SPECIAL REPORT 95-23

Estimated Snow Parameters for

Vehicle Mobility Modeling in Korea,

Germany and Interior Alaska

Timothy Horrigan and Roy E. Bates September 1995
Abstract

Snow is a crucial factor affecting the U.S. Army’s operations in cold regions.

Values for snow depth and snow density are needed for vehicle mobility studies, but unfortunately the available historical records of these parameters tend

to be relatively sparse. This report deals with the estimation of snow density

and snow depth from readily available parameters such as air temperature

and wind speed. As a basis for further study, the authors have summarized

previous work in three areas of particular interest to the U.S. Army’s vehicle

mobility programs: Korea, Germany and Alaska. Empirical models are presented for estimating snow parameters in these regions.

For conversion of SI units to non-SI units of measurement consult Standard

Practice for Use of the International System of Units (SI), ASTM Standard E380-

93, published by the American Society for Testing and Materials, 1916 Race St.,

Philadelphia, Pa. 19103.

This report is printed on paper that contains a minimum of 50% recycled

material.
Special Report 95-23

Estimated Snow Parameters for

Vehicle Mobility Modeling in Korea,

Germany and Interior Alaska

Timothy Horrigan and Roy E. Bates September 1995

Prepared for

OFFICE OF THE CHIEF OF ENGINEERS

Approved for public release; distribution is unlimited.

US Army Corps

of Engineers

Cold Regions Research &

Engineering Laboratory
PREFACE

This report was prepared by Timothy Horrigan, Physical Science Technician, Geological Sciences Division, Research and Engineering Directorate; and Roy E. Bates,

Meteorologist, Remote Sensing/GIS Center, U.S. Army Cold Regions Research and

Engineering Laboratory, Hanover, New Hampshire. Funding was provided by Office

of the Chief of Engineers under Project 633734T08, Combat Engineering Systems. The

authors thank Austin Hogan and Paul Richmond for their technical reviews. Others

who have made valuable contributions to this study include Michael Bilello, Richard

K. Haugen, Janet P. Hardy, Sally Shoop, Darryl Calkins and David Cate.

The contents of this report are not to be used for advertising or promotional purposes. Citation of brand names does not constitute an official endorsement or approval

of the use of such commercial products.

ii
Estimated Snow Parameters for Vehicle Mobility Modeling

in Korea, Germany and Interior Alaska

TIMOTHY HORRIGAN AND ROY E. BATES

modeling standpoint, since one of the most important objectives of such modeling is to define a

plausible worst-case scenario.)

Snow density was one of several properties of

the snowpack investigated during the study. This

was measured by means of density tubes inserted

into various layers of the snowpack, and a weighted average of the density tube values was used to

represent the overall density of the snowpack.

These weighted-average snow density values do

not take into account the densities of the ice crusts

and ice lenses within the snowpack. However, the

contribution of such ice inclusions to the overall

density should presumably be minor.

The average snow density at the nine test sites

at Fort Greely was 0.233 g⁄cm3, which represents a

medium snow density. The lightest snow tended

to be found early in the season and at sites with

vegetation cover. (Uncovered open areas had

denser snow because of snow drifts from wind

packing.) The lightest measured density of the entire snowpack came on November 28 at the forested site, when the average snow density was only

0.140 g⁄cm3.

The top layer of the deep snowpack is the most

important from a mobility modeling viewpoint

(since vehicles travel over the top of the snow).

The newer snow in the top layer is much lighter

than the older snow in the lower layers. Most toplayer density values were on the order of 0.100

g⁄cm3, but values as low as 0.038 g⁄cm3 and as high

as 0.424 g⁄cm3 were observed.

The worst case for mobility modeling purposes

is most likely to have average snow densities from

approximately 0.350 to 0.500 g/cm3 when traversing upslope. Above 0.500 g/cm3 there would be

little or no vehicle sinkage, and trafficability

INTRODUCTION

Snowfall and snow density are two important

factors for mobility modeling and simulation.

Knowledge of the regional distribution of these

parameters would also contribute materially toward the solution of numerous other winter problems. This report is a result of a request to CRREL

from the Waterways Experiment Station (WES) in

Vicksburg, Mississippi, for realistic estimated values of the regional variation of snow cover. These

values are needed for verifying and validating

WES’s mobility prediction models.

Three areas of the Northern Hemisphere were

selected to test and validate current prediction

algorithms residing within the mobility models:

interior Alaska, central and southern Germany,

and Korea north of the 38th parallel. The snow

parameters needed are the average and worstcase monthly and winter snow density (g/cm3)

and the accumulated snow depth (cm). To accomplish this task, average and worst-case values

were estimated by gleaning results from many

earlier published reports. Much of the data was

selected from a CD-ROM called International Station Meteorological Climate Summary (ISMCS) (U.S.

Navy, U.S. Air Force, U.S. Dept. of Commerce

1992).

INTERIOR ALASKA

The results of a study completed at Fort Greely

by Bilello et al. (1970) are a good starting point for

estimating the snow conditions in interior Alaska.

The terrain and climate at Fort Greely is typical of

interior Alaska in general. This study dealt primarily with field tests conducted in the winter of

1966–67, which was unusually snowy. (The unusually high snowfall is useful from a mobility
Table 2. Average wind speed (m/s) at various Alaska

sites (1948–1990). The data are derived from the International Station Meteorological Climate Summary (U.S.

Navy, U.S. Air Force, Dept. of Commerce 1992).

Nov Dec Jan Feb Mar Apr

Anchorage 2.78 2.68 2.83 3.04 3.04 3.19

Barrow 5.92 5.25 5.35 5.09 5.09 5.20

Bethel 5.35 5.76 5.97 6.28 5.81 5.35

Big Delta 4.37 5.40 5.61 4.73 4.22 3.40

Fairbanks 1.59 1.34 1.39 1.75 2.32 2.83

Kotzebue 6.28 6.02 6.28 5.92 5.30 5.35

would be of little concern. On level ground, snow

density has less effect; the depth of snow is the

major concern in this case.

Snow depths are difficult to characterize, since

they vary greatly according to the microclimate,

microgeography, aspect and vegetation cover

(open or forested). Open sites tend to have lower

snow depths, because the higher winds in open

areas tends to compact the snow more. The snow

in the forested areas is also colder than the snow

in open areas, which means that there is less melting of the outer surfaces of the ice crystals in the

snowpack, slowing down the compaction and settling process.

The greatest snow depth measured in a forested

site at Fort Greely was 116 cm, and the greatest

snow depth at the open site was 50 cm. The Fort

Greely study did not address the issue of drifting,

and the snow depths are determined by taking the

average of snow courses set out over a relatively

wide area. A worst-case value for the maximum

seasonal snow depths at Fort Greely would be

approximately 125 cm. A slightly higher figure

should be used for higher-precipitation areas in

interior Alaska.

The Fort Greely study cites an earlier study by

Bates and Bilello (1966) where an empirical relation between air temperature, wind speed and

snow density was derived:

R = 0.152 – 0.0031T + 0.019W (1)

where R = average seasonal snow cover density

(g/cm3)

T = average seasonal air temperature (°C )

W = average wind speed (m/s).

This equation was taken from measurements for

Alaska, the northern contiguous United States

and Canada. It presumably should be limited to

these relatively cold areas.

We next apply this equation to a few stations in

Alaska. We will assume that the snow season runs

from the beginning of November through the end

of April. The average temperatures at these sites

over the period of record (1948–1990) are shown

in Table 1.

The average wind speeds over the same period

are shown in Table 2. Using these data and eq 1 results in the estimates of average snow density

over the whole season shown in Table 3. Equation

1 can also be used for estimating the average densities for each of the months of the snow season by

estimating the snow density for the entire period

2

Table 1. Average temperature (°C) at various Alaska

sites (1948–1990). The data are derived from the International Station Meteorological Climate Summary (U.S.

Navy, U.S. Air Force, Dept. of Commerce 1992).

Nov Dec Jan Feb Mar Apr

Anchorage –5.6 –8.9 –9.4 –7.8 –3.9 2.2

Barrow –17.8 –23.3 –24.4 –26.7 –25.0 –17.8

Bethel –5.0 –10.0 –10.6 –10.6 –6.1 0.0

Big Delta –13.9 –19.4 –20.0 –16.7 –10.6 –1.1

Fairbanks –15.6 –21.1 –22.8 –19.4 –11.7 –0.6

Kotzebue –12.8 –17.8 –17.8 –19.4 –17.2 –11.1

Table 3. Average seasonal snow density

(g/cm3) at various

Alaska sites calculated using eq 1.

Location Density

Anchorage 0.225

Barrow 0.323

Bethel 0.283

Big Delta 0.282

Fairbanks 0.235

Kotzebue 0.313

Table 4. Cumulative snow density (g/cm3) at

various Alaska sites calculated using eq 1 and

a weighted-average technique.

Nov Dec Jan Feb Mar Apr

Anchorage 0.222 0.230 0.235 0.234 0.222 0.206

Barrow 0.320 0.324 0.329 0.331 0.326 0.306

Bethel 0.269 0.292 0.298 0.304 0.281 0.254

Big Delta 0.278 0.315 0.321 0.294 0.265 0.220

Fairbanks 0.231 0.243 0.249 0.246 0.232 0.207

Kotzebue 0.311 0.321 0.326 0.325 0.306 0.288

from November through the month in question

and then using a weighted-average technique to

remove the previous month’s estimated value

(Table 4).

A somewhat less complicated approach to estimating monthly snow densities is found in a
Nov: R (Nov) = –0.0098Tavg (Nov)+0.090

Dec–Feb: R (month) = –0.010Tavg (month)+0.016 (2)

March: R(Mar) = –0.0048Tavg (Mar) +0.197

3

temperature, wind speed and precipitation are

characterized as high or low.

CENTRAL GERMANY

The snowfall data for Germany are relatively

sparse in the most readily available data sources

(at least for those commonly used by U.S.-based

researchers); for example, only 10 stations in the

Airfield Weather Summary database have complete snow data (U.S. Navy, U.S. Air Force, Dept.

of Commerce 1992). However, there are numerous stations where air temperature data are

available but no snow data. Bates and Bilello

(1988) compensated for this shortage of data by

matching data for the northeastern United States

with that for Germany. Snow and temperature

conditions are quite comparable in these two areas, if the analysis is limited to those areas with a

mean January temperature ≥–8°C and an elevation <600 m.

A linear regression of 45 U.S. stations and 10

German stations (Bates and Bilello 1988) yields

the following relationship between average January temperature and annual total snowfall:

S(Total) = 54.0 – 17.6 T(Jan) (3)

where S(Total) is the total annual snowfall (cm),

and T(Jan) is the average January temperature

(°C).

Snow depth data were available only for the

U.S. cities (Bates and Bilello 1988). When the values for the average maximum snow depth (during an average winter) were regressed against

the January temperature, the following relationship was derived:

D(max) = 14.1 + 5.34 T(Jan) (4)

where D(max) is the average maximum snow

depth (cm).

Bates and Bilello’s (1988) work was applied to

the Hunfelds quadrangle, which is a roughly 22-

×22-km area just west of the border between the

former East Germany and West Germany. The

Hunfelds area has been intensively studied by

WES and other Corps of Engineers laboratories

to determine the environmental factors that affect Army materiel. The estimated January temperatures and other climatic parameters were determined for every grid square within the quadrangle (at 30-m resolution), and these estimated

parameters were used to estimate the snowfall

and snow depth (Fig. 1).

1957 report by Bilello. The equations, which do

not account for wind speed and which are applicable only to locations north of 62°00′N at elevations less than 1500 ft (or approximately 450 m),

are as follows:

where R (month) is the average monthly snow

density (g/cm3), and Tavg (month) is the average

monthly air temperature (°C). These equations

yield the monthly estimated snow density values

shown in Table 5.

A later study by Bilello (1984) examined snow

cover properties in the former Soviet Union by

analyzing published data from 41 locations

where both snow density and wind speed were

measured. The Soviet data proved to be consistent with the North American data in Bilello’s

earlier studies, once an adjustment was made for

the Soviet densitometers, which systematically

gave readings about 25% lower than those obtained by the equivalent measurement instruments used in the U.S.

A recent study by Sturm et al. (in press) proposed a scheme for classifying snow cover into

seven categories. Data collected in the field in

Alaska showed that snow density was the most

powerful statistical criterion for distinguishing

between various types of snow. The goal of

Sturm’s work is to develop techniques for distinguishing between these classes of snow without

an extensive field measurement program, using

readily available information such as satellite

imagery and climatic data. Five of these classes

(excluding ephemeral snow, which is found in

areas with intermittent snow cover, and mountain snow, which is found in mountainous areas

and other cold areas with highly variable snow

cover) are defined by a binary scheme whereby

Table 5. Estimated snow density (g/cm3) at

various Alaska sites calculated using the

equations of Billelo (1957).

Nov Dec Jan Feb Mar

Anchorage 0.144 0.105 0.110 0.094 0.216

Barrow 0.264 0.249 0.260 0.283 0.317

Bethel 0.139 0.116 0.122 0.122 0.226

Big Delta 0.226 0.210 0.216 0.183 0.248

Fairbanks 0.242 0.227 0.244 0.210 0.253

Kotzebue 0.215 0.194 0.194 0.210 0.280
central Germany are 20–30 cm, with midwinter

average snow densities of 0.25–0.30 g/cm3. A

good set of worst-case numbers for central Germany are a minimum snow density of 0.04 g/cm3 and

a maximum snow depth of 60 cm. [The minimum

snow density value is slightly lower than the lowest value obtained in the field mobility tests at

CRREL, reported by Richmond (1993).]

One of the relatively few locations where snowfall and snow depth data are readily available is

Fulda (U.S. Navy, U.S. Air Force, Dept. of Commerce 1992). The average January temperature at

Fulda was approximately 0.3°C over the period of

record (1960–1990). This implies an estimated average maximum snow depth of 15.6 cm. The average of the recorded yearly snow depth maxima

over the same period of record was 12.3 cm, which

means that the algorithm is somewhat inaccurate

in this case, but still close enough to be useful for

initial mobility analysis purposes, in the absence

of further information. Two important parameters

have been left out of this simple algorithm: elevation and wind speed. Fulda’s elevation (301 m) is

relatively high for central Germany (though obviously much lower than the more alpine locations

to the south), as is its average wind speed (5 knots,

or 2.6 m/s).

A long-term frequency distribution for snow

depth at Fulda for 1960–1990 is shown in Table 6.

Even during the three snowiest months of the winter (December through February) there is usually

no measurable snow on the ground at Fulda: for

example, even in the snowiest month, January,

there was no measurable snow on the ground 59%

of the time. The median snow cover when there is

a measurable snow cover is approximately 2–3 in.

(5–8 cm), but the snow depth can get much greater

on occasion. The deepest snow recorded between

1960 and 1990 at Fulda was 46 cm, in both January

and February 1963.

4

Figure 1. Average annual snowfall (cm), Hunfeld

Quadrangle area. (After Causey and West 1990.)

Table 6. Frequency distribution for daily snow depth at Fulda AAF, Germany (50°33′N,

009°39′E, 301 m elev). The data are derived from the Internation Station Meteorological Climate Summary (U.S. Navy, U.S. Air Force, Dept. of Commerce 1992).

Amount

(cm) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

None 44.2 53.2 81.6 98.3 100 100 100 100 100 99.7 84.9 65.3 85.0

Trace 15.1 15.7 11.4 1.6 0.3 8.1 11.5 5.5

0–3 9.0 10.0 2.3 3.5 8.1 2.8

3–5 7.3 7.6 1.7 0.1 1.4 5.1 2.0

6–10 7.2 2.9 1.1 1.5 4.1 1.5

10–15 10.2 6.2 1.1 0.6 4.9 2.0

16–30 6.3 0.8 0.9 0.9 0.8

31–60 0.7 3.6 0.4

Meas. amt. 40.7 31.1 7.0 0.1 7.0 23.2 9.5

No. of obs. 764 713 754 688 687 685 685 700 666 701 714 738 8495

The most obvious limitation of Bates and

Bilello’s approach is that Germany is not the U.S.

However, the German snowfall data do appear to

be similar to the U.S. data, and it seems safe to

assume (in the absence of better data) that snow

values are comparable in the two regions.

An equally important, though slightly less

obvious, limitation is that the climatic data in the

study were collected at open, urban locations. The

microclimatic conditions on the battlefield would

not, of course, necessarily be similar to those

found at the places where the recordings are being

made. Luckily, there are also a number of rural areas in the northeastern U.S. where field data have

been collected. As at Fort Greely, this study has

the additional limitation of not addressing the issue of drifting, which is crucial to vehicle mobility.

Average midwinter average snow depths for
5

Topographically like southeast Ohio

Climate more like western Indiana

Winter as cold as western Labrador

Topographically more rugged than Adirondacks

Summer like eastern New York

38° 38°

KOREA

The situation regarding snow data for Korea is

similar to that for Germany, i.e., snowfall and

snow depth data are mostly unavailable in the

standard climatic data sources. The ISMCS CDROM, for example, has only three stations in

Korea with snowfall data, and two of those stations have short periods of record.

An excellent basic resource for studying the

Korean climate is a very old but still useful study

published by the Environmental Protection Section of the Department of the Army in 1950. This

study points out that “outdoor conditions in

Korea are in most respects like those which would

be encountered in the corresponding places in the

United States.” In general, western Korea is climatically similar to the Mississippi Valley, and the

Korean Pacific coast is similar to the mid-Atlantic

and New England regions (Fig. 2).

A snowpack forms nearly every winter for at

least 30 days in Korea north of 35°N latitude and

at higher elevations south of this latitude. Good

worst-case numbers for vehicle mobility analysis

are similar to those used for Germany or the

northeastern United States, i.e., a maximum snow

depth of 60 cm, a maximum snow density of 0.4

g⁄cm3 and a minimum snow density of 0.04 g⁄cm3.

The three Korean stations in the ISMCS database that have records of snowfall are all in South

Korea. Two of these—Camp Stanley (37°34′N,

127°06′E) and Camp Humphries (36°57′N and

127°02′E)—had periods of records that were less

than ten years long, with a considerable amount

of missing data. Suwon AFB (37°15′N, 127°00′E)

had a long (1951–1988) and relatively complete

record, however. Suwon is located at an elevation of 25 m on the east coast of South Korea,

roughly 50 km south of Seoul. The areas of most

interest are well to the north of Suwon and at

much higher elevations.

The average annual snowfall over the period

of record at Suwon was 36 cm, with an estimated

depth on the ground of 15–20 cm. The average

January temperature was –3.9°C, with an average daily maximum of +1.1°C and an average

daily minimum of –8.9°C. In an average January–February period, the minimum temperatures fell below freezing 56 days out of the total

58. Winter conditions are presumably less severe

here than in most other northern parts of Korea,

including the areas of military interest near the

border between South and North Korea.

The single greatest daily snow depth ever

recorded at Suwon during the period of record

was 38 cm, on 31 January 1969 (occurring shortly

after the record daily snowfall of 26 cm on 28 January 1969.) In an average year the maximum

snow depth was only 8.4 cm.

The median values for the beginning and end

of the snow season over the period of record at

Suwon were as follows:

First snowfall: Nov 25

First measurable snowfall: Dec 6

First measurable snow depth: Dec 22

Last measurable snow depth: Feb 12

Last measurable snowfall: Mar 9

Last snowfall: Mar 24.

The earliest date for the initial formation of the

Figure 2. Areas of the United States with climates and topography

similar to those of Korea. (After U.S. Army 1950.)
6

During its fall to earth, a snow crystal may undergo considerable change. Variation of temperature and humidity with altitude leads to changes in

growth rate and form, and there may even be evaporation or melting of the crystal. Particles may be

“recycled” through some layers by turbulence in

the air and, during windy conditions at the surface,

fragmentation of the more delicate crystal types often occurs.

The character of the surface deposit after a snowfall depends on the form of the crystals and on the

weather conditions during deposition. When there

is no appreciable wind, dry stellar crystals (which

commonly aggregate into large snowflakes) settle

as a soft, fluffy mass whose density is generally less

than 0.1 g/cm3. Very small crystals of simple prismatic form, on the other hand, settle to relatively

high initial densities (say 0. 2 g/cm3) for obvious

geometrical reasons. Snow deposited in wind-free

weather has a smooth surface. When a snowfall is

accompanied by strong winds, crystals are broken

into fragments favorable for close packing, and the

surface of the deposited snow is mechanically agitated by wind shear and by the impact of bounding

particles. This produces high initial density, commonly greater than 0.3 g/cm3, and also leads to the

formation of snow dunes and sastrugi on the surface.

After deposition, snow may be dissipated by

melting and evaporation or it may persist for long

periods. If it persists, it will undergo metamorphism, changing its grain texture and structure and

eventually turning into hard, impermeable ice if it is

part of a perennial snow deposit.

Attempts to estimate snow depth and snow density in the absence of direct data need to take three

general classes of phenomena into account. Firstly, it

is useful to know the upper air conditions (temperature, water content, etc.) that determine how much

snow is produced in the cloud layer and what type

of snow crystals fall. Second, one needs to know the

temperature regime at the surface, which determines how fast the snow melts. Third, one needs to

know about the wind regime at the surface, which

regulates wind-packing, drifting, etc. Only the second and third classes of phenomena have been directly addressed in the work by Bilello, Bates and

others that have been summarized above.

The most difficult snow conditions for vehicle

mobility occur when snow densities are in the range

around 0.3–0.4 g/cm3. Newly fallen snow reaches

these densities only when there is a significant

amount of wind. This is not much consolation for a

vehicle mobility modeler, however, since virtually

all areas of the earth, including Korea, Alaska and

Germany, do commonly experience significant

winds.

snowpack at Suwon was in 1985, when the first

measurable snow depth occurred on 11 November. The latest date for the final disappearance

of the snowpack was in 1976, when the last

measurable snow depth occurred on March 22.

Although the Suwon data cover only point

information, they do tell us a few simple but

useful things. First, they show that there is a significant amount of snow in those parts of Korea

north of 35°00′N latitude. Second, we can see

that the snowpack is likely to be relatively

dense, since most of the country experiences a

large number of freeze–thaw cycles. Snow

depth is in general highly elevation dependent,

and of course the snow depths in most parts of

Korea, especially North Korea, will be much

higher than those at Suwon’s relatively low elevation.

ESTIMATED AVERAGE AND

WORST-CASE VALUES

Table 7 shows our recommendations for the

estimated average and worst-case values for

snow depth and density. The worst-case snow

depth represents what one might find at the

height of an unusually severe snow season, and

the density is simply the density that one might

find at the worst point of the same season. These

values correspond to flat, open ground; in the

field, one will of course typically have to deal

with sloping ground with a variety of cover

types.

Malcolm Mellor’s 1964 monograph continues

to be one of the best introductions to the properties of snow. One of Mellor’s general comments

about snow is of particular relevance:

Table 7. Recommendations for average and worst-case snow density

and depth values for interior Alaska,

central Germany and Korea.

Density Depth

(g/cm3) (cm)

Alaska

Average 0.230 50

Worst-Case 0.425 125

Central Germany

Average 0.275 25

Worst-Case 0.400 60

Korea

Average 0.275 18

Worst-Case 0.400 38
7

LITERATURE CITED

Bates, R.E. and M. Billelo (1966) Defining the cold

regions of the northern hemisphere. USA Cold Regions Research and Engineering Laboratory,

Technical Report 178.

Bates, R.E. and M. Bilello (1988) Comparison of

snowfall amounts and snow depths for locations

in Germany and the northeast United States. In

Proceedings of the 8th Annual EOSAEL/TWI Conference. Atmospheric Science Laboratory, White Sands

Missile Range, New Mexico.

Bilello, M.A. (1957) A survey of arctic snow-cover

properties as related to climatic conditions. USA

Snow, Ice and Permafrost Research Establishment

(USACRREL), Research Report 39.

Bilello, M.A. (1984) Regional and seasonal variations in snow-cover density in the USSR. USA

Cold Regions Research and Engineering Laboratory, CRREL Report 84-22.

Bilello, M.A., R.E. Bates and J. Riley (1970) Physical characteristics of the snow cover: Fort Greely,

Alaska, 1966-67. USA Cold Regions Research and

Engineering Laboratory, Technical Report 230.

Causey, E.M. and H.W. West (1990) Prototype environmental digital data for materiel design, testing and evaluation. USA Waterways Experiment

Station, Miscellaneous Paper EL-90-19.

Mellor, M. (1964) Properties of snow. USA Cold

Regions Research and Engineering Laboratory,

Cold Regions Science and Engineering Monograph III-A1.

Richmond, P. (1993) Predicting vehicle mobility on

snow-covered slopes. In Proceedings, First International Conference on Winter Vehicle Mobility (G.

Blaisdell, Ed.). USA Cold Regions Research and

Engineering Laboratory, Special Report 93-17.

Sturm, M., J. Holmgren and G.E. Liston (in press)

A seasonal snow cover classification system for

local to global applications. Journal of Climate.

U.S. Army (1950) Korean Environment. Environmental Protection Section, Office of the Quartermaster General, Report No. 171.

U.S. Navy, U.S. Air Force, Dept. of Commerce

(1992) International Station Meteorological Climate

Summary, ver 2.0. CD-ROM. Asheville, North

Carolina: Federal Climate Complex.
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September 1995

Estimated Snow Parameters for Vehicle-Mobility Modeling 633734T08

in Korea, Germany and Interior Alaska

Timothy Horrigan and Roy E. Bates

U.S. Army Cold Regions Research and Engineering Laboratory

72 Lyme Road Special Report 95-23

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Office of the Chief of Engineers

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Available from NTIS, Springfield, Virginia 22161.

Snow is a crucial factor affecting the U.S. Army’s operations in cold regions. Values for snow depth and

snow density are needed for vehicle mobility studies, but unfortunately the available historical records of

these parameters tend to be relatively sparse. This report deals with the estimation of snow density and

snow depth from readily available parameters such as air temperature and wind speed. As a basis for further study, the authors have summarized previous work in three areas of particular interest to the U.S. Army’s vehicle mobility programs: Korea, Germany and Alaska. Empirical models are presented for estimating

snow parameters in these regions.

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Alaska Snow density

Germany Snow depth

Korea Vehicle mobility