Climatology of High Impact Winter Weather Events
for U.S. Transport Hubs
Dominique Watson
Robert Fritzen
Kai Funahashi
Riskpulse

Winter weather and its impacts
Snowstorms/ice storms
Arctic temperatures
Transportation disruptions
Health and safety
Themes

Winter Weather Scales
Scales measuring storm intensity
Scales measuring disruptions
Hybrids
Most useful in forecasting to avoid disruptions

<T, P>: Temperature and Precipitation
Ability of transportation means to move
Surface conditions (e.g. rail switches, bridges, etc.) (Changnon 2006)
Functionality (e.g. braking system, etc.)
Quality of goods affected by winter extremes
TWO aspects to focus

(Illinois State Water Survey)
(Changnon, 2006)

(Changnon, 2006)
(KMOX)

High Impact Winter Weather Event
Constitutes winter precipitation and temperature events
that cause “major” disruption to transportation means
and goods being shipped
HIWWE

(Changnon, 2006)
(Changnon, 2006)

Data & Methodology
Major CONUS transport hubs (36 count)
Population
Railroad and highway access
Review and approval by Riskpulse
Emails to NWS for local expertise on HIWWE situations

Determining HIWWE Probabilities
How many times do FOS observations fall within the
temperature and precipitation thresholds?
Daily counts
Monthly counts
Seasonal time series

Temperature thresholds
Quality of goods considered (examples…)
Finalized thresholds: –10ºF, 0ºF, 9ºF
Precipitation thresholds—variable per hub
Dependent on location, time, precip type, location and climate
Thresholds

Temperature Analyses
Contour frequencies
Cluster analyses: peak probability of exceedance
Temporal trends using annual timeseries
Explore El Niño Southern Oscillation (ENSO) teleconnections
NAO influence?
Winter Precipitation
What to Analyze

RESULTS
Temperature Analysis
Temporal Trends
Major El Niño Events
Winter Precipitation
(NBC)

Temperature Analysis
● 9ºF: 2,546 Times
● 0ºF: 1,293 Times
● –10ºF: 598 Times
● Can map spatial
patterns with GIS for
each section!

Map of times the 9ºF threshold was broken

Map of times the 0ºF threshold was broken

Map of times the –10ºF threshold was broken

Probability Analysis
● Locate peak in probability of
exceedance
● Cluster similar peaks on map

Spatial Analysis of Probability Maxima
● Locate and chart peak
probability of exceedance
● Most hubs experience peak
probability of exceedance in
January.
● Areas outside the contours
experience a peak in February,
or not at all.

Temporal Changes
● Time series plotted for hubs
with total exceedance > 50
times between 1950-2014.
● Identify trends with time series
plots
● Statistical tests can analyze the
significance of these changes.
○ Linear Regression and r-
values
○ T-score for significance test

Temporal Changes
Station Trend Rate
HLN – 1 day every 5 years
DEN – 1 day every 25 years
SLC – 1 day every 9 years
CLE – 1 day every 20 years
CVG – 1 day every 20 years
PIT – 1 day every 20 years
BIS – 1 day every 3.5 years
DTW 0 Neutral
FSD – 1 day every 6.5 years
Station Trend Rate
ICT – 1 day every 12.5 years
IND – 1 day every 16 years
MCI + 1 day every 18.5 years
MDW – 1 day every 10 years
MKE – 1 day every 5 years
MSP – 1 day every 5 years
OKC – 1 day every 25 years
STL – 1 day every 11.5 years
OMA – 1 day every 14 years

Effects of Major El Niño’s
Correlation between major El Niño and overall reduction in the
amount of threshold days in a given winter.
Any extremes in frequency of exceedance during major El Niño
winters?
Predict 2015-16 winter?

Major El Niño Teleconnections
Station 57-58 72-73 82-83 91-92 97-98
HLN – 0 – 0 –
DEN – + – – 0
SLC – + – – 0
CLE 0 – – – –
CVG + 0 – – –
PIT 0 + – + –
BIS – – – – 0
DTW 0 – – – –
FSD – – – – –
Station 57-58 72-73 82-83 91-92 97-98
ICT – 0 – – –
IND 0 – – – –
MCI 0 + – – –
MDW 0 – – – –
MKE 0 – – – –
MSP – 0 – – –
OKC – 0 – – 0
STL 0 – – 0 –
OMA 0 + – – –

Several local maxima experienced in 1972-73
NAO a possible influence?
1972 - 1973: NAO remained positive during the entire
season, other seasons cycled phases.
2015 – 2016 Winter?
Expected strongest El Niño on record.
Possible warmer temperatures, lower threshold day
Major El Niño Teleconnections
Season NAO Phase Changes
1957 - 1958 Changed from negative to
positive
1972 - 1973 Continually positive
throughout season
1982 - 1983 Changed from negative to
positive
1991 - 1992 Reverses numerous times
during season
1997 - 1998 Reverses numerous times
during season

Too many factors to develop an exact definition of HIWWE:
Socioeconomic factors
Timing
Location
Precipitation type (snowfall, ice, freezing rain, etc…)
Surface Condition Analysis

NWS Feedback
No general consensus on daily snowfall rates
Daily analysis a poor temporal resolution
Plethora of variables to consider:Physical Factors Social Factors
● Precipitation type (rain, snow, etc.)
● Precipitation duration
● Precipitation intensity
● Horizontal visibility
● Wind
● Temperature
● Traffic conditions
● Road quality
● Trucking schedules
● Weather conditions between hubs
● Holiday? Major event?
● Road types (bridge? underpass?)

● Challenges determining precipitation thresholds
○ Need a more in-depth understanding of HIWWE for a specific location
● What should be done?
○ Direct contact with users!
● Ways to improve data:
○ Determine a more detailed threshold than simply a daily snowfall rate
Discussion

Temperature
Probability of exceedance depends on climate controls
Long-term slow decrease in frequency of threshold days
Major El Niño decreases threshold day count (Winter 2015-16 Forecast)
Surface Conditions
Lack of consensus based on NWS feedback
Conclusions

Future Work?
Considerations of Strong El Niño and positive NAO years
Focused study of ice as another aspect of HIWWE
Hourly obs vs. daily obs
HIWWE impact on consumers vs impact on sellers
Individual customers
Economical effects on small-scale businesses
More details about impact of temperatures on other goods

References
MRCC, cli-MATE. Midwestern Regional Climate Center.
[Available online at http://mrcc.isws.illinois.edu/CLIMATE/]
Cerruti, B.J., and S.G. Decker, 2011: The Local Winter Storm Scale: A Measure of the Intrinsic Ability of Winter Storms to Disrupt Society,
Bulletin of the American Meteorological Society, 92, 721-737.
Changnon, S.A., 2004: Characteristics of Ice Storms in the United States, Journal of Applied Meteorology, 42, 630-639.
—, and D. Changnon, 2005: The Pre-Christmas 2004 Snowstorm Disaster in the Ohio River Valley. Champaign: Illinois State Water Survey.
—, 2006: Railroads and Weather. Boston, American Meteorological Society.
Kocin, P.J. and L.W. Uccellini, 2004: A Snowfall Impact Scale Derived From Northeast Storm Snowfall Distributions, Bulletin of American
Meteorological Society, 85, 177-194.
Rauber, R. M., L. S. Olthoff, M. K. Ramamurthy, D. Miller, and K. E. Kunkel, 2001: A Synoptic Weather Pattern and Sounding-Based Climatology
of Freezing Precipitation in the United States East of the Rocky Mountains. Journal of Applied Meteorology, 40, 1724–1747.
Rooney, J.F., 1967: The Urban Snow Hazard in the United States: An Appraisal of Disruption. The Geographical Review, 57, 58-559
Spencer, J.M., 2009: Winter Weather Related Fatalities In The Conterminous United States: An Analysis Of Three Winter Fatality Databases. M.S.
Thesis.
Zielinski, G.A., 2002: A Classification Scheme for Winter Storms in the Eastern and Central United States with an Emphasis on Nor’easters.
Bulletin of the American Meteorological Society, 83, 37-51.

Acknowledgements
Riskpulse Northern
Illinois University
Mark Russo
Department of Geography
Jon Davis
Dr. David Changnon, advisor
Dr. Andrew Krmenec, director
Midwestern Regional Climate Center
cli-MATE Database

Questions?
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