NASA Technical Memorandum 104640

Estimating the Thickness of Sea Ice

Snow Cover in the Weddell Sea from

Passive Microwave Brightness

Temperatures

K. R. Arrigo

NASA Goddard Space Flight Center

Greenbelt, Maryland

G. L. van Dijken

Department of Oceanography

Texas A &M University

College Station, Texas

J. C. Comiso

NASA Goddard Space Flight Center

Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

1996
This publication is available from the NASA Center for AeroSpace Information,

800 Elkridge Landing Road, Linthicum Heights, MD 21090-2934, (301) 621-0390.
Estimating the Thickness of Sea Ice Snow Cover in the Weddell Sea

from Passive Microwave Brightness Temperatures

K. R. Arrigo, G. L. van Dijken, and J. C. Comiso

AIk_.T.R5.C_

Passive microwave satellite observations have frequently been used to observe changes in sea

ice cover and concentration. Comiso et al. (1989) showed that there may also be a direct

relationship between the thickness of snow cover (hs) on ice and microwave emissivity at 90 GHz.

Because the in situ experiment of Comiso et al. (1989) was limited to a single station, the

relationship is re-examined in this paper in a more general context and using more extensive in situ

microwave observations and measurements of hs from the Weddell Sea 1986 and 1989 winter

cruises. Good relationships were found to exist between h s and the emissivity at 90 GHz-10 GHz

and the emissivity at 90 GHz-18.7 GHz when the standard deviation ofh s was <50% of the mean

and when hs was <0.25 m. The reliance of these relationships on hs is most likely caused by the

limited penetration through the snow of radiation at 90 Ghz. When the algorithm was applied to

Special Sensor Microwave/Imager (SSM/I) satellite data from the Weddell Sea, the resulting mean

hs agreed within 5% of the mean calculated from >1400 in situ observations.

INTRODUCTION

The emissivity of sea ice and its associated snow cover is much greater at some microwave

frequencies than those of seawater. Consequently, passive microwave sensors have proven to be

very useful for mapping the extent of the sea ice cover. To date, several sensors have been

launched aboard satellites and have provided a nearly continuous record of sea ice distribution

since 1972. These include the single band Electrically Scanning Microwave Radiometer (ESMR,

N-imbus-5 satellite, 1972-1977), the Scanning Multichannel Microwave Radiometer (SMMR,

Nimbus-7 satellite, 1978-1987) with bands at 5 frequencies (6, 10, 18, 21, and 37 GHz) at dual,

horizontal (H) and vertical (V) polarization, and the Special Sensor Microwave/Imager (SSM/I,

Defense Meteorological Satellite Program, 1987-present), a radiometer with frequencies at 19 GHz

(H and V polarization), 37 GHz (H and V polarization), 85 GHz (H and V polarization), and 22

GHz (V polarization only). The spatial resolution of these sensors is approximately 30 km 2.

In order to provide in situ measurements which can be compared with satellite observations,

Comiso et al. (1989) used surface-based sensors to measure local variability in the microwave

signature of sea ice in the Weddell Sea. Comiso et al. (1989) also examined the effect of snow

thickness on the emission of microwaves. To do so, they mounted a radiometer -1.5 m. above the

sea ice in an area of substantial snow cover. Radiometric measurements were made of the snow

surface when it was undisturbed and as the snow was carefully scraped off in layers of 0.05 m.

Significant increases in brightness temperature (TB) at 90 GHz were observed when the snow was

removed, while at other frequencies the signal remained relatively constant (Fig. 1). Comiso ¢t al.

(1989) demonstrated a nearly linear decrease in TB with increasing h s at 90 GHz (V) when the

instrument was oriented 50° from nadir, the approximate viewing angle of the SSM/I. This

suggested that it might be feasible to estimate the thickness of snow cover within the ice pack using

satellite mounted sensors.

The decrease in TB at 90 GHz with increasing h s has been postulated as caused by greater

attenuation of radiation emitted from the snowflce interface by the thicker snow pack. It was argued

by Comiso et al. (1989) that the absence of the decrease in I"13 at lower frequencies was because of

the smaller influence of volume scattering on TB, since the wavelengths are large compared to the

size of the snow particles. Modeling results of the effect of snow cover on sea ice show similar

behavior. It must be noted, however, that the microwave signal is not only influenced by h s but

by other characteristics of the snow pack as well, such as the wetness, granularity, density, and

presence of informalities like ice lenses, as well as the underlying sea ice itself.

3
Unfortunately,the preliminary findings reported by Comiso et al. (1989) were limited to only a

few measurements made at a single location. The objective of the present study is to re-examine the

relationship between h s and its associated microwave signature using surface-based data, and if

possible, to develop an algorithm appropriate for estimating hs from space. The surface-based data

used in this study were collected during the cruises ANT V/2 (1986) and ANT VIIF2 (1989) to the

Weddell Sea aboard the R/V Polarstern. Also, satellite TBs are correlated with in situ snow

measurements where no ship observations were available.

ANT V/2 (1986)

Observations of both hs and microwave TB were made during the 1986 Winter Weddell Sea

Project at 32 out of a total of 37 stations. A ship-mounted radiometer was positioned approximately 17 m above the sea ice and measured TB at frequencies of 6, 10, 18.7, 37, and 90 GHz at

various angles from nadir. The change in polarization from vertical to horizontal were made by

rotating the antenna 90 ° . Only data measured at an angle 50° from nadir were used in the present

study, because this is the approximate angle at which SSM/I data are collected (i.e., 53°).

The mean value for h s at each of the 32 stations was determined from about 26 individual

measurements. The mean value for h s for all stations ranged from 0 to 0.4 m, with 3 stations

having h s = 0 m, 27 stations with h s <0.25 m and 2 stations with h s >0.25 m (0.26 and 0.40 m).

The standard deviation ofh s ranged from 20-130% of the mean.

Emissivity (e) at the sea ice surface, calculated as

TB - Tsky

e = TI - Tsky

(Comiso et al 1989), was highly correlated with TB in the 1986 data (Fig. 2), where TI is the

snow-ice interface temperature and Tsky is the radiance caused by the atmosphere and free space.

For example, the regression of TB10 GHz (V and H polarization) against el0 GHz (V and H

polarization) at the same station yielded a correlation coefficient of>0.99 (Fig. 2A). This was also

true at 90 GHz (V and H polarization) (Fig. 2B).

A plot of e90 GI-Iz (V) versus e8.7 GHz (V) (Fig. 3) illustrates that the latter is reduced at hs=0

m (i.e., the three data points in the lower right quadrant). When snow was present on the surface

of the sea ice, the signal at 18.7 GHz remained approximately constant, while the micro-wave

signal at 90 GHz varied from 0.75 to 0.98 (Fig. 3). This relationship was shown previously by

4
Comisoet al. (1989) who attributedthe variability at 90 GHz to differencesin either hs or

propertiesof the snow cover. A similarrelationshipalsowasapparentwhenthe 90 Ghz (e and

TB, V and H polarization) band was plotted against 10 or 37 GI-Iz bands, but not the 6 GHz band

which exhibited more scatter.

Plots ofh s versus e or TB at 6, 10 (Fig. 4A), 18.7, and 37 GHz remained relatively fiat (no

slope), indicating that there was little sensitivity in the passive microwave signal at these frequencies to variation in h s within the snow pack. The obvious exception was the case where hs=0 m

which exhibited much lower values for both e and TB. On the other hand, the 90 GHz (V) frequency exhibited much greater variability with respect to hs, although there was no significant

correlation between the signal at 90 GHz and h s (Fig. 4B).

However, when stations where the standard deviation ofh s was greater than 50% of the mean

were removed from the analyses, the relationship between e90 GI-Iz (31) and h s improved dramatically (Fig. 5). Two types of outliers in Fig. 5 can be identified; stations where h s = 0 m (n = 2)

and stations where h s > 0.25 m (n = 2). When those stations were omitted, a good linear relationship between h s and the 90 GI-lz (V) frequency was obtained (Fig. 7). The equations and

goodness of fit of the least-squares linear regression between h s and e90 GHz (V) and TB 90 GHz

(V) are_

e90 GHz (V) = 1.08 - 0.019 * h s (ella)

TB90 GHz (V) = 290.24 - 5.21 * h s (cm)

(n = 10, r2 = 0.78)

(n = 10, r2 = 0.77)

(1)

(2)

Measurements with vertical polarization were more strongly correlated with hs than those with

horizontal polarization, which had higher (by -10%) standard deviations. The number of stations

where measurements at vertical polarization were taken was higher as well. The linear regression

of the h s data from Comiso et al. (1989) was

TB90 GHz (V) = 257.89 - 1.06 * h s (cm) (n = 4, r2 = 0.99) (3)

and is shown in Fig. 6 along with the data used to generate Eq. (2). Equation (2) has a much

steeper slope than Eq. (3), suggesting that predictions of h s made using Eq. (2) will be more

sensitive to changes in T90 GHz (V). Also, if the measured T B is below 250°K, Eq. (2) will
predictlowervaluesofhs, whileatTB above250°KEq.(2)will givehigher values. At T 250°K,

both algorithms produce a similar value for h s (-42.08 m).

If the relationship between h s and microwave signal is expanded to include two frequencies,

90 GHz (V) and either 18.7 GHz (V) or 10 GHz (V), the regression improves further still

e90 GHz (V) - _10 GHz (V) = 0.20 - 0.0227 * h s (cm)

TB90 GHz (V) - TB10 GHz (V) = 55.56 - 6.20 * h s (cm)

e90 GI-Iz (V) - e18.7 GHz (V) = 0.13 - 0.0197 * h s (cm)

TB90 GHz (V) - TB18.7 GHz (V) = 35.91 - 5.43 * h s (cm)

(n=l 1, r2=0.92) (4)

(n=12, r2=0.92) (5)

(n=12, r2=0.89) (6)

(n=12, r2=0.90) (7)

The advantage of this approach was that, in contrast to Eqs. (1) and (2) where snow-free stations

were omitted, stations where h s = 0 m fitted the resulting regression well (Fig. 7). Thus, the

algorithms shown in Eqs. (4-7) apply to all cases where h s < 0.25 m. This reslriction is because

of the fact that at higher values of hs, the penetration depth of radiation at 90 GHz is smaller than

the snow depth itself (Hall & Martinet 1985).

Only Eqs. (6) and (7) can be applied to SSM/I satellite data to generate estimates of h s because

unlike SMMR, SSM/I does not have a 10 GHz channel. SSM/I does not have a 90 GI-lz channel

either, but it is expected that the differences between the data generated from the 85 GHz frequency

of the SSM/I and the 90 GHz field data willbe small enough to allow substitution of the 85 GHz

SSM/I data into Eqs. (6) and (7).

Foster and Chang (1993) developed a simple algorithm to predict h s on land using the

microwave brightness temperatures at 18 and 37 GHz. Their equation was

h s = 1.59 * [TBI8 GHz (H) - TB37 GHz (I-I)] (8)

However, this algorithm provided a poor fit to the sea ice data, perhaps because of the different

emissivities of land and ice.

ANT VIII/2 (1989)

During the WeddeU Sea Experiment, h s and surface based radiometer measurements were taken

simultaneously at 18 stations at an angle 50° from nadir from September 16 to October 6, 1989.

Unlike 1986, only 10 GHz (V and H polarization) and 85 GHz (V and H polarization) frequencies

6
weremeasured.Althoughmoredetailedinformationof the snow coverwas reported(grainsize,

verticalstructureof the snow layer, presenceof ice lenses,etc.), meanhs at eachstationwas

determinedfrom, at most, four measurementswithin the radiometerfootprint. Snow depths

rangedfrom 0.08to 0.61m andweregenerallylessvariablethanin 1986.Most of thethick snow

coverwasobservedin thewesternWeddellSeawheretheice coveris generallythicker. Standard

deviationsfor aspecificstationcouldbe>100%of themeanhs,butweregenerally<40%.

Plottingthe85 GHz versusthe 10GHz frequencyconformedto expectations;TB at 10GHz

was fairly constant,while the signalat 85 GHz was highly variable. Also, TB ande for the 10

GHz channel were highly linearly correlated (r2>0.99) (Fig. 8A). However, TB and e were

poorly correlated for the 85 GHz band for both H and V polarization (Fig. 8B). This observation

was in direct contrast to measurements made in 1986 which showed a highly linear relationship

(Fig. 2) and suggests that the sky calibration of the radiometer in 1989 was not as good as those in

1986. However, some of the 1989 data conform to the relationship between TB and e found in the

1986 data. When the line of best fit from 1986 (Fig. 2) is plotted with the 1989 data, some of the

points lie along the regression (Fig. 8B), suggesting that the calibration for these points may be

correct. It is interesting to note that all of the suspect data lie to the right of the 1986 regression

line.

Because of the calibration problem, hs was poorly correlated with the quantity (e85-e10) GHz

(V) in the 1989 data set (Fig. 9). However, if the line of best fit of this relationship from the 1986

data is plotted with the data from 1989, it can be seen that some of the 1989 data points lie on or

near the 1986 regression line. Interestingly, the 1989 data points that conform to the 1986 relationship between TB85 GHz and e85 GHz also conform to the 1986 relationship between hs and the

quantity (e85-10) GI-Iz (V) (see circled solid black data points on Figs. 8 and 9). There were four

other stations in 1989 which conform to the 1986 relationship between TB 85 GHz and 885 GHz

but do not fitthe 1986 relationship between hs and the quantity (e85-10) GHz (V). Therefore, the

fact that TB and e at a particular station relate as expected cannot be used as a proxy for "good

data" for the snow depth algorithm. On the other hand, the disagreement of those data with the

algorithm could be caused by experimental or weather conditions as well. For example, the header

of these data files contained comments such as: "probably moist snow surface," "radiometers may

not be warm enough yet," "cloud broken/sun in and out station," and "near white out start of

angular scan, slow motion work to be done at the end"; conditions which would very likely affect

the microwave signature.

7
Comparisons with SSM/I data

Eqs. (6) and (7) were used to predict hs in the Weddell Sea from SSM/I imagery. The mean

value for hs estimated from SSM/I data was nearly identical to that calculated from >1400 in situ

measurements. However, the frequency histograms exhibited different distributions, with the in

situ data exhibiting a lognormal distribution and the estimates from SSM/I tending toward a more

normal distribution (Fig. 10). The differences in the two distributions is not surprising, however.

The Central Limit Theorem states that the sampling distribution of the mean approaches the normal

distribution as the sample size increases, regardless of the shape of the distribution of the raw data.

In our case, the SSM/I averages the snow depth over an area of 625 km 2, which is essentially the

same as increasing the sample size, resulting in a normal distribution. Despite the differences in

distribution, the agreement between the means of the two data sets suggests that the algorithm is

able to provide a reasonable estimate of hs from SSM/I satellite data over a large area.

ACKNOWLEDGMENTS: This work was supported by a Maryland Sea Grant College/NASA

fellowship in Remote Sensing of the Oceans to G. van Dijken.

REFERENCES

1. Carsey, F.D., R.G. Barry and W.F. Weeks. 1992. Introduction. In: F.D. Carsey [ed.],

Microwave Remote Sensing of Sea Ice, Geophys. Monogr. Set. 68:1-7, AGU, Washington,

D.C.

2. Comiso, J.C., T.C. Grenfell, D.L. Bell, M.A. Lange and S.F. Ackley. 1989. "Passive

microwave in situ observations of winter Weddell Sea ice," d. Geophys. Res. 94(C8): 10,891-

10,905.

3. Foster, J.L. and A.T.C. Chang. 1993. "Snow cover," In: R.J. Gurney, J.L. Foster and C.L.

Parkinson [eds.], Atlas of satellite observations related to global change. Cambridge

University Press, New York, p. 361-370.

4. Grenfell, T.C., J.C. Comiso, M.A. Lange, H. Eicken and M.R. Wensnahan. 1994. "Passive

microwave observations of the Weddell Sea during austral winter and early spring," 3.

Geophys. Res. 99(C5): 9,995-10,010.

5. Hall, D.K. and J. Martinec. 1985. Remote Sensing of Ice and Snow, Chapman and Hall, New

York. 189 pp.
FIGURES

27O

v

v

• 260

!,.--

• 250

El

E

0

_ 240

co

0

C

r- 230

220

• vertical polarization

[] horizontal polarization

90 GHz, 8 = 50 °, tair = -3°C

I I I l

A

270

• vertical polanzation

[] horizontal polarization

v

• 260

n_ 250

E

_ 240

C

x:: 2:30

o) 37 GHz, 0 = 50 °

._

rn

!

[]

220

B

I I I I I

0 5 10 15 20 25

Snow thickness (cm)

Figure 1. Brightness temperature at 90 and 37 GHz as a function of snow depth. Data was

collected using a microwave radiometer mounted -1.5 m. above the sea ice in an area of

substantial snow cover. Radiomelric measurements were made as the snow was carefully scraped

off in layers of 0.05 m.

11
A 260

240

22O

N

"1-

(.g

200

C:)

qp..

m

I--

180

y = 4.7541 + 261.59x R 2 = 0.989

• vertical polarization

160 = • horizontal polarization

/

140 /i , ' • ' , ' • '

0.5 0.6 0.7 0.8 0.9 1.0

E;10 GHz

B 280

260

N

-1-

(.9

O

O)

m

!-

240

22O

y= -4.1871+271.92x R2=0.981

°=

ila

£

,t vertical polarization

• horizontal polarization

L=,

200 _- , . , . i

0.7 0.8 0.9 1.0

t;90 GHz

Figure 2. Brighmess temperature versus emissivity at frequencies of (A) 10 GI-Iz and (B) 90 GHz.

Data was collected in 1986 as described in Fig. 1 with the exception that the radiometer was

mounted on the RV Polarstern -17 m above the ice surface.

12
0.5

0.6 0.7 0.8 0.9 1.0

_:90 GHz (V)

Figure 3. Emissivity at 18.7 GHz versus emissivity at 90 GHz. Data was collected as described in

Fig. 2.

13
1.0

0.9

 0.e

N

T

O 0.7

0.6

0.5 I I I I I I

0 10 20 30

I

4O

A

|

5O

1.0

0.9

N

-r 0.8

0

0

0.7

_,n m

• •

• • •

mlm •

mm

mm

B

0.6 . m , m . , - , - ,

0 10 20 30 4O 50

Snow depth (cm)

Figure 4. Snow depth versus emissivity at frequencies of(A) 10 GHz and (]3) 90 GFIz. Data was

collected as described in Fig. 2.

14
0.6

0 10 20 30 40 50

Snow depth (cm)

Figure 5. Snow depth versus emissivity at 90 GHz after removing data points where the standard

deviation of the snow depth was <50% of the mean. Circled data points indicate where hs = 0 and

hs > 0.25 m. Data was collected as described in Fig. 2.

15
260

240

210

Comiso et al. (1989)

290.24- 5.2077x R2 = 0.767

2OO

0 5 10 15 20

Snow depth (cm)

Figure 6. Least-squares regression of brightness temperature at 90 GI-Iz versus snow depth (closed

squares). Data points where the standard deviation of the snow depth was <50% of the mean, and

where hs = 0 and hs > 0.25 m were also omitted. The best fit line of the data from Comiso et al.

(1989) is shown for comparison (open diamonds). Data was collected as described in Fig. 2.

16
N

"lt,,-

o6

¢,0

!

O

O_

y = 0.12595 -0.19675x R2 = 0.894

• •

-0.3

0 5 10 15 20

Snow depth (cm)

Figure 7. Snow depth versus the difference in emissivity at 90 GHz and 18.7 GHz. Note that data

points where hs = 0 m have now been included. Data was collected as described in Fig. 2.

17
A 260270f- Y = 6.8679 + 260.84x R_,,

iiif

,

0.7 0.8 0.9 1.0

t;10 GHz

B 240

22O

200

N

"1-

(.9

u') 180

oo

O0

1--

160_

140 f

1201

[] vertical polarization

• horizontal polarization

I I I = I = I

0.0 0.2 0.4 0.6 0.8 1.0

E.85 GHz

Figure 8. Brighmess temperature versus emissivity at frequencies of (A) 10 GHz and (B) 90 GHz.

Data was collected in 1989 as described in Fig. I with the exception that the radiometer was

mounted on the RV Polarstern - 17 m above the ice surface. The line in (B) is the least squares fit

to the 1986 data shown in Fig. 2B. Circled data points in (B) correspond to circled data points in

Fig. 9 and indicate data which may be salvageable.

18
N

"1-

O

¢.0

I

tO

CO

v

-0.1 •

®

nl

-0.6

0 10 20 30 40 50 60 70

Snow depth (cm)

Figure 9. Snow depth versus the difference in emissivity at 85 GHz and 10 GHz. The line is the

least squares fit to the 1986 data shown in Fig. 7. Data was collected as described in Fig. 8.

19
rID

"1

G)

(!)

m

(D

tw

1 I

|

|

0.9

0+J

0.7

0.6 I

0.5 \

0.4 _

0.3 mean in situ

0.2

0.1

0

0 0.2 0.4 0.6 0.8 1

Snow thickness (m)

Figure 10. Comparison of the relative frequency distributions of snow depth as determined from in

situ data and from estimates made using SSM/I data as input to Eq. (6). Note that the means for

the two methods are almost identical. Differences in the distributions are explained in the text.

20

Form Approved REPORT DOCUMENTATION PAGE oM,_No.0zo_o188

Public repo_ burden for this collection of information is es'drnated to average 1 hour per response, including the time fo( reviewing instTuctJor_, searching e0(isting data sources, gathering

and maintaining the data needed, and _ and reviev_g the ¢o41ection of information. Send comments regarding this burden embrnate or any other aspect of this co;lee'don of

infocmatJon, including suggeslJons for reducing this burden, _o Washington I-kmdquarter_ Sewces, Directorate for Infon_afion Opera_ons and Reports, 1215 Jeflemon Davis Highvay, Suite

1204, Arln_lfon r VA 22202-43021 and to the Office of Mana_ernent and Budget t Paperwork Reduct_n Pn)pc't p704-0188_1 V_shin_dcm, DC 20503. ____

1. AGENCYUSE ONLY (Leave ldank) 2. REPORTDA_IE 3. REPORT TYPE AND DATES COVr.m=u

December 1996 Technical Memorandum

4. TITLE AND SUB'nTUE 5. RJNDING NUMBB_

Estimating the Thickness of Sea Ice Snow Cover in the Weddell

Sea from Passive Microwave Brightness Temperatures Code 971

s. AUTHOR(S)

K. K Arrigo, G. L. van Dijken, and J. C. Comiso

7. PERFORIWNG ORGANIZATION NAME(S) AND ADDRESS0ES)

Laboratory for Hydrospheric Processes

Goddard Space Flight Center

Greenbelt, Maryland 20771

S. SPONSORI_G/MONn'ORING AGENCY NANE(S) AND ADDRESS(ES)

Goddard Space Flight Center

National Aeronautics and Space Administration

Washington, DC 20546-0001

8. PERFORMING ORGANIZATION

REPORT NUI_ER

97B00003

10. SPONSORING/MONITORING

AGENCY REPORT NUMBER

TM-104640

11. SUPPLEMENTARY NOTES

G. L. van Dijken: Department of Oceanography, Texas A&M University,

College Station, TX 77843-3146

12a. DISTRIBUTION/AVAILABILITY STATEMENT 121). DISIRIBU11ON CODE

Unclassified-Unlimited

Subject Catagory: 43

This report is available from the NASA Center for AeroSpace Information,

RO0 _lirrlda_. I ._ndino Rn_cl I inthic_lm l-l_i_ht_ _ 91f_fl" ('_131"_ ¢_9.l-f)qQf]

13. ABSTRAC_ (Ma_dmum2"00woMs') ....

Passive microwave satellite observations have frequently been used to observe changes in sea ice

Icover and concentration. Comiso et al. (1989) showed that there may also be a direct relationship

between the thickness of snow cover (h) on ice and microwave emissivity at 90 GHz. Because the in

• • • .

sltu experiment of expenment of Comtso et al. (1989) was limited to a single station, the relationship is

re-examined in this paper in a more general context and using more extensive in situ microwave observations and measurements ofh from the Weddel Sea 1986 and 1989 winter cruises. Good relationships

were found to exist between h Sand the emissivity at 90 GHz-10 GHz and the emissivity at 90 GHz-18.7

• S

GI-Iz when the standard dewallon ofh was < 50% of the mean and when hs was < 0.25 m. The reliance

• . S

of these relationships on h is most likely caused by the limited penetration through the snow of radiaS

tion at 90 GHz. When the algorithm was applied to the Special Sensor Microwave/Imager (SSM/I)

satellite data from the Weddell Sea, the resulting mean h s agreed within 5% of the mean calculated

from > 1400 in situ observations.

14. SUBJECT TERMS

Antarctica, snow, SSM/I, passive microwave

17. SECURITY CLASSIFICATION 18.SECURITY CLASSIRCATION

OF REPORT OF THIS PAGE

Unclassified Unclassified

NSN 7540-01-280-5500

19. SECURITY CLASSIFICATION

OF ABSTRACT

Unclassified

15. NUMBER OF PARS

16. PRICE CODE

20. UMITATION OF AB_-IcS, CT

Unlimited

Standard Form 298 (Rev. 2-89)

IDro_-e.lh_t hv AM._-J _ "_'.LQ-qR '_R.4tl")