeteorological factors as well as
meteorological ones when studying snow events.
Whether preparing forecasts and warnings for clients
or the general public, forecasters should consider the
role of both social and meteorological factors and
adjust the tone of their pronouncements accordingly.
Ultimately, improvements in the tone and emphasis of
forecasts will increase the ability of the meteorological community to provide valuable information for
the government and the general public.
ACKNOWLEDGMENTS. I wish to thank Anne
Mosher, Mark Monmonier, and Susan Millar for their guidance and suggestions during this study. Without the honest
and insightful responses of Tom Atkins, Steve Caporizzo,
AMERICAN METEOROLOGICAL SOCIETY
Tom DiVecchio, Don Paul, and Wayne Mahar, the discussion of meteorologists’ influence on snow events would
not have been possible. Similar thanks are in order to the
Commissioners of Snow: Joseph N. Giambra, Paul Noto,
and Jeff Wright. Thanks also to Steve McLaughlin for his
assistance in defining severe snowstorms and perspective
on NWS procedures. Finally, appreciation is extended to
all six reviewers of this article—their comments greatly
strengthened my argument.
APPENDIX: CREATING THE LISTS OF
CASE STUDY STORMS. Creating the lists of
case study storms was a surprisingly problematic task.
Although snow events are determined by much more
than snow amounts, using daily snow data to develop
the lists was easier and faster than more complex
methods [such as those used Branick (1997)]. For
Albany, the local National Weather Service Forecast
Office maintains a list of the largest snowstorms from
1888 through the present on its Web site; this list was
sufficient for this study. For the other three cities, lists
of storms had to be constructed. Some information
was available for Buffalo and Rochester, such as a list
of the greatest 24-h snowfalls since 1980 and a list of
the greatest daily snowfalls since 1943, respectively.
Although these provided a useful starting point, because long duration but less intense storms did not
appear on the lists, the daily snow record also had to
be examined. For Syracuse, no list of any type was
available. Thus, for Buffalo, Rochester, and Syracuse,
simple objective criteria defining a severe snowstorm
had to be created; then these had to be applied to
daily snow accumulation data to create lists of severe
snowstorms.
Unfortunately, no official criteria define severe
snowstorms. Although 6 in. (15.2 cm) of snow is
generally the minimum criterion for heavy snow in
the Northern United States (Changnon 1967; Branick
1997), that amount is fairly common in the case study
region. More problematically, snow falls nearly daily
in lake-effect snow regions during winter, and it is
difficult to tell if snowfall from consecutive days is
from one or multiple snowstorms. Thus, a working
definition was created as follows: at least 1 in. (2.5 cm)
of snow per day with a minimum of 10 in. (25.4 cm)
in total. Because no list of any type was available for
Syracuse, this initial definition was used to create the
list of severe snowstorms shown in Table 3.
After studying these storms, it was discovered
that most long-duration storms with relatively low
amounts per day (such as 14–22 January 1992) had
almost no impact, probably because the snow intensity never strained the city’s capacity. As a result,
DECEMBER 2005
| 1791
the definition of a severe snowstorm—at least by
Buffalo, Rochester, and Syracuse standards—was
revised to include only those storms with at least
3 in. (8 cm) of snow per day and at least 14 in. (36 cm)
in total accumulation.The higher amount of snow
per day was needed to prevent long-duration lowimpact snowstorms (generally lake-effect episodes)
from being considered severe. The requirement for
total accumulation was made to keep the lists of
potential case study storms manageable; as shown in
Tables 1–4, virtually every case study storm featured
at least 18 in. (46 cm) of accumulation. Exceptions
were either studied before the definition was revised
or were secondary storms that occurred close in time
to “top 10” storms.
Finally, the revised definition was applied to
the Syracuse climate data and the new list of severe
snowstorms was compared with the original list (see
Table A1). Not surprisingly, several storms dropped
far in the rankings, while a few did not meet the new
criteria. Although some of these storms were insignificant, the 1994 Syracuse Snowburst and second
February 1958 storm would not have been studied
if the revised definition had been used initially. The
revised rankings belie the major snow events associated with these storms.
In conclusion, because the revised definition
favors shorter but more intense storms, the author
believes it is a superior way to determine severe snowstorms. This is because intense snowstorms, at least in
the case study cities, are often more disruptive than
less intense storms with greater snow accumulations.
Nonetheless, as shown by the rank order changes
of Syracuse storms, significant snow events may be
excluded from study by this definition. It is likely
that some major snow events associated with low-accumulation storms in the other cities may have been
missed in this study. The challenge of determining a
severe snowstorm by using a parameter such as total
snow accumulation, or even snow intensity, underscores a central point of this article: the disruption
caused by a snow event is based on much more than
meteorological parameters.
REFERENCES
Albany (NY) Bureau of Streets Annual Report, 1986:
Proceedings of the Common Council: Vol. 2: Message
of the Mayor and Reports of City Officers. V-B Printing Company. [Available from Albany Public Library,
161 Washington Ave., Albany, NY 12210.]
TABLE A1. Largest storms, ranked from highest to lowest accumulation, that affected Syracuse based on the
original definition of a severe snowstorm as having at least 1 in. (2.5 cm) of snow per day and 10 in. (25.4 cm)
in total accumulation. Revised dates, amounts, and rank based on defining a severe snowstorm as having at
least 3 in. (7.6 cm) per day with at least 14 in. (36 cm) in total. Numbers in italics indicate storms that were
studied but would not have been studied under the revised definition. All amounts are inches.
Year
Original
date(s)
Revised
date(s)
Original
amount
Revised
amount
Original
rank
Revised
rank
Jan–Feb
1966
30–1
30–1
42.3
42.3
1
1
Jan
1994
4–9
4–5
42.2
16.4
2
27
Mar
1993
13–14
13–14
42.0
42.0
3
2
Jan
1992
14–21
18
38.6
19.8
4
14
Mar
1992
11–15
11–14
31.7
30.1
5
4
Feb–Mar
1984
28–3
28–3
30.9
30.9
6
3
Month
Feb
1958
15–20
16–17
29.2
16.9
7
25
Jan
1925
29–30
29–30
27.5
27.5
8
5
Feb
1958
7–10
8–5
25.3
21.1
9
10
Dec
1989
15–17
15–17
25.2*
25.2*
10
6
Dec
1997
30–31
3–31
24.8
24.8
11
7
Dec
1977
5–10
—
24.2
—
12
—
Dec
1991
4–6
4–5
24.0
24.0
13.5
8
Dec
2000
23–28
26–27
24.0
15.5
13.5
29
* Amount based on newspaper reports.
1792 |
DECEMBER 2005
Branick, M. L., 1997: A climatology of significant winter-type weather events in the contiguous United
States, 1982–94. Wea. Forecasting, 12, 193–207.
Cable, M., 1988: The Blizzard of ’88. Atheneum,
197 pp.
Call, D. A. 2004: Urban snow events in Upstate New
York: An integrated human and physical geographical analysis. M.A. thesis, Dept. of Geography, Syracuse University, 120 pp.
Changnon, S. A., Jr., 1969: Climatology of severe winter
storms in Illinois. Illinois State Water Survey Bulletin,
No. 53, 45 pp.
Hart, R. E., and R. H. Grumm, 2001: Using normalized climatological anomalies to rank synopticscale events objectively. Mon. Wea. Rev., 129,
2426–2442.
Kocin, P. J., 1983: An analysis of the “Blizzard of ’88.”
Bull. Amer. Meteor. Soc., 64, 1258–1272.
——, and L. W. Uccellini, 2004a: Northeast Snowstorms. Meteor. Monogr., No. 54, Amer. Meteor.
Soc., 818 pp.
AMERICAN METEOROLOGICAL SOCIETY
——, and ——, 2004b: A snowfall impact scale derived
from Northeast storm snowfall distributions. Bull.
Amer. Meteor. Soc., 85, 177–194.
McKelvey, B., 1995: Snow in the Cities: A History of
America’s Urban Response. University of Rochester
Press, 248 pp.
Moran, V. M., L. A. Abron, and L. W. Weinberger, 1992:
A comparison of conventional and alternative deicers: An environmental impact perspective. Chemical
Deicers and the Environment, F. M. D’Itri, Ed., Lewis
Publishers, Inc., 341–361.
Rooney, J. F., Jr., 1967: The urban snow hazard in the
United States: An appraisal of disruption. Geogr.
Rev., 57, 538–559.
Uccellini, L. W., P. J. Kocin, R. S. Schneider, P. M. Stokols, and R. A. Dorr, 1995: Forecasting the 12–14
March 1993 superstorm. Bull. Amer. Meteor. Soc.,
76, 183–199.
Zielinski, G. A., 2002: A classification scheme for winter
storms in the eastern and central United States with
an emphasis on Nor’easters. Bull. Amer. Meteor. Soc.,
83, 37–51.
DECEMBER 2005
| 1793