Climatology of High Impact Winter Weather Events for

U.S. Transport HubsDominique Watson

Robert FritzenKai 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

Data from first-order stations (FOS) run by the NWS

Provided by the Midwestern Regional Climate Center (MRCC)

Determining HIWWE Probabilities

How many times do FOS observations fall within the temperature and precipitation thresholds?Daily countsMonthly countsSeasonal 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

NWS comments

Thresholds

Temperature AnalysesContour frequenciesCluster analyses: peak probability of exceedance

Temporal trends using annual timeseriesExplore 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 TeleconnectionsStation 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 counts.

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

○ Using hourly observations rather than daily snowfall observations

○ Examine more Wx variables than snow: winds, visibility, ice, etc.

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

Requires specific definition and understanding of HIWWE

Conclusions

Future Work?

Considerations of Strong El Niño and positive NAO yearsFocused study of ice as another aspect of HIWWE

Hourly obs vs. daily obs

HIWWE impact on consumers vs impact on sellersIndividual customersEconomical 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

RiskpulseNorthern Illinois University

Mark RussoDepartment of Geography

Jon DavisDr. David Changnon, advisor

Dr. Andrew Krmenec, director

Midwestern Regional Climate Centercli-MATE Database

Questions?

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