3. Abstract
High Impact Winter Weather Events (HIWWE) cause disruptions across various sectors.
This project examined the climatology of certain winter temperature thresholds associated with
HIWWE for major transport hubs across the contiguous United States. Using temperature data
gathered from National Weather Service (NWS) firstorder stations (FOS), daily probabilities
and time series were created. The motivation of this project lies in the potential to assist
Riskpulse decision makers to improve transportationrelated winter weather forecasting.
Improved HIWWE forecasting will help the transportation sector plan for, and avoid these
extreme events. This in turn will help to avoid delays or damage to the goods and their
transportation means. The results for the temperature study show that winter temperatures break
thresholds (i.e 10ºF, 0ºF, 9ºF) more frequently in the northern U.S., especially farther inland.
Coastal areas experience lower probabilities of exceedence. The timing of the peak probabilities
shifts from early January east of the Rockies to late January in the western Great Lakes.
Temporal analysis of temperature show a decreasing trend for most U.S. locations that
experience colder air. On the other hand, results for HIWWE disruptive surface conditions reveal
a need for a much more indepth understanding of what constitutes a “highimpact” winter
weather event in terms of transportation disruptions at specific locations. Forecasting HIWWE
disruptive surface conditions is difficult because of several nonmeteorological factors that affect
transportation disruptions across the transport hubs. During major El Niños years, most stations
experienced fewer cold days.
2
5. Acknowledgements
Foremost, we would like to express our sincere gratitude to our advisor, Dr. David
Changnon, for giving us insightful suggestions and comments, for encouraging us with so much
enthusiasm, and for his guidance throughout the duration of this study. Our sincere thanks also
goes to the National Weather Services offices who took the time to read our emails and in return,
took the time to respond back to us with insightful and descriptive responses that were utilized in
this study. Last but not least, we would like to thank Mark Russo and Jon Davis from Riskpulse,
for supplying this project idea and for allowing us to work alongside them as part of our time for
this research project.
4
7. I. Introduction
Winter weather often presents a significant number of hazards to the transportation
sector, but some of the hazards are less known to the general public. While most individuals
associate heavy snow, ice storms, and blizzard conditions with transportation issues, businesses
must also consider the issue of temperatures affecting the quality of the goods being transported.
Riskpulse produces short and long term forecasts for clients, particularly those in the agricultural
and energy industries. They have presented the challenge of studying the effects of snow and
cold temperatures as it pertains to the transportation sector, and to generate a climatology of
these events to assist in future forecasting. High Impact Winter Weather Events (HIWWE)
generate all forms of hazards for the logistics industry and a climatology of these events can
assist Riskpulse in improving their forecasts.
A. Background and Literature Review
i. Impact analyses
There are many ways that winter events can impact transportation systems, particularly
the ability to transport commercial goods. Most impact analyses focus on road and rail surface
conditions that suppress the ability of transportation means to function. Most literature on winter
weather hazards focus solely on surface conditions causing transportation disruptions. HIWWE
affects both the transportation mean and the goods that are being delivered.
6
8.
Fig. 1.1. An Amtrak blasts through a large snow drift, which can be potentially dangerous to rail logistics
(Bloomington Pantagraph)
The immediate effect of winter weather on surface conditions is treacherous roadways
and railways. The main concern of decision makers is the immediate disruption of transportation
that winter precipitation causes. These disruptions can be felt through various sources, such as
highway, air, and rail delays, of which the worst can be considered a traffic “paralysis” (Rooney
1967). An example of “paralyzed” traffic can be seen on the 22nd and 23rd of December 2004,
after a winter storm that tracked over the Ohio River Valley dropped 29 inches of snow in
localized areas. This winter storm also brought traffic to a standstill, especially because holiday
travels yielded much greater traffic during those two days (Changnon and Changnon 2005).
Other disruptions that occur from heavy snow include ice accumulations within the wheels and
the braking system of vehicles, hindering their ability to regulate speed (Changnon 2006). Winter
weather also causes accidents in both roadways and railways.
7
10. While snow can make transportation difficult or even impossible given enough
accumulation within a given time span, ice accumulations make transportation treacherous even
quicker. When liquid rain falls through temperatures below freezing, it solidifies on contact with
surfaces that are below freezing. This freezing rain, even if the accumulations are minor, can
quickly cause traffic accidents and casualties (Rauber et al. 2001). With enough snowfall or
prolonged cold temperatures, freezing of railroad switches can occur (Changnon 2006).
Figure 1.4. Two crew members dig out a rail switch that froze switches closed. This problem was caused by snow,
ice, and freezing temperatures (Changnon, 2006)
9
12. impact the quality of goods. By improving forecasts of extreme cold outbreaks, damage to
goods, that are susceptible to these extreme temperatures, can be avoided.
ii. Intensity Scales and Climatological Analyses
General climatologies for winter weather events have been studied. From a storm
intensity standpoint, several variables can be assessed for climatological frequencies.
Additionally, over a range of years, winter storms can be put on a probability distribution, and
objective thresholds of storm intensity can be calculated with respect to deviations from the
normal distribution mean. For example, Zielenski (2002) developed a classification scheme for
winter storms using intensity and duration data of Nor’easter winter storm systems. The intensity
was measured by central pressure difference, pressure gradients, time rates of pressure change,
and the duration was measured by storm velocity. This classification scheme is similar to the
SaffirSimpson Hurricane Wind Scale (SSHWS) because both classify storms by intensity.
Although these scales are convenient for classifying the storm itself, it is not an accurate measure
for storm impacts upon a location because storm intensity only addresses the potential impact
that the storm has on different locations.
On the other hand, Rooney (1967) developed a classification scheme based on the
magnitude of a storm’s impact or disruption, particularly from snow hazards. First order
disruptions are considered “paralyzing,” which include the following within a city: Stalled
traffic; major civic, cultural, and athletic event cancellations or postponements; closure of
schools, retail establishments, and factories; power and communication failures (Rooney 1967).
The intensities diminish with higher order disruptions until the fifth order “minimal” disruptions,
which receive no press coverage for any affected city activities (Rooney 1967). This scale places
11
13. the focus outside of the storm itself, categorizing the disruptions it causes regardless of storm
intensity.
Other scales incorporate both disruption indices and storm intensity to provide
forecasting insight on potentially disruptive storms. For example, Kocin and Uccellini (2004)
developed the Northeastern Storm Intensity Scale (NESIS), a synoptic scale intensity index
based on the storm snowfall and estimated population of affected areas. On a more localized
scale than the NESIS, Cerruti and Decker (2011) formulated the Local Winter Storm Scale
(LWSS) based off of winds, snow and ice accumulations, and low visibilities with respect to
climatological means of the local area. This potential disruption, matched with local
susceptibility to winter hazards, can estimate the “realized disruption” of the area (Cerruti and
Decker 2011). Indices like the NESIS and the LWSS are useful tools in predicting the potential
disruption of HIWWE to specific areas.
Another winter weather aspect that can potentially have damaging impacts is extreme
cold temperatures. Cold outbreaks are primarily focused in the northern half of the United States,
since winters are generally colder in the higher latitudes. Most cold temperatures are addressed
in terms of health and safety because those affected tend to fall victim to the cold through
conditions such as hypothermia (Spencer 2009). Extreme cold temperatures are generally
associated with respect to normal distributions (e.g. means, record highs, and record lows) as
they relate to how a population is susceptible to extreme cold differ between climate regions.
iii. Other Considerations
There are other considerations to make while gathering climatological data for HIWWE.
First, with the current quality of weather observations, it is difficult to create an accurate measure
12
14. of freezing rain accumulations using National Weather Service (NWS) firstorder stations (FOS);
a close alternative to FOS observations are forensic analyses of ice damage in the aftermath of an
event (Changnon 2004a).
Second, in addition to the health and safety effects of extreme cold outbreaks, it is
important for businesses to consider how cold temperatures can affect the quality of the products
that they are shipping. These products will most likely have a set temperature tolerance, which
will not change even in different climate regions.
B. Main Focus of Study
This study will analyze two aspects of HIWWE: Precipitation and temperature. Winter
precipitation will affect surface conditions and subsequently, the ability for transportation means
to enter or exit major transport hubs. Understanding the precipitation climatology for these hubs
can help determine the probability that transportation means will not be able to enter or exit the
region. Extreme winter temperatures will also be analyzed due to their nature to impact the
quality of the goods being transported. The goal of analyzing these temperature and precipitation
climatologies is to assist decision makers in preventing delays or loss of goods. In other words, if
HIWWE are expected in an area, decision makers should not authorize transportation through
that area due to high risk of the transportation mean and potential damage to the goods being
transported. HIWWE affects individuals in various ways; this study on the effects of HIWWE on
the transportation sector is only a piece of the winter weather spectrum.
13
15. II. Approach
FOS observed temperature data were collected from the Midwestern Regional Climate
Center (MRCC) for the period of the study (1950 2014). Observed temperature data were then
fed into a program which analyzed the daily maximum and minimum temperatures for the period
of record to test for the set temperature thresholds. This program then used the information to
generate a probability table and a time series table for the individual station.
Missing values in the data were identified and substituted using other nearby FOS data
and cooperative observer (COOP) station data. Stations that contained a considerable amount of
missing data were mainly found in the central United States in this study. In Detroit, Michigan,
DET was used to substitute missing data from the DTW station from October 1, 1950, through
November 30, 1958, because it was the closest station with highquality data. For Kansas City,
MCI also contains missing data from October 1, 1950, through September 30, 1972, as well as
between May 31, 1996, through October 1, 1996. Therefore, data from the MKC station filled in
the gaps of missing data for MCI. In Chicago, Illinois, Midway 3SW was used to fill in missing
data from for MDW from September 2, 1980, through April 30, 2015.
The selection of the stations for this project was primarily based on regional population,
however station selection also considered the importance of major transportation and rail hubs in
the area. A list of 36 stations are selected for this project (Appendix A) and are shown in Figure
2.1. Collaborations with Riskpulse helped to fine tune the selections and identify the temperature
thresholds. While most stations are susceptible to HIWWE, others are not commonly known to
14
16. be impacted by winter weather. These stations are still included as this primary focus of this
study is on the overall impact of winter weather across the CONUS.
After selecting the 36 stations, email messages were sent to the National Weather Service
office that forecasted for the region of interest. The email was a query to ask for expert
knowledge as it pertains to what defines a high impact winter weather event for the given region.
The primary focus of the email was to determine a general 24hour total snowfall amount that
dictates a HIWEE for the given location.
Figure 2.1. A map of the 36 selected stations for this study
A. Winter Temperatures
Temperature threshold values that were considered after receiving insight from Riskpulse
are the following: 9˚F, 0˚F, and 10˚F. Exposure to temperatures within these thresholds affect
the quality of goods (e.g. Wishbone dressing freezing and exploding at 9˚F, or Budweiser beer
bottles exploding below 10˚F) in a manner that would be unacceptable to recipients. These
15
17. temperature thresholds mainly focus on how temperatures may affect the cargo being
transported, as opposed to how it may affect transportation means alone since low temperature
extremes are not considered major components that critically hinder rail operations. Issues that
may arise due to cold temperatures are usually resolved in a short time period (Changnon, 2006).
Once the temperature data were collected, they were analyzed to determine a probability
of occurrence for each day in the defined winter season (October 1st through April 30th) that one
of the selected temperature thresholds would be crossed. To assist Riskpulse decision makers, a
time series analysis was performed to see if extreme winter temperatures were changing in
frequency over time.
Additionally, temperature data for strong El Niño winters were analyzed, identifying
correlations between El Niño events and probability of exceeding temperature thresholds, if any.
A major El Niño may indicate lower than average probability of exceedance because previous
major El Niño winters exhibited positive temperature anomalies. This analysis was useful in
producing a general insight in temperatures for the 201516 El Niño winter due to the high
potential of the winter of 201516 being a record setting El Niño winter.
B. Surface Conditions
The other aspect of HIWEE that needed to be analyzed were the effects of adverse
surface conditions that can cause disruptions to the transportation sector. Winter weather that
typically causes surface conditions to deteriorate include heavy snowfall, blizzard conditions,
and ice. Because ice accumulation is extremely difficult to analyze in the climatological record,
this study focused on heavy snow events. The goal of this part of the study was to determine a
16
18. regional threshold that could be used to signify a high impact event for the given station. The
study could have considered the standard National Weather Service definition of 6” of snow in a
24 hour period; however, it was determined that reaching out to the individual NWS offices for
the 36 stations would offer a stronger scientific approach. An inquiry sent to the NWS offices for
this study asked for a daily snowfall intensity (in inches per day) that would cause a major event
for the region.
III. Results
This study analyzed HIWWE for 36 stations throughout the continental United States for
the period of 19502014 (i.e. winters of October 1, 1950 through April 30, 1951, up through
October 1, 2014 through April 30, 2015). For each station, the number of times that daily
observed minimum temperatures experienced a selected threshold of 9ºF, 0ºF, and 10ºF were
determined using the climatological information gathered from the MRCC.
A. Temperature Thresholds and Spatial Patterns
Each of the 36 stations were analyzed independently using a program developed by the
research group. This program ensured that only highquality data were analyzed. After the data
were cleaned, they were analyzed to count the number of times an individual threshold was
crossed for the given station. From these data, a series of maps were created to illustrate spatial
patterns of the number of times a given threshold was crossed for the period of the study.
17
20. experience minimum temperatures at or below the 9ºF mark. Over the Rocky Mountain Range,
contours are less likely to be climatologically accurate as the smallscale topographic differences
impact the frequencies when thresholds are met or exceeded.
Figure 3.3. A map of the United States. Contours show the number of times the 0ºF threshold was crossed for the
study period (19502014).
Figure 3.4. illustrates the
temperature threshold
analysis for Helena, Montana.
The 9ºF threshold was broken
2,546 times, the 0ºF threshold
1,293 times, and the 10ºF
threshold 598 times.
For most U.S. locations, the 0ºF threshold has generally been broken about half as many
times as the 9ºF threshold was broken. In other words, the likelihood of temperatures falling at or
19
23. case with Pittsburgh, Pennsylvania (PIT) (Figure 3.7). An extreme example is Bismarck, North
Dakota (BIS), where the temperature dropped to or below 10ºF a total 1,360 times over the 65
studied winters, an average of about 21 times per year (Figure 3.6).
While frequency analyses of the three temperature thresholds aid forecasters in gaining
an insight of where extreme winter temperatures occur, creating daily probability distribution
charts of the temperature exceeding each thresholds will enable these forecasters to make
specific decisions on avoiding extreme temperatures that may damage certain goods.
For each threshold, the daily probability of exceeding the temperature threshold was
calculated using the daily frequency and the 65year range. This calculation reveals the
probability, based on observation history, that a certain day will observe a certain temperature.
For example, temperatures crossed the 9ºF threshold on January 18th a total of 18 times over the
65year January 18ths between 1951 and 2015 in Cincinnati, Ohio (CVG). This means that based
on the observed temperatures at CVG, there is a 27.69% chance that a given January 8th will
experience temperatures at or below 9ºF (Figure 3.8). This probability analysis was performed
for all cities that observed temperatures breaking 9ºF, 0ºF, or 10ºF thresholds.
Figure 3.8. illustrates the
probability of breaking any of
the three temperature thresholds
for Cincinnati, Ohio.
22
25. exceedance for each threshold. If there were multiple days with the same maximum probability,
the peak day was calculated by taking an average of the days that exceeded the threshold.
Using the calculated peak probability of exceedance, a map was created to cluster data
with similar results. In this case, hubs with similar peak probabilities were clustered (Figure
3.11). Across the U.S., temperatures fall at or below the thresholds most frequently between
January 3rd and February 1st. Most of the central United States, typically along and east of the
Rockies and including the Missouri Valley, experience their peak cold temperatures at the
beginning of a year, within the first two weeks of January. An example of a peak frequency
within the first two weeks of the new year is Helena, MT (Figure 3.9) Areas to the north and east
of this region typically follow afterwards, peaking in mid to late January, as is the case for
Bismarck, ND (Figure 3.10) or Cincinnati, OH (Figure 3.8). A small band of the southcentral
and southeast United States station was found to experience cold temperature maximums in mid
to late January. The Upper Midwest experienced its peak frequency around the end of January.
While some stations experienced maximum values in early February such as Milwaukee, WI,
with its peak frequency on February 4th and Minneapolis, MN, with its peak threshold on
February 1st, several other stations never experienced any extreme winter temperatures below
the 9ºF, 0ºF, or 10ºF thresholds. For this reason, areas outside of the existing contours either
experienced a peak after February 1st or did not experience a peak at all (Figure 3.11).
This analysis in peak probability of exceedance is useful for forecasters to identify when
would statistically be the least ideal day to allow shipments of goods that are susceptible to
extreme cold damage. A peak in cold temperatures on January 20th for a hub, for example, will
24
26. allow decision makers to be more informed in advance to be prepared for potential shipment
delays around January 20th.
Figure 3.11. A map of the United States. Interpolated contour lines show the date at which the three temperature
thresholds were crossed most frequently. Areas outside of the contours peak after February 1st, or not at all.
B. Winter temperature and temporal changes
In addition to spatial trends and probabilities for HIWWE temperatures, temporal
changes in the frequency of these extremes were analyzed over the 65year period. The goal of
the analysis was to determine if the number of days where temperature thresholds occur are
changing over a long period of time and, if so, by how much. A time series analysis for stations
where the 9ºF threshold exceeded 50 counts during the 65year period was developed by
evaluating the winter number of days each year during the 65year period. The analysis was then
analyzed for an average rate of change, peak decades, and statistical significance using r^2
values and tscores of a normal probability distribution.
25
27. One example of a significant decrease is exhibited in Bismarck, ND between 1950 and
2014. Bismarck is losing threshold days over time: After a cool decade between 1955 and 1965,
a decreasing trend began and persisted throughout the analysis period. The trend line analysis for
Bismarck shows a loss of about 1 threshold day every 3.5 years (Figure 3.12). Cities like Helena,
Montana (HLN), Milwaukee, Wisconsin (MKE), and MinneapolisSt. Paul, Minnesota (MSP),
observed a similar trend (see Appendix D).
Figure 3.12. illustrates the
time series of thresholds for
Bismarck, ND. The statistical
rvalue of 0.314 for these
curves shows up to 99%
significance in temporal
trends in frequencies of
broken thresholds, however
there has been a decreasing
trend on average of 1
threshold day every 3.5 years.
On the other hand, Cleveland, OH has generally shown a slower decrease in the number
of threshold days over the period of the study. Similar to other regions in the Midwest, there was
a peak in the threshold counts in the late 1970s followed by a steady decline over the rest of the
analysis period (Figure 3.16). A trend analysis of these curves shows that Cleveland has lost
about one threshold day per 20 years, which is less evident than the trends in Bismarck, ND.
Cleveland observes a high variance in the annual count for breaking thresholds. Localized
maxima indicate the strong variability in the data juxtaposing a weak longrange trend. Cities
like Detroit, Michigan (DTW), Denver, Colorado (DEN), and Pittsburgh, Pennsylvania (PIT),
observed similar weak trends.
26
28.
Figure 3.13. illustrates the
time series of thresholds for
Cleveland, OH. Cleveland
shows a negative trend with
an rvalue of 0.01206 for the
9ºF threshold, which yields
little to no temporal trend in
frequencies of broken
thresholds over time. In other
words, the trend is a
decreasing slope an average
of about 1 threshold day
every 20 years.
It is noteworthy that the hubs with weak, statistically insignificant trends clustered
together: Cleveland, OH, Detroit, MI, Pittsburgh, PA, and Cincinnati, OH. Perhaps the location
and, thus, climate region affects how drastic the longrange changes are. Moreover, strong,
significant trends are clustered as well: Milwaukee, WI, MinneapolisSt. Paul, MN, Bismarck,
ND, and Sioux Falls, SD. Further research is needed to determine what is causing the overall
decrease in frequency across the entire region.
By performing a time series analysis on each of the stations for which there was
sufficient data to do so, decision makers can be better informed of how the probabilities derived
in the earlier section may be changing over time. Almost every station for which the analysis was
performed on saw a decreasing trend in the number of threshold days (Table 3.14). However,
Detroit, MI (DTW) observed a neutral trend, and Kansas City, MO (MCI) observed a positive
trend. As stated earlier, perhaps the neutral trend in Detroit is similar to the statistically
insignificant slow decrease in frequency of exceedance in Cleveland, OH (CLE) because they are
in similar regions. The positive trend in Kansas City, MO (MCI) may be a result of poor data, as
27
29. discussed earlier. Of course, further research must be conducted in order to thoroughly examine
the factors that affect the magnitude of these longrange temporal changes.
Station Trend Peak Decade Rate r2
tscore Statistical
Significance
HLN Decrease 196878 1 day every 5 years 0.0592 1.9909 ~95%
DEN Decrease 195565 1 day every 25 years 0.0121 0.8784 ~80%
SLC Decrease 197080 1 day every 9 years 0.0770 2.2917 ~95%
CLE Decrease 197585 1 day every 20 years 0.000145 0.09573 < 75%
CVG Decrease 197585 1 day every 20 years 0.0109 0.8348 ~80%
PIT Decrease 197585 1 day every 20 years 0.00904 0.7583 ~75%
BIS Decrease 195565 1 day every 3.5 years 0.0986 2.6251 ~99.5%
DTW Neutral 197585 Neutral 0.00462 0.5410 < 75%
FSD Decrease 195565 1 day every 6.5 years 0.0493 1.8072 ~97.5%
ICT Decrease 197585 1 day every 12.5 years 0.0600 2.0058 ~97.5%
IND Decrease 197585 1 day every 16 years 0.0166 1.0325 ~85%
MCI Positive 197585 1 day every 18.5 years 0.0177 1.0651 ~85%
MDW Decrease 197585 1 day every 10 years 0.0350 1.5109 ~95%
MKE Decrease 197585 1 day every 5 years 0.1176 2.8983 ~99.75%
MSP Decrease 197585 1 day every 5 years 0.0784 2.3150 ~99%
OKC Decrease 197585 1 day every 25 years 0.0515 1.8501 ~97.5%
STL Decrease 197585 1 day every 11.5 years 0.0534 1.8845 ~97.5%
OMA Decrease 197585 1 day every 14 years 0.0199 1.1304 ~90%
Table 3.14. contains a list of the 18 stations for which time series analysis were performed on and the trend in the
number of threshold days for those stations. Columns indicate for each hub the trend, peak decade of frequency of
exceedance, the rate at which this trend is occurring, r2
values, tscores, and statistical significance of trend.
Similarities in the threshold counts were analyzed. Most stations saw a period of
significant cold temperatures in the mid to late 1970’s followed by a trend of decreasing
threshold days. Similarly, most stations saw a minima in the early 1980s as well as the early and
late 1990’s. A majority of the stations saw a recent increase in threshold counts in the early
2010’s, which could suggest a largescale control may be affecting temperatures across the
CONUS.
C. Major El Niño Events
Major El NiñoSouthern Oscillation (ENSO / El Niño) events occur about once every
decade and are generally associated with warmer temperatures and lower amounts of snowfall
28
30. for most of the United States (Figure 3.15). This study looked at the effects of major El Niño
years on the number of temperature threshold exceedances for the year in which the event
occurred. Because 2015 is expected to become the largest El Niño year on record, examining
past major El Niño years can provide great insight into what the winter season of 201516 may
hold.
Figure 3.15. Average temperature rankings during El Niño events, adapted from the Climate Prediction Center
(CPC). Left, average temperature rankings from October through December for El Niño winters 194142, 195758,
196364, 196566, 197273, 198283, 198788, 199192, and 199495. Right, average temperature rankings from
December through February for El Niño winters 194041, 195758, 196566, 197273, 198283, 198687, 198788,
199192, and 199495. Note how El Niño effects are most prevalent inland and over the northeastern part of the
Rocky Mountains and the north central plains.
To analyze the effects of strong El Niño years on the temperature threshold count, the
individual years needed to be identified. To do this, CPC historical records were analyzed to
determine the years with the strongest El Niños. The records determined that the winters of
195758, 197273, 198283, 199192, and 199798 contained the highest values of El Niño sea
surface temperature anomalies, with 198283 being the largest on record prior to 2015. To
analyze the effects of the major El Niño years on the temperature thresholds, a localized analysis
was performed on the time series data by examining the five with major El Niño events to test
for local minima (), maxima (+), or neutral (0) counts of temperature thresholds (Table 3.16).
29
31. While El Niño years typically bring warmer temperatures and therefore a lower amount
of threshold crossing (63 of 90 stations, it is not the only variable in forecasting a season. The
winter of 197273 is a peculiar example of how a forecast can fail even when there was certainty
for a strong El Niño winter. Numerous stations actually saw temperature threshold maxima for
that season.
Station 195758 197273 198283 199192 199798
Helena, MT 0 0
Denver, CO + 0
Salt Lake City, UT + 0
Cleveland, OH 0
Cincinnati, OH + 0
Pittsburgh, PA 0 + +
Bismarck. ND 0
Detroit, MI 0
Sioux Falls, SD
Wichita, KS 0
Indianapolis, IN 0
Kansas City, MO 0 +
Chicago, IL 0
Milwaukee, WI 0
Minneapolis, MN 0
Oklahoma City, OK 0 0
St. Louis, MO 0 0
Omaha, NE 0 +
Table 3.16. contains a listing of the stations where time series analysis were performed and shows how major El
Niño years impacted the total amount of temperature thresholds crossed. Red squares indicate years where a
localized threshold minima occurred, blue squares indicate a localized maxima, and white squares indicate a neutral
case where no minima or maxima was found.
A possible answer to how the 197273 winter was different lies within the North Atlantic
Oscillation (NAO). For the United States, the NAO is the other major contributor for seasonal
variation of weather events. Unlike ENSO that contains warm and cold phases, the NAO
contains positive and negative phases, each with short term modifications to the weather for the
United States. The NAO typically reverses these phases numerous times during a given year,
30
32. however, in some cases, it can remain in a specific phase for an extended period of time,
affecting the teleconnection range of other periodic climate oscillations like ENSO.
To determine if the NAO had any potential effect on the winter temperature anomalies
for the winter of 197273, the CPC NAO historical records were analyzed to determine the
changes of phase (or lack thereof) in NAO for the given winter season.
Season NAO Phase Changes Table 3.17. describes changes in the NAO phase for
winter seasons for each major El Niño event
1957 58 Changed from negative to positive
1972 73 Continually positive throughout season
1982 83 Changed from negative to positive
1991 92 Reverses numerous times during season
1997 98 Reverses numerous times during season
The winter season of 197273 showed a continually positive NAO phase during a strong
El Niño event (Table 3.17). Positive NAO events cause regional low pressure systems to develop
in the northern Atlantic Ocean and in turn, this can cause a persistent trough pattern to develop
over regions of the Northern Hemisphere. Since the NAO remained positive throughout the
entire season, the trough pattern could have persisted throughout a long period of the season
causing a cold air outbreak across regions of the United States. It still remains difficult to be
certain that a continually positive NAO can override a strong El Niño event, but interactions
between positive NAO and strong El Niño events should be explored further.
Even with the NAO phase in mind, most stations in 197273 still saw a warmer than
average season. The winter of 198283 was the second strongest El Niño analyzed, and all
analyzed stations recorded a significant minimum with regards to thresholds. The strongest El
Niño year on record (199798) recorded a similar pattern, however some stations saw a neutral
31
33. pattern with regards to threshold counts. This points to the important notion of how El Niño is
only one of many potential seasonal controls that need to be studied in the future. Given the
historical record of major El Niño events however, it can be assumed that 201516 winter season
will likely see a significant minimum in winter temperature thresholds being broken. Though El
Niño typically controls the temperatures during major years, it should still be noted that outlier
seasons are still possible during these seasons due to other teleconnections (197273, Table
3.16).
D. Surface conditions and NWS insights
Useful input from several National Weather Service Offices was taken into consideration
when evaluating the surface conditions that would be deemed a High Impact Winter Weather
Event. Societal impacts as well as the complexity of determining local criteria for each hub was
also discussed. Due to the complexity of winter storms, many offices also expressed how
difficult it is to determine exactly what would be considered a HIWWE for the specific areas.
The New York City NWS stated that most criteria for impact are set up by NWS
warnings. In the New York metropolitan area and most areas, 2 inches is determined to be a
"plowable" snow and schools tend to close at 6 inches.The factor that is difficult to predict that
has the biggest impact is response of the society. For example, if there were numerous cars on
the road at the same time as was seen in Atlanta, disaster can result. This has occurred in many
other parts of the country including New York. “A snow that begins at 2:00 pm and comes down
heavy often results in gridlock” which causes transportation delays.
The NWS in Oklahoma City expressed that the impacts from snowfall in the OKC area
“differ with each storm”. “These differences can be due to the physical aspects of a winter
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34. weather event (i.e. temperature, ground temperature...etc) or even time of day/day of week.”
Typically, 4 inches of snow would be seen as a "Winter Storm" which would result in highend
impacts. “However, under certain circumstances, 13 inch snowfalls could also result in
highend impacts”. It is also obvious that ice is a major winter weather hazard in the OKC area.
“Generally, 1/4 inch ice accumulation is considered highend, but transportation can be impacted
with less”. OKC NWS also mentioned some factors to consider which include: wind, previous
weather conditions, ground temperature, other winter weather hazards (e.g.freezing rain/drizzle,
sleet). A"Winter Storm Warning" is issued for 6 inches of snow accumulation in a 24hour
period, but serious disruption can occur with much less. This southerly location “causes snow to
mix with sleet or freezing rain/drizzle far more often than not”. Additionally, “the general
windiness of this area causes issues with drifting and blowing snow when temperatures are low
enough for the precipitation to fall mostly as snow. The opposite effect occurs when ground
temperatures are relatively warm, as it can cause quite a lot of rapid snow melt on highways,
while grassy areas see accumulation”. Generally speaking, any snow accumulation causes some
transportation difficulties in the area since snow is uncommon enough that local residents are not
well skilled in driving on the roads in these conditions and tend to slow down significantly (or
slide off the roads). “However, to cause major transportation disruption usually requires more on
the order of 6 to 8 inches of new accumulation in 24 hours”.
The NWS in Albuquerque, New Mexico states that “disruptions are all about impacts,
and our diverse terrain contributes to a varying range of impacts during the winter season….our
forecast warning area is the largest in the CONUS and extends to the Arizona, Colorado, and
Texas borders for the northern twothirds of New Mexico”. Travel on the two major interstates,
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35. I40 and I25, is often impacted during winter events, though infrastructure can play a greater
role than the weather. “Widespread freezing precipitation is rare in the area, although the
foothills of the Albuquerque metro area can be locally impacted by frozen precipitation when
east gaps winds associated with arctic air masses east of our central mountains usher in cold air.
Ice on only a small portion of Interstate 40 can result in fairly significant local impacts and
would not be identified using data from ABQ. Ice is possible but not very common across the
eastern plains closer to Texas”. Dense fog during winter events, can also cause transportation
disruptions, “generally from Raton to Las Vegas on I25 and near Clines Corners on I40.
Otherwise, snow and blowing snow is the most common winter element to cause transportation
disruptions”. The NWS office in Albuquerque is currently making some minor revisions to their
winter weather advisory and warning criteria but also considering that a different criteria based
on terrain in the past was maintained. “Generally, high terrain zones need 810 inches for a
warning while lower terrain need 46 in. for a warning. Interstates are not located in higher
terrain zones except for the area between Raton and the Colorado border on I25, the continental
divide area between Gallup and Grants on I40, and Tijeras Pass just east of Albuquerque on
I40”. Also, becoming aware that a shift to focus warnings on frequency of events, from common
to rare to historic, is being considered. “This can be difficult in New Mexico due to a paucity of
data on which to base the statistics but certainly needs to be considered. Snow in Albuquerque is
most likely during the cooler overnight hours, so 12 inches of snow falling just prior to the
morning rush hour can have impacts comparable to 24 in. events. Across the entire region,
especially the eastern plains, blowing snow with accumulations of 24 inches, can be more
hazardous than wet events with higher accumulations”. Speaking in societal terms, the biggest
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36. criteria in when focusing on the impacts on transportation is related to the low population
densities away from the few "metro" areas. “For storms impacting western zones and/or the
continental divide, it is not uncommon for I40 to be closed between Gallup and Albuquerque, as
there are very few hotel rooms between these two locations. Similarly, when the eastern plains
are being impacted, it is not uncommon for I40 to be closed between Albuquerque and
Amarillo, TX for the same reason. Likewise, I25 can be closed between Santa Fe or Las Vegas
(NM) and Raton”. Here, conditions are often more severe along this stretch, but a lack of hotel
rooms also contributes to closures.
The NWS office located in Boston, MA indicated that a "major event" in the Boston area
would be one where snowfall exceeds 6 inches, but “significant disruption for lower snowfall
amounts that coincide with peak travel times (morning and evening rush hours, typically 6 to 10
am and 3 to 7 pm)” is also considered. “In some cases, as little as 1 to 3 inches falling at the peak
of rush hour caused significant impacts”. Another factor to consider in terms of transportation is
the consistency of the snow. Also, “powdery (dry) snow has less of an impact on road treatment
as compared to wet snow”.
The NWS office in Phoenix, AZ, alike other locations that do not see much winter
weather, conveyed that any accumulation of winter weather would create problems for
transportation and would be then deemed as a “HIWWE”. The same story goes for New,
Orleans, LA. The NWS here also states that any accumulation of snow or ice creates a major
travel disruption since there is a lack of snow plows or salt trucks in the region and “many
elevated highways and bridges due to the swampy and coastal nature of the region”. There is no
way to leave the city of New Orleans and consequently the port of New Orleans without crossing
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37. over a bridge or major body of water. The entire region shuts down for any type of winter
weather event, as the bridges freeze over and create extremely hazardous driving conditions”.
Additionally, “all of the major highways and roads out of the city are closed when any snow or
ice is forecast to accumulate”. Fortunately, “rail traffic is the least impacted since the tracks are
rarely covered with enough snow or ice to cause significant issues”. “Snow and ice would be
considered an extreme event in this region as it is very rare’.
In regards to Denver, CO, the NWS states that the “impact is the snowfall intensities or
snowfall rates per hour, even more so than maybe a overall "bigger event" where you get say 12
inches of snow over 24 hours”. CDOT/airports can usually handle these lower intensity
snowfalls when they are less than 1 inch snow per hour. “Typically, when we see snowfall rates
of 1 inch of snow or greater in less than an hour, then there will be impacts on the transportation
system”. Snow impacts become more extreme when wind is added, especially across roadways
where wind may be blowing perpendicular to the roads at speeds of greater than 10 kts. Snow
plows are not able to keep up when when intensity and wind is added. What was seen in the
“past several years to be the biggest impacts is an intense, yet shorter duration snow event”,
especially if this event occurs during a rush hour period (e.g.46pm). “Roadways may be
initially wet with solar heating, then there is rapid cooling with night fall and snowfall where
roadway surfaces freeze very quickly and become a glaze of ice. So even a minimal snowfall
event of 12 inches over a 2 hour period could cause a transportation nightmare on highways and
roads as they become iced over and snowpacked”. “For an intense event, or for their highest
level impacts at DIA (when they issue a snow emergency, meaning they max out at 104 pieces of
snow removal equipment), this equals greater than 10 inches of snow and/or winds greater than
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38. 25 kts in a 6 hour period”. ‘This is not to say that we have had some historical snowfall events
which have crippled portions of the city, with the most notable events in March of 2003 and
December of 1982. The March 2003 storm was more of a heavy wet snow where impacts were
roof collapses, power outages, and mountain avalanches. The 1982 storm was more
characterized by strong winds up to 51 mph and intense snowfall for 17 consecutive hours at
Stapleton Airport, which closed the airport and paralyzed the transportation system”.
The Wichita, KS, NWS office identified that the most important factor in winter weather
affecting traffic is the timing of the snow. However, behaviors of the driver also play a role in
how traffic can be impacted. Winter storm watches tend to cause more careless accidents than
winter storm warnings do.
For Kansas City, MO, the timing was also discussed. Snowfall during rush hour is much
more hazardous compared to the weekend. Additionally, the NWS at Kansas City mentioned that
the first snow of the season causes more impacts than the tenth snow event of the season, and
liquidtosnow ratios also determines how much snow can cause massive disruptions. They also
noted that the accuracy and quality of communication of forecasts also plays a role on how each
winter weather event is perceived by the public. Since snowfall is not very common in Kansas
City, a large snowstorm of 6 inches will typically shut the city down in an effort to mitigate
impacts.
The NWS office at Atlanta, GA, did not disclose a specific snowfall rate that causes
transportation impacts. Most of the factors that determine the level of impact, they say, are
nonmeteorological. Freezing rain, incoming solar radiation, precipitation rates and timing were
also discussed in terms of how accumulations occur on the surface. Snowfall impacts are so
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39. complex that the NWS claims that summarizing impacts to simply daily snowfall rates is
“uncomfortable”, more so because most of the impacts are related with the ground rather than the
actual precipitation itself. Atlanta introduced a Canadian opensource road condition model
called the Model of the Environment and Temperature of Roads (METRo) that outputs road
forecasts for a given location. Research on road sensor observations and model forecasts do
exist, and road condition forecasting seems to be in development. The Atlanta NWS office
mentioned several examples of light winter events that caused significant impacts (e.g. the
“SnowJams” of January 12, 1982 and January 28, 2014), as well as extreme events that caused
little to no travel impacts (e.g. March 1, 2009)
The NWS Office in Cleveland, OH began their response in societal terms. They stressed
the fact that “people are not used to driving in snow and an inch of snow could cause a major
disruption in the traffic around the Cleveland area; especially if accidents occur. Ice
accumulation just enough to glaze over the roadway will cause many accidents across the
Cleveland area and cause havoc. But, during the winter season once people are use to the snow,
there is a scenario that can be disastrous for the city”. An example of an event that has happened
in the past would be a lake effect snow band developed over the Cleveland area around 2:00 pm
in the afternoon which rapidly changed into “a large blob of heavy lake effect snow right over
downtown Cleveland”. This phenomenon caused many business to close early and send their
employees home. “Snow was accumulating at about 1 inch an hour and at times, 1 to 2 inches an
hour”. This caused gridlock in and around the city of Cleveland to develop. This occurred since
“people were getting stuck in the snow and snow plows were unable to clear the roads due to all
of the traffic. Some people spent up to 8 hours in their cars because of the gridlock”. “The key
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40. factor is more so about the rate of snow rather than the amount of snow on the ground.” There
were instances where 6 inches of snow fell on the roads but the roads remained passable. In the
above example, “the panic caused by the heavy snow falling inundated the roadways with too
many people trying to drive home at once”. Individuals normally trickle a few at a time out of
the city at different hours but not all at once.
The NWS office in Cincinnati, OH, also described the challenge in interpreting impact
forecasts. Similar to the Atlanta NWS office, the Cincinnati office also mentioned the conditions
of the pavement themselves as the main controls in travel impacts. They listed pavement skin
and subskin temperatures, chemical treatments for roads, and precipitation rates and timing. In
addition to mentioning the occurrence of light winter events causing massive travel disruptions
and extreme winter events causing minimal disruptions, the NWS at Cincinnati distinguished
between widespread impact and localized impact, stating that most injuries occur in localized
impacts. They also mentioned how one inch of snow typically shuts down the city in an effort to
mitigate impacts.
In Portland, OR, the NWS states that “snowfall in the Portland metro area is fairly rare,
but usually highimpact”. The impacts from snowfall rely on the time of day and the day of the
week. An example would be, “a 12 inch snowfall during primary morning or afternoon
commute times has a much bigger impact than the same amount of snow falling on a Sunday
afternoon”. Snowfall of 24 inches usually cause issues in Portland. Most of the snowfall events
in this area are usually followed by a transition to freezing rain and then to rain Therefore, a
mixed precipitation during a big winter storm is fairly common in Portland, OR.
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41. The NWS office in Houston, TX describes any amount of snow or ice can have a
significant impact on transportation if it produces ice on roadways. Most of the main
transportation routes are elevated and are more susceptible to freeze than on surface streets.
When an event a few years ago where roughly one tenth of an inch of freezing drizzle occurred,
about “1000 traffic accidents and numerous road closures of major routes in and around
Houston” took place. HGX uses 1/8 of an inch of freezing rain for the ice/winter storm warning
threshold and 2 inches of snow for the winter storm warning threshold. With all of that aside,
“the impacts can be significant for lesser amounts depending on whether the roads ice up or stay
wet or develop an icy glaze”. In their efforts, “TexDOT pretreats roadways and bridges with
varying degrees of success”. Jeff Evans, the MIC for the HGX NWS, points out that “sea fog can
also have a significant impact on transportation (marine and land based) in the winter and early
spring”. Scenarios were seen where “the moisture content of the air and associated dew point
temperature are high relative to the colder water temperatures and sea for can result”. This
situation can result in shutting down transportation in and out of the Port of Houston and can also
impact road travel.
The NWS in Milwaukee, WI declares that “it is all about timing of when the snow is
expected to fall and weather it is able to stick to the surfaces”. For example: if 2 inches of snow
falls at 6:00 am the consequences are worse than if it falls at 11:00 pm when there are not as
many people on the road. Timothy Halbach, a meteorologist from the office shared his local
study on “using DOT accident rates and weather conditions to compare what conditions tend to
be similar to create our big accident days”. The study showed that in early season snows vs. late
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42. season, “snows tend to have more accidents since people are not as acclimated to driving in the
snow”.
Helena, MT, gets little snow during the winters, but the surrounding mountains receive
much of the snowfall. The NWS office in Helena also mentioned that the daily snowfall is too
broad to capture the impacts that the winter precipitation can cause. Traffic conditions and road
conditions (i.e. how many cars drive through it) can affect how quickly the snow turns to slush
and then to ice. A coating of freezing drizzle can cause just as much impact as a 1218 inch
snowfall event within 24 hours.
The NWS office in Detroit, MI, mentioned that the daily snowfall amount is too broad to
capture the impacts that winter precipitation can cause. Even in Detroit, half an inch of snow can
cause significant disruption. The NWS office mentioned the criteria for issuing winter weather
advisories, including snow advisories for between 3 to 8 inches within 12 hours, 2 to 6 inches
within 8 hours, and watches and warnings for over 8 inches within 12 hours or 6 inches in 8
hours.
The Los Angeles, CA, NWS office mentioned that there is not a lot of snow in the Los
Angeles area. They mentioned significant impacts, if at all, based on the potential impacts of 1
inch for urban areas, 3 inches in Tejon, 6 inches on the mountains, and 3 inches elsewhere per
day.
Extreme variations exist in St. Louis, MO, on what precipitation outputs could be
considered a HIWWE. The NWS in St. Louis did not disclose a specific snowfall rate and
instead listed some of the factors to consider, including timing of the precipitation, the
temperatures of the air and the pavement, any residual pavement treatment for icing, the location
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43. of the roads (i.e. elevated, bridge, on the ground), the accuracy and quality of communication of
the forecast, and the climatology of the area (i.e. how susceptible and/or resilient is a certain
location to winter precipitation?). The NWS office stressed that the intensity of the winter
weather does not mean intense travel impacts. In other words, higher snowfall rates will
generally have a higher potential impact, but the potential impacts reveal virtually nothing about
actual impacts.
Charlotte, SC, issues winter storm watches and warnings for heavy snow of 3 inches
within 12 hours, or 4 inches within 24 hours. The NWS office, however, noted that less snowfall
can cause more travel disruptions.
Defining HIWWE precipitation is an extremely complex process, and it takes a thorough
understanding of a plethora of variables that affect how the precipitation disrupts traffic (Table
3.33). Precipitation types and timing, location and resilience to winter weather were some of the
most common factors that the NWS considered. In short, no matter how accurate a forecast for
winter precipitation may be, that forecast does not correlate to an accurate prediction of traffic
disruptions. For any station, several issues must be adequately addressed by both forecasters and
users, including rush hour, trucking routes, type of shipping/trucks, etc. Forecasting traffic
disruptions becomes more of a social science than a physical science.
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44.
Physical Factors Social Factors
● Precipitation type (rain, snow, etc.)
● Precipitation duration
● Precipitation intensity
● Horizontal visibility
● Wind
● Temperature
● How much snow/ice already on ground
● Traffic conditions
● Road quality
● Trucking schedules
● Weather resiliency at or between hubs
● Holiday? Major event?
● Road types (bridge? underpass?)
● Power outages? Communication lapses?
Table 3.18. Physical and social factors that may affect transportation means as mentioned by the NWS offices.
Factors such as precipitation type can be forecasted, but human factors like traffic conditions and resilience to
weather has a greater effect on disruptions and is also more difficult to quantify.
IV. Discussions
Reflecting on the NWS comments on what determines HIWWE, the biggest challenge for
this project was determining the precipitation thresholds for what is considered a HIWWE that
disrupted surface transport. As discussed, the NWS emails revealed no consensus in what daily
snowfall will be considered “high impact” for many of the hubs. A thin coating of ice during
rush hour before the holidays will disrupt traffic more than two inches of snow late Sunday
night. While local winter storm scales like that of Cerruti and Becker (2011) incorporate factors
that determine the perceived intensity of the event, such as precipitation timing, rates, and type,
winter weather susceptibility and resilience of a population, wind conditions, and temperature
changes, they only provide so much information on direct impacts because these factors only
describe the potential impact of the winter weather event. A general daily snowfall rate, in
addition to using daily snowfall observations, will only output extremely crude data on snowfall
across the CONUS.
There is no one type of precipitation that determines the issue that is brought by HIWWE.
This complex issue should be addressed by the users themselves as opposed to climatologists and
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45. meteorologists. Issues with transporting commodities, whether examining harsh surface
conditions or analyzing extreme temperatures that may affect the quality of the produce, are
variable in every situation.
A much more indepth understanding of HIWWE at a specific location will be needed if
the project were to provide a conclusive presentation of the climatology of HIWWE
precipitations. There are two ways, therefore, to improve the data: (1) determining more detailed
thresholds than simply a daily snowfall rate, and (2) using hourly observations instead of daily
snowfall observations.
Snowfall rate is just as rudimentary a measure for snowfall impacts as the SaffirSimpson
Hurricane Wind Scale (SSHWS) is a measure for hurricane impacts. Factors outside of snowfall
rates affect the impact it has on a location. While these factors will need to be determined for a
good summary of what a HIWWE can be considered, a better source may be from the companies
themselves. If companies keep a record on why a certain transport was cancelled, the history of
cancelled or delayed shipments due to the weather can determine the winter precipitation rate,
type, and timing that tend to wreak havoc on the transportation sector the most for any given
location. These findings can be used to determine a set HIWWE surface transportation criteria
that tends to cause the most disruption.
V. Conclusions
This study investigated two aspects of HIWWE: temperatures and surface
conditions.Extreme temperatures (10°F, 0°F, 9°F) impact the quality of products and goods
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46. being transported in order to assist decision makers in preventing the loss of goods at specific
locations for the 36 U.S. locations. Winter precipitation affects the conditions of road surfaces or
rail surfaces, which affects the ability of transportation means to leave and enter cities.
Specifically, decision makers can use temperature climatology to avoid potential damage
to the goods being transported. How often daily temperatures broke these thresholds depended
on a number of climate controls, primarily latitude and proximity to waters. By expanding the
daily frequency of these cold temperatures time of the seasons temperatures tended to break
thresholds most frequently, and climate controls such as topography, latitude, and wind patterns
affected the timing of these extreme winter temperatures. Additionally, forecasters can use
climatology of winter precipitation for similar decision making. However, the complexity of how
different social factors affects the magnitude of traffic disruptions makes producing a thorough
climatology of highimpact winter precipitation very difficult.
Temperature thresholds were broken more frequently in the northern part of the
contiguous U.S. This trend is consistent with the fact that latitude is a major climate control that
affects the temperature distribution throughout the winter season. The probability of exceedance
was greater farther inland, which justifies the continentality and proximity to ocean as a climate
control. Peaks of threshold days were generally concentrated in January, but a few stations
experienced peaks in February, and some did not experience any exceedance of thresholds
whatsoever (e.g. Phoenix, New Orleans).
Temporal trends of HIWWE temperatures were also analyzed. In general, transport hubs
generally exhibited decreasing trends on the order of one fewer day each decade. Most stations
averaged a loss of about one threshold day every 10 years, while other rates were as fast as a day
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47. every 3.5 years. While the causes for these changes can be discussed in a future project, these
results on temporal trends can be used to identify any possible longterm changes in extreme
winter temperatures.
The relationship between major El Niño events and the frequency at which temperatures
reached or exceeded each of the three thresholds was determined for the stations where time
series analysis were completed. Most stations experienced a localized threshold minimum during
El Niño years, meaning that the frequency of exceedance was lower than that of the winters
before and after. The winter of 197273 proved to be an outlier for usual El Niño years, with a
large number of stations showing a localized maximum in threshold counts instead of the
expected minimum. A possible cause of this was the deviation of the NAO for the winter of
197273. NAO remained in a positive phase throughout the 197273 winter season, which may
suggest that NAO affects the ability of ENSO anomalies to impact temperatures over the
CONUS. However, it was stipulated that the upcoming winter season (201516), possibly the
strongest to date, would have a significant decrease in threshold counts based on the prior record
setting El Niño years (199798, 198283).
With the focus on winter temperatures for the purpose of the project, these findings are
important for Riskpulse because data on winter temperature extremes are hardly readily available
except for in the medical field. While hypothermia can affect consumers, extreme low
temperatures can affect goods which the consumers may want. This research topic is also unique
in that temperature threshold with respect to the quality of goods are based on the company’s
experiences, rather than the summaries or insights of physical science experts. While the project
explored precipitation probabilities, exact data cannot be produced using crude methods of
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48. scientific summaries of what a HIWWE is considered. Similar to the method to determine
temperature thresholds, precipitation with respect to surface conditions should also be based on
the company’s experiences rather than the summaries or insights of the NWS. In essence,
requesting histories of when winter weather disrupted trucking/railroad operations may help
identify the circumstances associated with the transportation disruptions.
VI. Recommendations for Future Work
Surface conditions and NWS responses that of, however, provided no consensus on how
much snowfall in general can be considered a HIWWE. There are too many other factors to
consider, including precipitation type, timing, and rates. Other factors include physical
geography and topography, social factors (e.g. special events, traffic/road conditions, and
socioeconomic differences). These factors make the HIWWE precipitation too complex to
perform data analysis of simply the daily snowfall record, and forecasters sometimes simply
determine the local precipitation thresholds or criteria on a casebycase basis. Perhaps what
causes a ‘traffic nightmare’ is up to the transportation logistics to determine. Again, since there is
no one type of precipitation that determines the issue that is brought by HIWWE, the complex
issue could be addressed to the users themselves because they will have a better understanding
on how the conditions that will negatively affect the quality of their produce. Future work may
involve a more narrow examination on how HIWWE may affect each user at a specific location
in order to produce more accurate forecasts.
Hubs that displayed longrange temporal changes can be studied to determine the
potential causes for the decreasing trend. While the most speculated cause is climate change,
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49. other factors (Major El Niño years) may play an important role in explaining these temporal
changes.
Additionally, El Niño effects are far weaker in the East Coast as opposed to the Midwest
region. While climate controls may be the driving factor, a detailed study is needed to determine
what causes this difference in spatial patterns.
Finally, as noted during the section on the seasonal variation of El Niño years, a study
that looks at the interaction of strong El Niño years with years in which the NAO remains
continually positive may help to explain the anomalies that were found in the winter of 197273.
Exploring additional teleconnections and their representative effects on the studied region may
also help to answer the questions generated by the winter of 197273.
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