Technical Report 2002-01 

==================================================================================

================================================================================== 

National Climatic Data Center 

 

 The Development 

of a U.S. 

Climatology of 

 Extreme Ice Loads 

 Kathy Jones (CRREL), 

 Ron Thorkildson (BPA), 

 Neal Lott (NCDC)

US Department of Commerce 

NOAA/ NESDIS 

National Climatic Data Center 

Asheville, NC 28801-5696 

December 2002

==================================================================================

==================================================================================
=====================================================================

===================================================================== 

National Climatic Data Center Technical Report No. 2002-01 

The Development of a U.S. Climatology 

Of Extreme Ice Loads

Kathy Jones, Research Physical Scientist, Snow and Ice Branch, Cold Regions 

Research and Engineering Laboratory, Hanover, NH, 

kathleen.f.jones@erdc.usace.army.mil

Ron Thorkildson, Meteorologist, Conductor Design, Bonneville Power 

Administration, Vancouver, WA, rthorkildson@bpa.gov

Neal Lott, Physical Scientist, Data Access Branch, National Climatic Data Center, 

Asheville, NC, neal.lott@noaa.gov

December 2002 

U.S. Dept of Commerce 

National Oceanic and Atmospheric Administration 

National Environmental Satellite Data and Information Service 

National Climatic Data Center 

Asheville, NC 28801-5001

=====================================================================

===================================================================== 
2

Abstract

Ice thickness (and therefore weight) is a key engineering design consideration in the construction 

of many structures which are subject to outdoor weather. This includes most load-bearing 

structures, such as cables, towers, wires, etc. Detailed information for engineers regarding ice 

loads from freezing rain has been sorely lacking, due to a deficiency of site-specific data. 

Therefore, beginning in 1994, a consortium of individuals and government agencies undertook a 

project to produce a U.S. climatology of ice thickness due to freezing rain, in the form of an 

extreme value analysis. This effort, under the auspices of the American Society of Civil 

Engineers, utilized data modeling techniques to develop such a climatology. This report 

describes this effort, along with future plans for further development. 

1) Introduction

In this report we discuss the development of the map of extreme ice loads with concurrent wind 

speeds that is included in the latest revision of American Society of Civil Engineers (ASCE) 

Manual 74 and in ASCE Standard 7. This project is a joint effort of the U.S. Army Cold Regions 

Research and Engineering Laboratory (CRREL), the National Climatic Data Center (NCDC), the 

Air Force Combat Climatology Center (AFCCC), the Bonneville Power Administration (BPA), 

the Regional Climate Centers, and various state climatologists. The map is based on historical 

weather data from hundreds of weather stations operated by the National Weather Service 

(NWS), Department of Defense (DoD), and Federal Aviation Administration (FAA). Equivalent 

uniform radial ice thicknesses on wires perpendicular to the wind direction in past freezing rain 

storms were determined from the data at each weather station using ice accretion models. 

Qualitative damage information was obtained for the storms that appeared to be severe enough to 

damage trees and power lines. This information was used both to check the modeling algorithms 

and to group the stations into superstations for the extreme value analysis. Ice thicknesses for 

long return periods were determined by fitting the generalized Pareto distribution to the sample 

of largest ice thicknesses for each superstation. Wind speeds concurrent with the extreme ice 

thicknesses were also calculated. In the West, from the Rocky Mountains to the Pacific, ice 

thickness zones were extrapolated using qualitative information because extreme ice thicknesses 

have not yet been calculated from the weather data in this region. For application to overhead 

electrical wires, the mapped ice thicknesses are adjusted for return period, height above ground, 

topography, and possibly wire orientation. 

Ice and wind-on-ice loads on electric power transmission lines and communication towers are 

the governing loads (i.e., for engineering design, the key weight factor that must be considered) 

on these structures in much of the United States. For the 1998 revision of ASCE Standard 7 

Minimum Design Loads for Buildings and Other Structures (ASCE 2000), the Ice Load Task 

Committee provided a map of ice thicknesses from freezing rain with concurrent gust speeds for 

a 50-year return period for the eastern half of the country. This Standard is referenced by other 

codes, guidelines and standards, including the National Electrical Safety Code (NESC), ASCE 

Manual 74 Guidelines for Electrical Transmission Lines Structural Loading, and EIA/TIA 222

Structural Standards for Steel Antenna Towers and Antenna Supporting Structures. A revised 
3

ice map is provided in the draft of the 2002 revision of ASCE Standard 7, and for the revision of 

ASCE Manual 74 that is also in progress. The 1998 map was revised based on work done by the 

U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) for the American 

Lifelines Alliance (www.americanlifelinesalliance.org) to analyze weather data in the Plains 

states, discussions with the Oregon State Climatologist about the map for the Pacific Northwest, 

and qualitative damage information from Storm Data (NOAA 1959–present) west of the 

Rockies, excluding the Pacific Northwest. 

CRREL developed software and algorithms for processing historical data from weather stations 

with hourly weather data and 6-hourly or daily precipitation data. AFCCC provides researchers 

at CRREL with the archived data, as part of its support for the US Army. The period of record 

of the electronically archived data typically begins in the late 1940s for long-established NWS 

and military stations and around 1973 for most FAA stations. The hourly weather data and 

precipitation data are then merged into composite observations. (Note that this merging process 

is no longer necessary, with the availability of the Integrated Surface Hourly (ISH) database 

(Lott 2001), which provides a composite of the available data.) If hourly precipitation data are 

not available, accumulated precipitation (e.g., from 6-hourly reports) is prorated to each hour 

based on the type and severity of precipitation. Freezing rain storms are extracted from these 

merged data. The accretion of ice, expressed as an equivalent radial thickness, and wind-on-ice 

loads are modeled for each storm. Both the detailed CRREL ice accretion model (1996b), and 

the sometimes more conservative Simple model (Jones 1996a,b), are used. The CRREL model 

does a heat-balance analysis to determine how much of the freezing precipitation impinging on a 

horizontal cylinder freezes. The Simple model simulates the accretion of ice, assuming that it is 

cold enough that all the precipitation freezes. 

Model results are checked for ice storms with large modeled ice thicknesses using qualitative 

damage information from Climatological Data, National Summary (NOAA 1950–1958) and

Storm Data (NOAA 1959–present) supplemented by contemporaneous newspaper reports. The 

damage reports are also used to determine the footprint of damage to overhead lines, 

telecommunication towers, and trees for each ice storm. 

To generate a long period of record for the extreme value analysis of ice and wind-on-ice loads, 

the weather stations are grouped into superstations. These groupings are based on the frequency 

of ice storms, the distribution of damaging ice storms, topography, proximity to large bodies of 

water, etc. Ice thicknesses and wind-on-ice loads for a 50-year return period are determined 

using the peaks-over-threshold method with the generalized Pareto distribution (Hosking and 

Wallis 1987). This is a three-parameter distribution, which allows for a heavy tail if the data 

warrant. The parameters of the distribution are determined, with a threshold chosen to give an 

occurrence rate of extreme ice thicknesses of up to about 1/year, using the method of probability 

weighted moments (Wang 1991). Wind speeds concurrent with the 50-year ice thicknesses are 

back-calculated using the 50-year wind-on-ice load on a 1-in. wire and the 50-year ice thickness. 

Finally, the ice thicknesses and concurrent gust-on-ice speeds for the superstations are mapped, 

using 0.25-in. increments in ice thickness and 10 mph increments in gust speed. The maps for 

the revision to ASCE Manual 74 are shown in Figure 1. 
4

2) Weather Data in the United States

In the United States, historical weather data are archived at NCDC and AFCCC. Weather data 

are reported and provided by the NWS, the Navy, Army and Air Force, the FAA, and other state 

and federal agencies. At weather stations in the United States, temperatures are considered 

accurate to the nearest 1o

F, wind speeds to the nearest knot, and precipitation amounts to 

hundredths or tenths of an inch, varying over time and from station to station. Temperature is 

archived in tenths of a degree Celsius, wind speeds in tenths of a meter per second, and 

precipitation amounts in millimeters (AFCCC) or hundredths of an inch (NCDC). 

Before the data are archived, they are checked using quality control software to correct any data 

errors that can be automatically corrected and to flag apparent problems that will require a 

manual check of the data. NCDC does a further manual quality control of NWS and Navy 

weather records to check and correct data that were flagged and to fill in missing data elements 

and records if available. AFCCC provides the same level of quality control for the Army and Air 

Force data. Weather data from the FAA and other agencies, along with international data, do not 

go through this highest level of quality control, but are quality-controlled using automated QC 

software. More information on the network of weather stations and data archiving are in Lott 

and Jones (1998) 

For the recent project for the American Lifelines Alliance to extend the mapped region west to 

the Rockies, both AFCCC data and ISH data from NCDC were obtained through AFCCC. The 

ISH data files are merged from both the AFCCC and NCDC archives. They include hourly, 6-

hourly and 24-hourly precipitation amounts, when reported by the station. As earlier data 

(typically pre-1948) are digitized from paper records, they will be merged into the ISH database 

to extend the digital period of record for climatological studies, business interests, and research. 

This will provide a longer period of record for estimating extreme events. In addition, ISH data 

have been processed through additional quality control algorithms to make the data more robust. 

The daily precipitation data are available in a set of Cooperative Summary of the Day CDROMs. 

The period of record for the electronically archived data begins in the late 1940s at many of the 

NWS and military weather stations. However, for a number of years, typically 1965 through 

1972, but sometimes extending into the early 1980’s, weather records were digitally archived 

only every 3 hours, even though hourly measurements were made. The original handwritten 

hourly data are available at NCDC, and are expected to be archived electronically and included 

in the ISH database over the next few years. 

The NWS now uses an Automated Surface Observing System (ASOS), which was phased-in 

nationally during the early to late 1990s. This system is now in use at most hourly reporting 

stations, which are typically at airports. The system provides a continuous datastream, with 

generally reliable data for temperature, wind speed and direction, dew point, pressure, and 

visibility. However, the following elements are somewhat less accurate unless augmented by a 

human observer, which is done occasionally at selected locations: 

• Reports of precipitation type sometimes do not properly show the type of precipitation 

occurring; 
5

• Precipitation amount from freezing or especially frozen precipitation is problematic and 

not always reliable in sleet and snow, but usually reliable for freezing rain; 

• Clouds are reported up to only 12,000 feet at most stations. 

The Rosemount ice sensor has proven to be an excellent addition to the equipment suite at NWS 

and some FAA stations. It reports the occurrence of freezing rain as soon as it begins. It also 

has the capability to report: 

• Freezing drizzle, for which algorithms are now being developed that will be implemented 

in the next couple of years; 

• Ice thickness, for which algorithms have been developed, and are being tested at selected 

stations, to report the actual severity of ice accretion in the hourly observation. 

The ice thickness capability is very promising for the future, in that utilities, aviation interests, 

and others will have access to these data in real time, and the archived data for climatological 

studies. Even without that, the current Rosemount reports of freezing rain have allowed for 

some continuity in the use of the archived data from the pre-ASOS through ASOS eras. 

3) Modeling Ice Accretion in Freezing Rain

The most important parameters in determining ice loads caused by freezing rain from weather 

data are the precipitation rate and wind speed during the freezing rain storm. Unfortunately, 

anemometers and precipitation gauges may be adversely affected by accreted ice, and sometimes 

freezing rain storms cause power outages at weather stations. Thus, the expertise and dedication 

of the weather observers may have a significant effect on the quality of the recorded wind speed 

and precipitation amounts. The quality of measurements has varied over time at individual 

stations and varies from station to station. Furthermore, the quality of the archived data depends 

on the level of quality assurance that was applied prior to archiving. In analyzing the weather 

data to estimate ice loads, it is assumed that the data are correct, with the exception of certain 

known types of errors and shortcomings as mentioned above. 

CRREL and Simple Ice Accretion Models 

The Simple model determines the equivalent uniform radial ice thickness from the amount of 

freezing rain and the wind speed: 

[ ] ( ) (3.6 ) , 1 1/2 2 2

1

j o j j

N

j i

Req =∑ P + V W

=

ρ

ρ π

 (1) 

where 

Pj = precipitation rate (amount in mm in the jth hour) 

ρo = density of water (1 g/cm3) 

ρi = density of glaze ice (0.9 g/cm3) 

Vj = wind speed (m/s) in the jth hour 

Wj = liquid water content (g/m3

) of the rain-filled air in the jth hour = 0.067Pj

0.846 (Best 

1949) 

N = duration of freezing rain storm (hr) 
6

Req does not depend on the air temperature because it is assumed that all the available 

precipitation freezes. Then, because the ice is uniformly thick around the wire, Req does not 

depend on the wire diameter. Note that the liquid water content W is expressed in terms of the 

precipitation rate P, implicitly incorporating a fall speed for the raindrops. The relationship used 

in (1) results in a fall speed VT(m/s) = 4.15P0.154. 

The CRREL model uses a heat-balance calculation to determine how much of the impinging 

precipitation freezes directly to the wire and how much of the runoff water freezes as icicles. If 

it is cold enough and windy enough, the ice loads determined by the CRREL and Simple models 

are the same. However, if the air temperature is near freezing and wind speeds are low, the 

CRREL model calculates smaller ice loads than the Simple model. In those conditions much of 

the impinging precipitation may freeze as icicles and some may drip off without freezing. The 

CRREL model requires the user to specify the diameter of the wire on which the accretion of ice 

is to be modeled; however, the ice thickness is essentially independent of wire diameter. 

The CRREL and Simple models are discussed and compared to each other and to other ice 

accretion models in Jones (1996a). Comparisons of measured and modeled ice loads on three 

test spans in Canada are discussed in Newfoundland and Labrador Hydro et al. (1998). 

Characteristics of a number of models for the accretion of ice in freezing rain that have been 

used by utilities in the United States and Canada to map ice loads are briefly discussed in Jones 

and White (2002). 

Data–Model Interface 

To use historical weather data to determine ice loads, a number of decisions must be made about 

the data that are separate from the model, but affect the results. These include 1) prorating 6-

hourly and 24-hourly precipitation amounts to each hour, 2) deciding how much of the 

precipitation accretes as ice when there are other types of precipitation, such as rain, snow or ice 

pellets, mixed with, or alternating with, freezing rain, 3) correcting the measured wind speed 

from the height above ground of the anemometer to the height of the wire, 4) dealing with wire 

orientation to the wind and variability in wind direction, 5) interpolating the weather data when 

they were archived only every third hour, 6) deciding when a freezing rain storm ends. Each of 

these issues is discussed in this section. 

Prorating Accumulated Precipitation 

The weighting factors used to prorate 6- and 24-hourly precipitation amounts to each hour are 

shown in Table 1. These weights were originally chosen to be the typical precipitation rate in 

mm/hr for each type of precipitation. Table 1 is based on a table provided by Tsoi Yip of 

Environment Canada (EC). The Canadian table was originally provided in an unpublished report 

for EC in August 1984. The main difference between Table 1 and the Canadian version is the 

larger weighting factor for moderate freezing rain, equal to that for moderate rain, here. 

However, this difference is unlikely to be significant because precipitation in ice storms is often 

described as light or moderate freezing drizzle or light freezing rain. 
7

Table 1. Weighting factors for prorating 6- and 24-hourly precipitation amounts 

 Precipitation 

 Intensity/type 

Rain Rain 

showers 

Drizzle Freezing 

rain 

Freezing 

drizzle 

Snow Snow 

grains 

Ice 

pellets

Snow 

showers 

Snow 

pellets 

Hail

 Light 1.8 1.8 0.1 1.8 0.1 0.6 0 1.8 0.6 0.6 1.8

 Moderate 5.1 5.1 0.3 5.1 0.3 1.3 1.3 1.3 5.1

 Heavy 13. 0.8 2.5 

The weight assigned to each hour in the weather record is determined by the present weather 

codes for the hour, with the weight set to zero if there is no precipitation. For example, if the 

only type of precipitation reported in an hour is light freezing rain, the weighting factor for that 

hour is 1.8. If in the next hour moderate freezing drizzle is reported with light snow, the 

weighting factor is (0.3+0.6)/2=0.45. The fraction of the accumulated precipitation attributed to 

each hour is the weighting factor for the hour divided by the sum of weighting factors for the 6 

or 24 hours in which precipitation accumulated. This fraction is then multiplied by the 

accumulated amount to obtain the estimated hourly precipitation amount. Continuing the 

example above, assume the accumulated precipitation amount in a six-hour period is 4.5 mm. 

Freezing rain was observed in 1 of these 6 hours and moderate freezing drizzle with light snow 

was reported in the next hour. The weighting factors for the 4 hours without precipitation is 

zero, so the sum of the weighting factors for the 6-hour period is 1.8+0.45=2.25. In the hour 

with freezing rain, 1.8/2.25=80% of the precipitation (3.6 mm) is assumed to have occurred, and 

the next hour with freezing drizzle and snow has 0.45/2.25=20%, or 0.9 mm, of precipitation. 

These precipitation amounts are then used in (1) in estimating the increment in equivalent radial 

ice thickness for those hours. 

Mixed Precipitation Types 

In freezing rain storms, the type of precipitation varies from hour to hour, and, in any hour, often 

two or even three types of precipitation will be noted. In the CRREL algorithm, there is no 

further subdivision of the prorated hourly precipitation amounts. Instead it is assumed that all 

the precipitation in an hour in which freezing rain falls accretes to the wire as if it were all 

freezing rain. The models are also allowed to accrete precipitation that was described as rain or 

drizzle (not freezing) if the air temperature is below freezing. These procedures are intentionally 

conservative. They allow the modeled ice loads to represent the possibly more severe conditions 

in the vicinity of the weather station, where all the precipitation is freezing rain rather than the 

mixture of precipitation types observed at the weather station, or where convective and 

evaporative cooling are slightly greater than at the weather station. This choice also expresses a 

reluctance to further subdivide the precipitation amounts based on weighting factors that at best 

are correct on average, but cannot represent the mix of varying precipitation types in an hour, of 

which the observers provide only a glimpse in their once per hour observations of the 

precipitation type.

In both the CRREL and Simple models, ice loads may be determined for two cases: 1) allowing 

ice to accrete only in hours in which the precipitation type is reported as freezing rain or a 

combination of freezing rain and other types of precipitation, and 2) allowing ice to accrete also 

in hours in which the precipitation type is ice pellets. Freezing rain and ice pellets occur in the 
8

precipitation-type transition region of winter storms (Stewart 1992), which typically is bounded 

by snow on one side and rain on the other. Freezing rain and ice pellets develop in the same 

meteorological conditions, namely a layer of warm air over a layer of cold air. Snowflakes, 

formed in clouds above the layer of warm air, melt as they fall through the warm air. These 

water drops then cool while falling through the layer of cold air below. For the right 

combinations of cold and warm layer thicknesses and temperatures, the raindrops may supercool 

in the cold air layer, but remain liquid and ultimately freeze on impact with a structure. 

However, there are two scenarios in which the precipitation falls as ice pellets rather than 

freezing rain: 1) if the cold air layer is thick enough and cold enough, the rain drops freeze 

partially or entirely, forming ice pellets, and 2) if the warm air layer aloft is relatively thin or 

cold, the snowflakes may not melt completely before falling into the cold air layer. In the first 

case, structures at higher elevations or high enough above ground may be in freezing rain while 

ice pellets are observed at weather stations. The CRREL ice storm team observed this in a storm 

in February 1996 in Tennessee where freezing rain damaged trees and power lines on Lookout 

Mountain, near Chattanooga, while ice pellets were falling at the Chattanooga airport. The 

inclusion of ice pellets in modeling ice loads at weather stations is intended to estimate ice loads 

that may have occurred on structures at sites where freezing rain occurred while ice pellets were 

observed at the weather station. 

Anemometer and Wire Heights Above Ground 

The ice thickness on wires is often calculated at 10 meters above ground, but may be calculated 

at any height. Because wind speed increases with height above ground through the earth’s 

boundary layer, the ice thickness also increases with height, as shown by (1). Thus, it is 

important to know how far above ground the wind speed is measured. The anemometer height at 

any weather station has typically varied over time, and also varies from station to station. The 

rate of increase of wind speed with height depends on the ground cover, the roughness of the 

terrain, and the exposure of the site. In this study the wind speed was assumed to be proportional 

to the 1/7 power of the height, following ASCE Standard 7-93 (1993) for exposure C, which is 

appropriate at these airport weather stations. Thus 

 1/ 7















 =

A

W

W A h

h V V (2)

where VW and VA are the wind speeds at the height above ground of the wire hW and the height 

above ground of the anemometer hA, respectively. This formula provides only an estimate of the 

actual wind profile at any time. 

In addition to this anemometer height correction, a correction for hours in which the recorded 

wind speed is zero is also made. In these hours the wind speed for the previous hour with a nonzero wind speed is used. This is done in case the zero wind is a result of a frozen anemometer. 

Hours that are actually calm are “corrected” erroneously by this procedure, resulting in modeled 

ice thicknesses that are too high. On the other hand, if ice accreting on the anemometer has 

caused erroneously low but non-zero winds for a number of hours, the modeled ice thicknesses 

will be too low. 
9

Wire Orientation and Wind Direction 

Both the CRREL model and the Simple model compute the ice thickness on a wire which 

reorients as necessary so that it is always perpendicular to the wind to give the largest effect of 

wind-blown rain. This assumption is conservative for power lines, particularly for line routes 

that are nearly parallel to the prevailing wind direction in freezing rain storms. To determine the 

variation in ice thickness with orientation, the ice thickness for a wire that is always parallel to 

the wind and for wires with fixed orientations from north ranging from 0o to 150o in 30o

increments are also computed in the Simple model: 

{ } ( ) ( ) 3.6 sin[ ] , 1 1/ 2 2 2

1

ρ θ φ

ρ π = ∑ + − =

j o j j

N

j i

Req P V W (3)

where θ is the wire direction and φ is the wind direction. For the extreme value analysis, only 

the ice thicknesses on wires that are always perpendicular to the wind direction are used. The 

results of (3) can be applied in specific locations or for specific storms to determine the 

variability of ice thickness with wire orientation that is expected. 

Interpolating 3-Hourly Data 

 

At NWS stations, from about 1965 to about 1972 or later, weather data were digitally archived 

only every 3 hours, even though measurements were made every hour. During those years, all 

weather elements were archived electronically at 0000, 0300, 0600, 0900, 1200, 1500, 1800, and 

2100 Universal Coordinate Time (UTC) only. These gaps in the data are dealt with by assuming 

that the weather was the same as the archived hour in the two hours following. For example, the 

wind speed at 0400 and 0500 is assumed equal to the archived wind speed at 0300. However, 

the precipitation totals for the full period are available (i.e., for the entire event), so there is no 

loss of data in regard to precipitation accumulation. Also, with freezing rain generally being 

non-convective, there is less variation in conditions from hour to hour than during many other 

weather events. As a result, we found the level of verification with qualitative information (e.g., 

Storm Data) to be just as high as for events with hourly data available. 

Storm End 

 

An important aspect of pre-processing the weather data before running ice accretion models is 

deciding when a freezing rain storm ends. That choice affects both the maximum wind-on-ice 

load and the maximum ice thickness at the end of the storm. The maximum wind-on-ice load 

may occur following the freezing rain, if a cold front accompanied by higher winds moves into 

the storm area as freezing rain ends. In the models, storms are ended at the first hour after 

freezing rain ends when the air temperature rises above 1o

C. This choice sometimes results in 

ice accreting on top of previously accreted ice that is many days or even weeks old. Ideally, one 

would model the melting and sublimation of accreted ice. However, that process is more 

complex and locally variable than modeling the accretion of ice because melting by direct or 

reflected solar radiation and ice shedding before complete melting are both significant. 
10

4) Damage Information

For the purposes of this qualitative investigation, a storm is defined as beginning when freezing 

rain begins at any station in the study region and continuing as long as freezing rain events 

continue to occur, with an event beginning at one station before one at another station has ended. 

A storm may be as short as a few days or as long as many weeks in regions where cold winters 

are typical. 

Four criteria were checked in choosing the storms to investigate: 

1) At least 13 mm of ice from the CRREL model, accreting freezing rain only, at one or more 

stations; 

2) At least 13 mm of ice from the Simple model, accreting freezing rain only, at one or more 

stations, that is also at least 6 mm more than the CRREL freezing rain only result; 

3) At least 13 mm of ice from the CRREL model, accreting both ice pellets and freezing rain, at 

one or more stations, that is also at least 6 mm more than the CRREL freezing rain only 

result; 

4) At least 13 mm of ice from the Simple model, accreting both ice pellets and freezing rain, at 

one or more stations, that is also at least 6 mm more than the CRREL freezing rain only 

result. 

Storms chosen, based on the first and second criteria, help to determine if the CRREL model 

should be used rather than the Simple model. Those chosen by the third and fourth criteria help 

to determine if freezing rain often occurs in the region while ice pellets are observed at the 

airport sites. 

The storms that are investigated are test storms that may be damaging ice storms. In these 

storms, reports of downed trees and outages in the power distribution system, and perhaps in the 

power transmission system, are expected to have occurred where ice thicknesses are significant. 

However, it must be kept in mind that damage to overhead lines in ice storms is not necessarily 

caused by ice thicknesses exceeding the design value. Typically, most of the damage in ice 

storms is caused by ice-covered branches and trees falling on distribution lines. However, there 

are often reports that the weight of the ice on the wires of power, phone, cable TV, or telegraph 

lines caused outages by breaking wires and poles. High winds, galloping (conductor movement 

causes line to swing in a sine wave pattern), transformer problems, wet ground, frozen switches, 

broken ground wires, structures falling on wires, cars hitting poles, repairs not completed from 

prior storms, prior lightning damage, cracked arresters, defective components, and touching 

wires are sometimes mentioned as causing outages. There may also be regions with significant 

icing where there is little or no damage to trees and power lines. This is most likely to occur 

where there are few trees or an effective hazard-tree program, where the overhead lines are 

designed for relatively high ice loads and there is an effective line maintenance program, or 

where the power distribution system is underground. Thus, information on damage to overhead 

lines provides only an indication of significant icing. 

For each storm, information from Storm Data (NOAA 1959–present) and Climatological Data, 

National Summary (NOAA 1950–1958) is compiled. This information was supplemented by 

newspaper reports from cities where the modeled ice thickness is at least 6 mm. The region in 

which damage to trees, power, phone, telegraph, and cable TV lines was reported is outlined on a 
11

map of each storm. Regions where only slippery roads, school closures, flooding or damaging 

snow accretions were reported are not included in this storm footprint. 

Typically, storms chosen based on the first and second criteria are damaging ice storms, while 

those chosen on the third and fourth criteria are not. However, the information obtained for all 

of these test storms helps in constraining the ice thickness estimates from the models and 

identifying patterns of damaging storms. In addition, based on these investigations, we have 

tightened the criteria for allowing the accretion of ice (Jones 1998), which in turn has reduced 

the differences between the ice thicknesses determined by the CRREL and Simple models in 

some storms. 

5) Extreme Value Analysis

The modeled ice thicknesses at the weather stations were used to estimate ice thicknesses for a 

50-year return period. Both the peaks-over-threshold method (e.g., Hosking and Wallis 1987, 

Wang 1991, Abild et al. 1992, and Simiu and Heckert 1995) and the concept of superstations 

(Peterka 1992) were used in the extreme value analysis. 

Superstations 

The superstation concept is described in Peterka (1992) for extreme wind speeds. The 50-year 

return-period wind map in the 1993 revision of ASCE Standard 7 shows small regions in the 

Midwest with high winds. Peterka argued that these small-scale variations in the extreme wind 

speed were not real but were due to sampling error from determining the parameters of the 

extreme value distribution from relatively short data records. He suggested that the records of 

extreme winds, from different weather stations with the same wind climate, could be appended to 

each other to form a superstation with a much longer period of record. The long period of record 

of a superstation supplies many more extremes to use in the extreme value analysis and thus 

produces better estimates of the parameters of the extreme value distribution. The limitation on 

forming the superstation is the requirement that the maximum annual winds from the different 

stations in the superstation should be uncorrelated. If extreme winds at two stations are 

correlated, then including the second station supplies no new information on the extreme wind 

climate. 

Sampling errors in the estimation of extremes can be significant for the electronic data records of 

weather stations, which vary from less than 20 years up to about 50 years in length. At any 

weather station, the probability that the 50-year return-period ice thickness has occurred 

increases as the period of record increases. However, a large ice thickness with a long return 

period may have occurred at a station with a short period of record, and, conversely, only short 

return-period ice thicknesses may have occurred at a station with a longer period of record. 

Thus, the ice thickness estimate for a 50-year return period, which is calculated from the 

available sample, may change significantly as additional years of data are added to the historical 

record. This variability decreases as the period of record increases. 
12

In grouping stations into superstations, a number of factors were considered, including 1) the 

number of damaging storms at each station, 2) the number of times adjacent stations were in the 

same damaging storm, 3) the frequency of ice storms causing at least 1 mm of ice at each station, 

and 4) station elevation, along with latitude, proximity to water, and relief. A balance was 

sought between grouping only stations likely to have the same severe icing climatology against 

the desire to generate as long a period of record as possible to reduce sampling error. 

Correlation Between Stations 

If the ice thicknesses (and wind on ice) at pairs of stations in a superstation are correlated in 

time, then the concatenation of data from those stations does not supply new information and the 

apparently long period of record for the superstation is not real. However, if the correlation 

between stations is low, the stations are essentially independent. 

The correlation between each pair of stations in each superstation was calculated as follows. For 

each pair, storms that overlapped in time were paired. If the ice thickness for either or both of 

these storms exceeded the threshold value for the superstation, that pair of ice thicknesses was 

included in determining the correlation for the station pair. The sample of extreme ice 

thicknesses is bounded from below by the threshold, so it is not normally distributed. Therefore, 

the non-parametric Spearman rank-order correlation coefficient rs (Press et al. 1987) was used, 

rather than the commonly used Pearson correlation coefficient. The strength of the association 

between stations is given by the square of rs. Stations that were correlated were not 

simultaneously included in the superstation. In this case, two versions of the superstations were 

analyzed, each including one of the correlated stations and the uncorrelated stations. 

 

Peaks-over-threshold Method 

The peaks-over-threshold (POT) method was used to estimate ice thicknesses for long return 

periods. This is different from the epochal method (fitting a distribution, often Gumbel, to the 

annual maxima) and is a better approach for freezing rain storms because: 

• At a given location, freezing rain storms occur infrequently and some winters have no 

measurable freezing rain. In those years the maximum ice thickness is zero, which would 

have to be considered part of the extreme population in the epochal method; 

• In other years there is more than one severe ice storm, each of which may cause larger ice 

thicknesses than the most severe storms in milder years. The epochal method does not 

include these large, but not worst-that-year, ice thicknesses in the estimation of the 

parameters of the extreme value distribution. 

These problems are avoided using the POT method because values are chosen as members of the 

sample population if they exceed a specified threshold. The excess of the value over this 

threshold is used to determine the two additional parameters of the generalized Pareto 

distribution: 

 1 exp 0

 0 ( ) ( ) ( ) 1 1

1 /

= 



 



 − = − −

≠ 



 



 − = ≤ ≥ = − −

 k x u

k k x u F x P X x x u

k

α

α (4) 
13

The threshold is u, the shape parameter is k, and α is the scale parameter. The cases k = 0, k < 0, 

and k > 0 correspond to the extreme value distribution types I (shortest infinite tail), II (longer 

infinite tail), and III (finite tail length, x < α/k). Typically, k ranges between –0.5 and 0.5. 

The method of probability weighted moments (Abild et al. 1992, Wang 1991, Hosking and 

Wallis 1987) was used to determine the distribution parameters k and α. Estimates of the 

distribution parameters (Wang 1991) are provided by: 

∑

= −

− =

∑

=

=

= − +

−

− + =

l

i

x

l

i

l

b

l

i

x

l

b

(b u)( k)

b b

b b u k

i

i

1 1

1 1

1

1

where

1

2

4 3

1 ( )

0 ( )

0

0 1

1 0

α

 (5) 

where the x(i) are the ordered sample, x(1) ≤ x(2) ≤ ... ≤ x(l) of values greater than the threshold u. 

Note that b0 is the mean of the ice thicknesses in the sample and b1 is a weighted mean. 

A variety of methods can be used to define the threshold u. It should be high enough that only 

true extremes are used to estimate the parameters of the distribution, but low enough that there 

are sufficient data so sampling error is not a problem. For determining ice thickness extremes, 

the threshold thickness is chosen so that the occurrence rate of the sample extremes is about 

1/year, except in the southern states where ice storms occur more infrequently. There the 

occurrence rate varied from about 1 in 2 years to 1 in 10 years. 

Once the parameters of the distribution have been determined, the ice thickness xT corresponding 

to a specified return period T is calculated from 

[ ] ( ) k

T T

k

x u − = + − λ α 1 (6) 

where λ is the occurrence rate (number per year) of values exceeding the threshold. 

The estimation of extremes is discussed further in Jones and White (2002). 

Wind-on-ice Speeds 

The amount of ice that accretes on a wire is affected by the speed of the wind that accompanies 

the freezing rain. Wind speeds during freezing rain are typically moderate. However, the ice 

that accretes on a wire may last for days or even weeks after the freezing rain ends, as long the 

weather remains cold. Thus, the ice-laden wires may be exposed to high winds that occur after 

the storm. The wind speeds to use in combination with the extreme ice thicknesses were 

determined from the modeled wind-on-ice loads on a 1-in. wire. 
14

The summary information for each freezing rain storm includes the maximum wind-on-ice load 

that occurred at any time during the storm. The parameters of the distribution of extreme windon-ice loads for the superstations were calculated by the peaks-over-threshold method described 

above. Assuming that the maximum wind-on-ice load in each storm occurs with the maximum 

ice thickness, we can calculate the concurrent wind-on-ice speed VC from the wind-on-ice load 

F50 and the ice thickness Req50 for a 50-year return-period: 

C (D R )

F

V

a D eq

C

50

50

2

2

+ = ρ

, (7) 

where ρa is the density of air, D is the diameter of the bare wire, and the drag coefficient is CD. 

VC is the wind speed that, when used in combination with the 50-year return-period ice thickness, 

gives the 50-year return-period wind-on-ice load. It is used to characterize the 50-year wind load 

rather than using F50 directly because F50 applies only to a 1-in. diameter wire. Furthermore, if 

the concurrent wind speed is calculated for other return periods, it is found to be essentially 

constant. Therefore, the wind speed to be applied concurrently with the extreme ice thickness 

does not increase with return period. 

VC is an hourly wind speed, rather than a 3-s gust speed or a fastest-mile wind speed. It is 

obtained from the 1- or 2-minute average wind speeds that are reported each hour at the weather 

stations. Gust speeds are recorded at most first-order airport weather stations in the United 

States whenever there is a rapid change in wind speed with at least a 10-knot difference between 

the high and low speeds. In a previous study, these gust wind speeds at a number of Army and 

Air Force weather stations were used to calculate the 50-year return-period gust-on-ice speed GC

and the ratio between GC and VC: 

fgust =GC /VC = 1.34. (8) 

f gust was used to estimate GC from VC for each superstation. This concurrent gust-on-ice speed 

GC is mapped in Figure 1 with the 50-year equivalent radial ice thickness Req50. 

6) Ice Thickness Estimates in the Western U.S.

From the Rocky Mountains to the West Coast, excluding the Pacific Northwest, extreme ice 

thicknesses have not yet been calculated from the weather data. In this region ice thickness 

zones with concurrent wind speeds were extrapolated based primarily on information from Storm 

Data (NOAA 1959–present). The information in Storm Data is sorted by state and includes the 

date the storm began, a list of the forecast zones within each state that were affected, and a brief 

narrative describing the storm and the resulting impact. 

The evaluation process began by separating freezing rain storms from the snow and in-cloud 

icing events that can cause the same type of damage. The identification of freezing rain storms 

from the storm description was sometimes difficult, particularly along the eastern slopes of the 
15

Rockies in Colorado and New Mexico where severe in-cloud icing apparently occurs with only a 

small contribution from freezing rain to the ice thickness. 

For each of the freezing rain events that were identified, ice thicknesses and the associated wind 

speed were estimated based on the extent of damage to power and telephone lines and 

communication towers, the financial impact of ice and wind damage, duration of power outages, 

comparisons to previous ice storms, and reported ice thickness and wind speed. After all the ice 

storms were processed, they were plotted on a map by intensity and by location within each state. 

Ice thickness and concurrent wind speed zones were based on this information. Two of the 

authors (Jones and Thorkildson) established these zones independently, discussed differences, 

and ultimately came to an agreement on the zone boundaries. 

Additional guidance for the extrapolations was provided by two papers on freezing rain in the 

United States (Bernstein and Brown 1997 and Robbins and Cortinas 1996). Both of these 

publications characterize the frequency of freezing rain across the United States, but neither 

contains information on the duration, precipitation amounts, or accumulated ice thicknesses. A 

narrative describing the distribution of freezing rain throughout the western United States by 

Kelly Redmond, Deputy Director of the Western Regional Climate Center, was also helpful. 

Finally, climatologists from these states reviewed the map and it was revised based on their 

comments and suggestions. 

7) Site-Specific Issues

A number of factors contribute to differences in the equivalent radial thickness of ice on different 

wires of the same span and wires of different spans in a freezing rain storm. While the amount 

of freezing rain has an obvious effect on the accreted ice thickness, other factors are also 

important. The importance of wind speed and direction, air temperature, and Joule heating are 

discussed in this section. 

Wind-blown rain may contribute significantly to the ice thickness on a structure. The wind flux 

term in (1) is comparable to the falling rain term at a wind speed of about 5 m/s. More ice will 

accrete on wires of spans at windy locations than on wires that are sheltered from the wind, if the 

other conditions are the same. Because wind speed typically increases with height above ground, 

more ice is expected to accrete on ground wires than on the conductors of the same span, and on 

the highest conductor in a vertical configuration than on the lower conductors. On the other 

hand, the ice thickness on wires of spans that are parallel to the wind direction will be less than 

the Simple model estimates in (1) and no increase in ice thickness with height above ground is 

expected. 

The air temperature may vary at any location during an ice storm and across the region affected 

by the storm. At near-freezing temperatures, even small variations in temperature can have a 

significant effect on the fraction of the impinging precipitation that freezes to a wire and on the 

rate of freezing, which controls the shape of the accretion. At relatively higher temperatures, 

icicles may account for a large portion of the accreted ice. At lower temperatures, on the other 

hand, the impinging precipitation tends to freeze where it hits, accumulating in an eccentric 
16

accretion, which would cause torsionally flexible ground-wires and single conductors to rotate, 

with the ice eventually forming a cylindrical sleeve around the wire. At intermediate 

temperatures the impinging water flows before freezing, resulting in thicker ice on the sides or 

bottom of a wire than on the top. The shape of the accreted ice affects the drag coefficient and 

the actual wind-on-ice load. 

Following an ice storm the temperature may remain below freezing longer at higher elevations 

than at the airports where the weather data are recorded. Where it remains cold, structures may 

see higher wind-on-ice loads than are obtained from the concurrent wind speeds on the map in 

Figure 1. Joule heating from the current in conductors may have a significant effect on the 

amount of ice that accretes. For the impinging precipitation to freeze to a conductor, the heat of 

fusion must be removed, typically by convective and evaporative cooling. If sufficient heat is 

generated in the conductor, it will remain ice free. Although this is unlikely, the amount of heat 

generation in the conductor may be enough to decrease the initial rate of freezing and make more 

water available for icicle formation. This larger volume of dripping water affects the aspect ratio 

of the icicles, with long, thin icicles occurring with lower freezing rates and short, fat icicles with 

high freezing rates. The shape of the accretion ultimately affects both the further accretion of ice 

and the rate of ice shedding when the storm ends and the weather warms. 

8) Current Work

CRREL and the Bonneville Power Administration (BPA) are working to remap ice thicknesses 

in the Pacific Northwest. In addition to mapping extreme ice thicknesses from freezing rain, this 

project also includes the testing of an accretion algorithm for in-cloud icing. The map in Figure 

1 is based on detailed maps provided to BPA by Meteorological Research Inc. (MRI) in 1977. 

The map of ice thicknesses from freezing rain that was compiled from these maps for ASCE 

Standard 7-98 (2000) was revised for the current revisions of that standard and ASCE Manual 74 

based on discussions with the Oregon State Climatologist. When MRI originally mapped the 

region, they used weather data through 1964 at most of the stations in the region, a different 

model was used to estimate accreted ice thicknesses than has been used for the rest of the 

country, and extremes were calculated using Weiss (1955), who developed nomograms based on 

the Gumbel fitting method. 

As a follow-on to the project to map extreme ice thicknesses in the Plains states for the American 

Lifelines Alliance, CRREL is analyzing weather data in the rest of the western states and Alaska. 

The in-cloud ice accretion algorithm tested in the Pacific Northwest will be applied in this region 

if it is successful. 

9) Acknowledgments

This work was supported by the Corps of Engineers Engineer Research and Development 

Center, the American Lifelines Alliance (which is a consortium of the Federal Emergency 

Management Agency and the American Society of Civil Engineers), the Electric Power Research 

Institute and its member utilities, Bonneville Power Administration, Vermont Electric Power 
17

Company, the Air Force Combat Climatology Center, the National Climatic Data Center, the 

Regional Climate Centers, and various state climatologists. The review process and 

contributions by all of these agencies and supporters were essential to the success of this project. 

10) References

Abild, J., E.Y. Andersen and L. Rosbjerg (1992) The climate of extreme winds at the Great Belt, 

Denmark, J. of Wind Engineering and Industrial Aerodynamics, 41-44, pp 521-532. 

ASCE (1993) Minimum Design Loads for Buildings and Other Structures, ASCE Standard 7-93, 

American Society of Civil Engineers, New York, 134 pages. 

ASCE (2000) Minimum Design Loads for Buildings and Other Structures, ASCE Standard 7-98, 

American Society of Civil Engineers, Reston, Virginia. 

ASCE (in draft) Minimum Design Loads for Buildings and Other Structures, ASCE Standard 7-

02, American Society of Civil Engineers, Reston, Virginia. 

Bernstein, B.C. and B.G. Brown (1997) A Climatology of Supercooled Large Drop Conditions 

Based Upon Surface Observations and Pilot Reports of Icing, Proceedings 7th Conf. on 

Aviation, Range and Aerospace Meteorology, Long Beach, California, Feb. 2 to 7. 

Best, A.C. (1949) The size distribution of raindrops, Q. J. Royal Met. Soc., 75, pp 16-36. 

Canadian Electrical Association (1998) Report No. ST-331-C Beta Testing of Icing Models, 

CEA, Montreal. 

Hosking, J.R.M., and J.R Wallis (1987) Parameter and quantile estimation for the generalized 

Pareto distribution, Technometrics, 29, no. 3, pp 339-349. 

Jones, K.F. (1996a) A simple model for freezing rain ice loads, Proceedings of the 7th 

International Workshop on Atmospheric Icing of Structures, Chicoutimi, Canada, pp 412-

416. 

Jones, K.F. (1996b) Ice accretion in freezing rain, CRREL Report 96-2, Cold Regions Research 

and Engineering Laboratory, Hanover, New Hampshire. 

Jones, K.F. (1998) Comparison of modeled loads in freezing rain storms with damage 

information, Proceedings of the 8th International Workshop on Atmospheric Icing of 

Structures, June 1998, Iceland. 

Jones, K.F. and H.B. White (2002) The Estimation and Application of Extremes, this 

proceedings. 

Lott, J. N. and K.F. Jones (1998) Using U.S. weather data for modeling ice loads from freezing 

rain, Proceedings of the 8th International Workshop on Atmospheric Icing of Structures, 

June 1998, Iceland. 

Lott, N. R. Baldwin, and P. Jones. NCDC Technical Report 2001-01, The FCC Integrated 

Surface Hourly Database, A New Resource of Global Climate Data. [Asheville, N.C.]: 

National Climatic Data Center, 2001. 

Newfoundland and Labrador Hydro, Ontario Hydro Technologies and École de Technologie 

Supérieure (1998) Validation of ice accretion models for freezing precipitation using 

field data, Canadian Electrical Association, 331 T 992 (A-D). 

NOAA (1950-1958) Climatological Data, National Summary, National Climate Data Center, 

Asheville, NC. 

NOAA (1959-present) Storm Data, National Climate Data Center, Asheville, NC. 
18

Peterka, J.A. (1992) Improved extreme wind prediction for the United States, J. of Wind 

Engineering and Industrial Aerodynamics, 41-44, pp 533-541. 

Press, W.H., B.P. Flannery, S.A. Teukolsky and W.T. Vetterling (1987) Numerical Recipes, 

Cambridge University Press, Cambridge. 

Robbins, C. C. and J. V. Cortinas, Jr. (1996) A climatology of freezing rain in the contiguous 

United States: Preliminary results, Preprints, 15th AMS Conference on Weather 

Analysis and Forecasting, Norfolk, Virginia, August 19-23. 

Simiu, E. and N.A. Heckert (1995) Extreme wind distribution tails: A “Peaks over Threshold” 

Approach, National Institute of Standards and Technology Building Science Series 174, 

U.S. Government Printing Office, Washington D.C., 72 pages. 

Stewart, R.E. (1992) Precipitation types in the transition region of winter storms, Bulletin 

American Meteorological Society, 73, no. 3, pp 287-296. 

Wang, Q.J. (1991) The POT model described by the generalized Pareto distribution with Poisson 

arrival rate, J. of Hydrology, 129, pp 263-280. 

Weiss, L.L. (1955) A nomogram based on the theory of extreme values for determining values 

for various return periods MWR, 83, pp 69-71. 

11) Symbols and Acronyms

CD drag coefficient of ice-covered wire

D diameter of wire 

fgust G50/V50

F(x) cumulative distribution of x 

F50 50-year return period wind-on-ice load on a 1-in. wire 

G50 gust speed equivalent to V50

hA height of anemometer above ground 

hW height of wire above ground 

k shape parameter for generalized Pareto distribution 

N number of hours 

P precipitation rate 

Req equivalent uniform radial ice thickness 

Req50 50-year return period equivalent uniform radial ice thickness 

rs Spearman rank order correlation coefficient 

T return period 

u threshold for generalized Pareto distribution 

V wind speed 

VA wind speed at height of anemometer 

VT terminal velocity of raindrops 

VW wind speed at height of wire

VC 1-min hourly wind speed associated with Req50 and W50

x(i) ith extreme value 

xT T-year return-period value 

W liquid water content 

α scale parameter for generalized Pareto distribution 

λ occurrence rate of extreme loads 
19

π 3.14159 

ρa density of air 

ρi density of glaze ice 

ρo density of water 

AFCCC Air Force Combat Climatology Center 

ASCE American Society for Civil Engineering 

ASOS Automated Surface Observing System 

BPA Bonneville Power Administration 

CRREL Cold Regions Research and Engineering Laboratory 

EC Environment Canada 

FAA Federal Aviation Administration 

ISH Integrated Surface Hourly data 

MRI Meteorological Research, Inc. 

NCDC National Climatic Data Center 

NWS National Weather Service 

POT Peaks over threshold 

UTC Universal Coordinate Time 
20

Figure 1. Uniform radial ice thicknesses due to freezing rain, with concurrent 3-s gust speeds, 

for a 50-year return period a) western United States
21

Figure 1 (cont). Uniform radial ice thicknesses due to freezing rain, with concurrent 3-s gust 

speeds, for a 50-year return period b) eastern United States 
22

Figure 1 (cont). (Referenced above as inset 10-4, from ASCE Manual 74) Uniform radial ice 

thicknesses due to freezing rain, with concurrent 3-s gust speeds, for a 50-year return period c) 

Pacific Northwest 
23

Figure 1 (cont). (Referenced above as inset 10-3, from ASCE Manual 74) Uniform radial ice 

thicknesses due to freezing rain, with concurrent 3-s gust speeds, for a 50-year return period d) 

Lake Superior