1

Microwave Emissivity of Sea Ice Estimated from Aircraft Measurementsduring FIRE-SHEBA

Julie A. Haggerty and Judith A. CurryProgram in Atmospheric and Oceanic Sciences, University of Colorado

Boulder, Colorado

Corresponding Address:

Julie HaggertyDepartment of Aerospace Engineering SciencesCampus Box 429University of ColoradoBoulder, CO 80309-0429(303)492-4469(303)492-7881 (fax)haggertj@cloud.colorado.edu

Submitted toJournal of Geophysical Research, FIRE-ACE Special Issue(submitted December 1,1999)

2

e

ly,

ice emis-

tain

xperi-

ates

eri-

Hz,

mul-

n May

cal-

Abstract

Passive microwave radiometers with frequencies ranging from 37 GHz to 220 GHz wer

flown over the Surface Heat Budget of the Arctic (SHEBA) experimental site in May and Ju

1998. Brightness temperature measurements from these sensors are used to calculate sea

sivity at each frequency using ancillary aircraft data to characterize the atmosphere and ob

surface temperature. Surface emissivities on clear sky days during the FIRE Arctic Cloud E

ment (FIRE-ACE) aircraft campaign have been calculated and compared with previous estim

cited in the literature. Average emissivity at nadir for snow-covered multiyear ice in the exp

mental region during late May is estimated as 0.91 at 37 GHz, 0.78 at 89 GHz, 0.73 at 90 G

0.77 at 150 GHz and 0.89 at 220 GHz. In early July, the average nadir emissivity for melting

tiyear ice is 0.89 for 37 GHz and 0.87 for 90 GHz. Estimates at a 50° viewing angle are compared

with previous satellite and groundbased measurements at 37 GHz and 90 GHz. Our results i

are higher than previous averages for dry multiyear ice, particularly at 37 GHz. Emissivities

culated for July compare well with previous measurements for summer melt conditions.

3

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ation,

from

ace at

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chan-

to be

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appli-

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1. Introduction

Passive microwave measurements of sea ice have been available since the late 1970s, a

been used extensively for monitoring various surface properties including ice type, concentr

and extent. Limited effort has been given to retrievals of atmospheric properties over sea ice

microwave data for two reasons. First, the high emissivity and heterogeneity of the ice surf

microwave frequencies make separation of the atmospheric contribution more difficult than o

radiatively cold surface such as the ocean. Second, it has been widely assumed that the a

sphere is effectively transparent to the commonly used frequencies (e.g., the 19 and 37 GHz

nels of SSM/I) when performing sea ice retrievals. However, if microwave observations are

used for retrieving atmospheric properties, the surface contribution to the upwelling radiance

be estimated. With a method for estimating the surface contribution, retrieval algorithms su

those used over the ocean for deriving cloud liquid and ice water path, can be developed for

cation over sea ice.

Various researchers have addressed the issue of high background emissivity in the micr

portion of the spectrum with studies over land. Jones and VonderHaar (1997) developed a m

for measuring cloud liquid water path over land with SSM/I. Their algorithm includes an estim

of land surface emissivity derived with ancillary visible and infrared satellite observations in c

sky regions. By estimating the surface contribution to the upwelling microwave radiance, the

able to subtract it from the signal and assign the remaining term to cloud liquid contributions

a region of Colorado. Land surface emissivity calculations from SSM/I were also performed

Prigent et al. (1997) in the Meteosat observation area (Europe and Africa). Felde and Pickl

(1995) retrieved emissivity at 91 and 150 GHz over various surfaces including snow, bare s

and vegetation using SSM/T2 data.

4

ations.

urface

ontri-

and

meter

irborne

dget

ur-

EBA

e are

still

ows us

te-

e emis-

o sen-

r

in

fre-

uency

Sea ice and snow emissivity have also been calculated from passive microwave observ

Comiso (1983, 1986) used SMMR brightness temperatures and infrared measurements of s

temperature to calculate emissivity for ice classification applications, though atmospheric c

butions were not taken into account. Estimates of microwave emissivity for a variety of snow

ice types in the Baltic Sea were made by Hewison and English (1999) using airborne radio

data at frequencies ranging from 24 GHz to 157 GHz.

In this study, we adapt techniques described by these researchers and apply them to a

radiometer data from the FIRE Arctic Clouds Experiment (FIRE-ACE) and Surface Heat Bu

of the Arctic (SHEBA) aircraft observing period (Curry et al., 1999; Perovich et al., 1999). D

ing May, June, and July 1998, two aircraft carrying microwave radiometers overflew the SH

ice camp. Using these passive microwave measurements along with ancillary aircraft data, w

able to examine the variations in emissivity prior to the onset of melting when snow cover is

present and during the summer melt season. The high resolution of the airborne sensors all

to characterize the variability in emissivity on a much finer scale than in the previous satelli

based studies, so features such as leads can be detected. The two radiometers also provid

sivity estimates over a wide range of frequencies. Overlapping frequencies between the tw

sors provide data for intercomparison. The in situ data set from the SHEBA ship is useful fo

relating emissivity values to observed surface conditions and for understanding differences

emissivity at varying frequencies.

2. Methodology

Surface emissivity can be derived from the equation of radiative transfer for microwave

quencies. Assuming a non-scattering (cloud-free) atmosphere, the equation at a given freq

and polarization is

5

er,

hich is

(1)

(2)

where Tb is brightness temperature measured by a radiometer,εs is surface emissivity, Ts is the

physical temperature of the surface,τ is atmospheric opacity, H is depth of the atmospheric lay

T(z) is the atmospheric temperature as a function of height,α(z) is the atmospheric absorption

coefficient,µ is the cosine of the viewing angle, and dz’=dz/µ. The first term on the right hand

side represents surface emission, the second term is downwelling atmospheric emission w

reflected by the surface, and the third term is upwelling atmospheric emission.

Rearranging the above relationship to solve for emissivity, we obtain

where the following notation has been introduced:

(3)

(4)

Tb εsTsτ 0 H,( )–

µ---------------------

1 εs–( ) T z( )α z( ) τ 0 z,( )–µ

------------------- exp z' T z( )α z( ) τ z H,( )–

µ---------------------

exp z'd

0

H

∫+d

0

H

∫+exp=

εsTb Tup– aTdown–

a Ts Tdown–( )----------------------------------------------=

aτ 0 H,( )–

µ---------------------

exp=

Tup T z( )α z( ) τ z H,( )–µ

--------------------- exp z'd

0

H

∫=

Tdown T z( )α z( )expτ 0 z,( )–

µ-------------------

z'd

0

H

∫=

6

sing

sources

issiv-

ing

or

ied

ly of

he

camp

AR

cies,

verti-

to

ely 3

ion

t tem-

Emissivity is determined by applying (1) through (4) at a given frequency and polarization u

observations obtained from aircraft and surface based measurements. Each of these data

is described in the following section, along with assumptions made in applying them for em

ity calculations.

3. Data

Aircraft measurements in the vicinity of the SHEBA ice camp were conducted in the spr

and summer of 1998. Of particular interest for this work are flights by the National Center f

Atmospheric Research (NCAR) C-130 aircraft and the NASA ER-2 aircraft, since both carr

microwave radiometers. The NCAR C-130 performed 8 research flights in May and 8 in Ju

that year. The NASA ER-2 flew 11 missions from mid- May through early June. Review of t

meteorological observations on each flight day revealed cloud-free conditions over the ice

on May 20, May 24, and July 8. Both aircraft were operating in the region on May 20; the NC

C-130 also flew on May 24 and July 8.

3.1 Sensor Descriptions

The Airborne Imaging Microwave Radiometer (AIMR) is a cross-track scanning system

which flies on the NCAR C-130. Four channels measure upwelling radiation at two frequen

37 and 90 GHz, and two orthogonal polarizations which can be converted to horizontal and

cal components. The AIMR views underlying scenes over an angular swath of 120°. Beam

widths of 1° at 90 GHz and 2.8° at 37 GHz produce spatial resolutions on the order of 20 m

300 m at typical flight altitudes and velocities. Corresponding swath widths are approximat

to 20 km. Detailed specifications for the AIMR can be found in Collins et al. (1996).

Calibration of the AIMR is performed internally as the scanning mirror views two calibrat

loads after viewing the underlying scene. One load is heated while the other floats at ambien

7

ments

m cal-

ight-

ich was

e not

ncies,

68

ith

tter

ose

e spec-

era-

ors in

the

perature, so two reference points are obtained relating voltage to radiance. Scene measure

are converted to radiance by interpolating between the reference points. Errors resulting fro

ibration uncertainties and polarization conversion are discussed by Collins et al. (1996). Br

ness temperature errors are estimated as less than 1°K for angles greater than 20°; at smaller

angles the errors may increase to 1.5°K or more. It should be noted that these error estimates

assume scene brightness temperatures fall between the hot and cold load temperatures wh

not always the case in the FIRE-SHEBA experiment. The uncertainties in this situation hav

been quantified.

The Millimeter Imaging Radiometer (MIR) was flown on the NASA ER-2 during FIRE-

SHEBA. The MIR is a cross-track scanner measuring microwave radiation at several freque

including 89, 150, and 220 GHz. It scans over an angular swath of 100° and has a 3.5° beam

width. Resulting swath width at the typical 20 km flight altitude of the ER-2 is approximately

km and spatial resolution is 1.2 km. The MIR calibration system is similar to that of AIMR w

two internal loads viewed during each scan. Racette et al. (1996) give error estimates of be

than 1°K for brightness temperatures between 240 and 300°K. Post-flight calibration efforts have

demonstrated errors on the order of 2 to 4°K for brightness temperatures below 100°K.

A set of Heimann Model KT19.85 pyrometers was flown on the NCAR C-130 for the purp

of remotely sensing surface (skin) temperature. These sensors measure radiation within th

tral range 9.6 to 11.5µm over a 2° field of view, and relate that measurement to surface temp

ture. The accuracy of the measurement is affected by absorption and emission of infrared

radiation from atmospheric water vapor in the layer between the surface and the aircraft. Err

surface temperature are also introduced by assuming the surface is a blackbody. In reality,

8

ng

amp

us

te.

0,

n the

ass

tern.

0

ce the

on

l used

mis-

skin

epth

1 and

en

spectral emissivity of ice and snow is smaller than unity, resulting in reflection of downwelli

infrared radiation. Corrections for these effects are discussed in Section 4.

3.2 Data Analysis

Equations (1) through (4) require input from several sources in order to estimateεs at a given

frequency. Brightness temperatures, Tb, at 37 and 90 GHz are obtained from the AIMR. The MIR

provides Tb at 89, 150, and 220 GHz. Straight and level flight segments over the SHEBA ice c

provide two-dimensional images of Tb for analysis. Flight patterns for the C-130 tended to foc

within 50 km of the ship. The ER-2 covered a wider region, but also overflew the SHEBA si

Figure 1 shows the portion of the C-130 and ER-2 flight tracks used for this study on May 2

1998. On each C-130 flight, a series of 50 km segments oriented east-west and centered o

SHEBA ship are used for emissivity calculations. The ER-2 flight on May 20 included one p

over the SHEBA site, oriented approximately north-south, passing over the C-130 flight pat

Vertical profiles of temperature and humidity are needed to calculate the atmospheric

upwelling and downwelling terms, Tup and Tdown. Soundings were routinely made by the C-13

at the completion of its flight pattern over the ice camp from the surface to about 6 km. Sin

C-130 profiles do not extend up to the altitude of the ER-2 (20 km), a radiosonde observati

from the SHEBA camp was used for that case. These profiles also serve as input to the mode

for calculation of atmospheric absorption coefficient,α, at microwave frequencies (Liu,1998).

A measure of the physical temperature of the surface emitting layer is required for the e

sivity calculation. Measurements from the KT19 infrared thermometer provide an estimate of

temperature, however, radiation at microwave frequencies emanates from a layer of finite d

depending on frequency. Table 1 gives penetration depths (calculated following Ulaby, 198

Liu and Curry, this issue) for snow and ice at frequencies measured by AIMR and MIR. Giv

9

r the

orizon-

pera-

cle of

early

e late

ere

empera-

he

file

resent

rature

should

les

now-

ppears

er

iation

ula-

e trans-

that the emitting layers are generally several cm deep, we need an effective temperature fo

layer rather than just a skin temperature. Since this measurement is not available over the h

tal scale of the passive microwave measurements, we improvise by trying to relate skin tem

ture measurements to emitting layer temperature. Perovich et al. (1997) logged an annual cy

sea ice temperature profiles in a multiyear ice floe in the Beaufort Sea. Their results show a n

isothermal profile of sea ice temperatures to well below microwave penetration depths for th

May and early July periods of interest for this study. Similar profiles near the SHEBA site w

measured in 1998 (Perovich et al., 1999), so it seems reasonable to assume that the skin t

ture is a good approximation for the temperature of the emitting layer at this time of year. T

assumption is probably less valid when snow covers the sea ice, since the temperature pro

through the snow layer cannot be assumed isothermal. A snow layer of about 35 cm was p

in late May, 1998 (Perovich et al., 1999), but the differences between the atmospheric tempe

and the ice temperature were not large, so the temperature gradient through the snow layer

be relatively small. Data from the SHEBA site (Perovich et al., 1999) give temperature profi

through the snow layer showing a slight increase between the snow-ice interface and the s

atmosphere interface in late May. On the dates of interest here, the temperature difference a

to be about 1°K. Thus, the error involved in using skin temperature to represent emitting lay

temperature should be 1°K or less.

Errors in skin temperature measurements due to emission and absorption of infrared rad

by the atmosphere can be estimated and removed from the data prior to our emissivity calc

tions. The upwelling radiance observed by a KT19 sensor can be described by the radiativ

fer equation for a non-scattering atmosphere:

Lm εLsfc Latm 1 ε–( )Lref+ +=

10

iance

ic and

ions

and

odel

and

a

infra-

resent

Temper-

des.

priate

rared

ge of

ter),

ends

the

t the

, et al.

where Lm is the upwelling radiance observed by the sensor,ε is infrared emissivity of the surface,

Lsfc is radiance emitted by the surface, Latm is radiance emitted by the atmosphere, and Lref is

atmospheric radiance reflected by a surface with emissivity less than unity.

Skin temperature is derived from the KT19 measurement by assuming that observed rad

consists only of surface emission. The data can be corrected by calculating the atmospher

reflected radiances and including them in the surface temperature derivation. Such correct

have been previously applied to infrared temperature measurements of sea ice by Steffen

Lewis (1988) and Muller et al. (1975). Following their methods, we use a radiative transfer m

(Key, 1998) to calculate correction factors for each of the days analyzed here. Temperature

humidity profiles from the aircraft were input into the model, and runs were performed over

range of hypothetical surface temperatures. The differences between “top-of-atmosphere”

red brightness temperature calculated by the model and the input surface temperature rep

the measurement error. Figure 2 gives results for each of the three days considered here.

ature errors are plotted as a function of KT19 measured temperatures for typical flight altitu

Least square fits to the model output are shown; these curves are used to “look-up” the appro

correction when using surface temperature measurements in the emissivity calculations.

The effect of varying infrared emissivity was also considered with these model runs. Inf

emissivities for snow and ice are discussed by Salisbury et al. (1994). Over the spectral ran

the KT19, they report dry snow emissivities ranging from 0.997 to 0.999. For ice (distilled wa

they give values ranging from 0.97 to 0.995 for this spectral range. The actual emissivity dep

on many factors (e.g., snow grain size, extent of melting) that cannot be characterized from

available data. Infrared surface temperature measurements were also made from towers a

SHEBA site; researchers report using an emissivity of 0.99 for those measurements (Claffey

11

rs

the

0.99 is

nd

tered

flight

0 for

layer

layer

n the

air-

gan.

and

tted

al reso-

for

geneity

ost of

1999). For consistency, we assume the same, and perform model runs to estimate the erro

induced by assuming an emissivity of unity versus 0.99. For the atmospheric conditions on

three days considered here, the effect on surface temperature of changing the emissivity to

on the order of 0.2 to 0.3°K. We consider this amount to be within the error of the instrument, a

thus have not corrected the surface temperature data for this effect.

4. Results

Surface emissivity at AIMR and MIR frequencies has been calculated over an area cen

on the SHEBA site as shown in Figure 1. Clear sky days are required for these calculations;

days satisfying this requirement were May 20, May 24, and July 8 for the C-130 and May 2

the ER-2. In late May, the multiyear ice floe on which the ship was located was covered by a

of snow about 35 cm deep. Melting appears to have begun near the end of May, so the snow

is assumed to consist of dry snow on May 20 and May 24. Numerous leads were present i

vicinity of the ship, but they were largely frozen with almost no open water observed from the

craft. Conditions had changed drastically by early July when the second series of flights be

No snow was present on the ice during the July 8 flight. There were numerous open leads,

meltponds had formed on the ice.

4.1 Spatial and Temporal Variation

Time series of 37 and 90 GHz emissivities along the C-130 flight track on May 20 are plo

in Figure 3. Each of the discrete time segments represents a distance of about 50 km. Spati

lution of the AIMR at this altitude (4000 m) is about 70 m for the 90 GHz channels and 200 m

37 GHz, so leads of at least those widths are resolved in these measurements. The inhomo

of the surface is apparent in the range ofεs values that occur over small distances. At 37 GHz,εs

ranges from 0.8 to 0.97, while at 90 GHz the values span a larger range from 0.65 to 0.95. M

12

g

n be

tion

d sur-

Hz,

d radi-

ion,

than at

re not

erence

the

ce. If

tion

s

r of

ow

as

of

the variation at 90 GHz is attributable to leads; the standard deviation ofεs exclusive of leads is on

the order of 0.035. The difference inεs at the two frequencies may be explained by considerin

the observed snow layer covering the ice together with microwave penetration depths. It ca

seen from Table 1 that the snow layer has little effect on emission at 37 GHz, since penetra

depth for dry snow exceeds the measured depth of 35 cm. Thus we surmise that the emitte

face radiation at 37 GHz is coming primarily from the multiyear ice beneath the snow. At 90 G

the theoretical penetration depths suggest that the snow layer contributes part of the emitte

ation. Thus the two frequencies are effectively “seeing” different dielectric materials. In addit

scattering by snow at 90 GHz causes attenuation of the emitted radiation, soεs is further reduced.

Lead signatures are apparent, especially at 90 GHz, whereεs values briefly jump to 0.8 and

above as would be expected for first year ice. The leads are more easily detected at 90 GHz

37 GHz for two reasons. First, the spatial resolution is higher at 90 GHz, so smaller leads a

resolved in the 37 GHz measurements. Large leads are apparent at 37 GHz, though the diff

betweenεs for leads versus that for multiyear ice is smaller than at 90 GHz. The reason for

large difference at 90 GHz may be that the snow layer on leads is thinner than on multiyear i

the snow layer on a lead were less than 22 cm, then that channel would be receiving radia

emitted from the first year ice. No observations of lead snow cover are available, but it seem

plausible that there would be less snow on leads due to heating from below the thinner laye

ice, and perhaps due to wind blowing snow off the smooth ice.

By July the ice surface has changed dramatically since melting has occurred and the sn

cover is gone. A similar flight pattern consisting of 50 km segments centered on the ship w

flown by the C-130 on July 8. Times series plots ofεs are shown in Figure 4. In contrast to the

measurements in May,εs values at the two frequencies are now quite similar. In the absence

13

-

l, the

eads

the

n

dou-

aining

lution

peaks

aller

this fre-

ear.

e

foot-

the

s from

ow

ponent

now

nd 150

snow and at surface temperatures near 0°C, penetration depths are less than 1 cm for both fre

quencies. Thus each channel is viewing the same dielectric material in this case. In genera

multiyear iceεs values are more homogeneous than during May, ranging from 0.85 to 0.93. L

now look radiometrically cold, since they are occupied by open water. At both frequencies,

standard deviation exclusive of leads is smaller (~0.025) than in May.

Figure 5 shows frequency distributions ofεs for the May 20 and July 8 cases. (Distributions o

May 24 are similar to those on May 20). The 37 GHz distributions for the May dates show a

ble peak, the higher peak presumably consisting of pixels with leads and the other peak cont

multiyear ice pixels. The separation is not distinct, though, probably because the 200 m reso

at this frequency causes many pixels to contain mixtures of leads and multiyear ice. Single

centered at about 0.71 characterize the 90 GHz distributions on May 20 and May 24. A sm

number of pixels are spread over the 0.8 to 0.95 range. These represent leads detected at

quency. The July plots show the similarity of the 37 and 90 GHz distributions at this time of y

In this case, the lead pixels are those withεs values below 0.8.

Frequency distributions ofεs for MIR channels on May 20 are shown in Figure 6. The rang

of values observed is smaller than that seen by AIMR; this is probably a result of the larger

print of MIR (1.2 km) and its inability to resolve individual leads. Mean values ofεs are 0.78,

0.77, and 0.89 for the 89 GHz, 150 GHz, and 220 GHz channels, respectively. Referring to

penetration depths in Table 1, we infer that a portion of the emission at these frequencies i

the snow layer, though some emission is also coming from the sea ice. Emission by dry sn

increases with frequency. Scattering by snow also increases with frequency. Hence the com

emitted by the sea ice will be more strongly attenuated due to enhanced scattering by the s

layer at higher frequencies. The net effect is that emission decreases slightly between 89 a

14

nuation

it is

ot fly

mpted

gh,

g the

. It

the

d tem-

ht-

alti-

ich

atched

erived

ntative

mpera-

tion of

s

GHz, then increases again at 220 GHz where the higher emission from snow exceeds atte

of the sea ice signal by scattering.

The MIR and AIMR share a nearly common frequency (89 and 90 GHz, respectively), so

useful to compare the emissivity calculations from both sensors. Because the aircraft did n

exactly coincident flight paths and were separated in time by a few hours, we have not atte

to make a pixel-by-pixel comparison. We can compare the results in a statistical sense thou

since both aircraft flew in the vicinity of the SHEBA site on May 20. The meanεs in the 50 km2

box surrounding the ship is 0.73, as calculated from AIMR measurements at 90 GHz. Alon

150 km linear track where the ER-2 passed over the ship, the meanεs from MIR at 89 GHz is

0.78. The Tb error ranges given in Section 3 translate intoεs error bars of±0.005 for MIR and

±0.01 for AIMR, so errors in the Tb measurements do not completely account for the difference

is possible that Tb errors for one or both sensors are larger than is thought, especially since

brightness temperatures at this frequency tend to fall below the range of the calibration loa

peratures for both sensors. (Calibration errors are likely to be larger for the AIMR since brig

ness temperatures are lower compared to MIR, and because the lower end of the AIMR

calibration temperature range is higher due to warmer ambient temperatures at lower flight

tudes). Another source of error that may contribute to the bias is the differing method by wh

surface temperature is specified for each data set. Whereas each AIMR data sample was m

individually with a surface temperature at its location, an average surface temperature as d

from KT19 measurements was applied to the entire MIR segment. The average was represe

of multiyear ice temperatures in the area covered by the C-130, and excluded the higher te

tures of leads. Underestimating the surface temperature would tend to cause an overestima

εs according to (1). Finally, the calculation of the atmospheric contribution probably contain

15

that

s a 4

me-

nsor.

along

ovided

BA

2-0.3

ad sur-

the

ata set,

of the

the

ong

g over

eads

ds are

some uncertainties, though it is unclear how large or of what sign the errors would be. Given

the atmospheric integration is performed over a 20 km layer for the MIR calculations versu

km layer for AIMR, the errors introduced in each result could be considerably different.

4.2 Polarization and Viewing Angle Variations

In calculating emissivity for the two-dimensional images produced by the scanning radio

ter, surface temperatures were not available for each pixel since KT19 is a nadir-viewing se

In the case of the AIMR, it was assumed that the nadir surface temperature at a given point

the flight track represented the surface temperature for each pixel in the cross-track scan, pr

the pixel contained multiyear ice. Statistics compiled from the C-130 flight tracks in the SHE

region showed little variation in surface temperature (standard deviations on the order of 0.

°K) on a given day at the resolution of the KT19, so this assumption seems reasonable. Le

face temperature statistics were also compiled. Again, small variations were observed over

region, so a constant lead temperature was assumed for each day analyzed. For the MIR d

coincident surface temperature measurements were not available. In addition, the resolution

MIR does not allow for differentiation of most leads. Thus, an average surface temperature

derived from the KT19 measurements in the SHEBA region on a given day was applied to

entire MIR image.

AIMR images of emissivity on May 20, 1998 are shown in Figure 7. The flight track is al

the vertical axis of these images which correspond to the east-west segment shown passin

the ship in Figure 1. As noted when comparing the nadir traces,εs at 37 GHz andεs at 90 GHz are

substantially different, presumably due to the snow cover and differing penetration depths. L

are easily detected in the 90 GHz image as high emissivity features. Many of the same lea

also apparent in the 37 GHz image, but substantial variation inεs outside of leads is also noted.

16

ger

ed.

ompar-

ound-

er

vari-

n in

ey

y. We

tmo-

ondi-

e

AIMR

calibra-

cess

e

y is

y 8. In

Brightening along the edges of theεs images is also apparent in the Tb signal and is possibly an

artifact of the sensor, as noted by Collins et al. (1996). MIR images (not shown) cover a lar

area at lower resolution; surface features such as the leads seen in Figure 7 are not resolv

Estimates ofεs given in the literature are generally at 50° viewing angles to correspond with

satellite microwave radiometers. AIMR estimates at these angles have been averaged for c

ison with previous estimates. Carsey (1992) compiled emissivity values from satellite and gr

based sensors at 37 and 90 GHz for various ice types including dry multiyear ice and summ

melting conditions. Comiso (1983) gives SMMR estimates of emissivity for multiyear ice at

ous Arctic sites over an annual cycle. Results for multiyear ice conditions in late May are give

Table 2. AIMR estimates at 37 GHz are significantly higher than the averages cited by Cars

(1992), but are close to the upper end of the range determined by Comiso (1983) for late Ma

note that Comiso did not incorporate atmospheric effects in his estimates; removal of the a

spheric contribution to brightness temperature tended to raise emissivity estimates for the c

tions observed in FIRE-SHEBA. Thus, AIMR emissivity estimates using Comiso’s techniqu

would be smaller. For example, at typical brightness temperatures observed on May 20,εs would

change from 0.92 with the atmosphere included to 0.87 without the atmosphere. Also, the

manufacturer notes possible errors along the edge of the scan due to energy from the hot

tion load entering the antenna sidelobes, particularly at 37 GHz (Collins et al., 1996). This ex

energy is most apparent at scan angles between 55 and 60° where it has been observed to increas

Tb by about 5-10°K, but there may be some smaller influence at 50° which would artificially

raise Tb and thereby increaseεs. The effect is not consistently observed, though, and more stud

required before this source or error can be quantified. Table 3 shows the comparisons for Jul

17

nd 90

has

, and

pro-

fre-

EBA

aria-

s for

ially at

given

ombi-

eoreti-

nd

ions.

ut are

pari-

tter char-

this case, AIMR estimates are close to the averages compiled by Carsey at both 37 GHz a

GHz, and are again slightly above the range determined by Comiso for July.

5. Conclusions

An algorithm for calculating microwave sea ice emissivity from airborne radiometer data

been developed. The method accounts for emission by the surface, atmospheric upwelling

reflection of downwelling atmospheric radiation. Infrared surface temperatures and vertical

files of atmospheric temperature and humidity are used as input. Emissivities at a range of

quencies have been calculated around the SHEBA site for clear sky days during the FIRE-SH

aircraft campaign in the spring and summer of 1998.

While the available data set is not large enough to draw conclusions about day-to-day v

tions in emissivity, the limited cases analyzed point out some of the important consideration

including surface effects in cloud retrievals from passive microwave data:

1) The presence of snow on the ice has a large impact on the microwave emissivity, espec

frequencies above 37 GHz. Specifically, the snow depth in relation to penetration depth at a

frequency determines whether the emissivity will be characteristic of sea ice alone, or of a c

nation of sea ice and snow. The relationship between emissivity at different frequencies is

strongly dependent on the nature of the surface. Spectral differences in emissivity could th

cally be used as an indicator of snow cover and possibly snow depth.

2) Comparisons of AIMR estimates with previous emissivity measurements from satellite a

groundbased sensors give good results at both frequencies during the summer melt condit

Estimates are higher than those cited in the literature for multiyear ice at 37 GHz in May, b

closer for 90 GHz in May. Given the uncertainties in each data set, the relatively good com

sons give us some confidence in the measurements produced by the airborne sensors. Be

18

n, are

ed

rces of

us to

rison

a, and

nly

et

ing the

verage

triev-

irly

n be

set,

her

acterization of the errors inherent in AIMR measurements, particularly at the edges of a sca

recommended.

3) Comparison of 89 and 90 GHz emissivities on the one day that both AIMR and MIR view

the SHEBA area reveals a difference of about 7% in the mean values. Given the many sou

uncertainty, this sort of bias is not unexpected. Comparison of just one case does not allow

draw any conclusions regarding the accuracy of either sensor. A more enlightening compa

might be made by georeferencing both data sets, degrading the resolution of the AIMR dat

performing a pixel-by-pixel comparison of the AIMR and MIR estimates. Though there was o

one clear sky day on which both aircraft flew in the SHEBA region, the FIRE-SHEBA data s

should be examined for other colocated flight segments outside the experimental region.

4) The range ofεs values in summertime is less than in spring, and the horizontal variability

(exclusive of leads) is smaller. Thus the summer season may be an easier time for estimat

surface contribution in cloud retrieval schemes, because it may be possible to assume an a

value ofεs. Frequent cloud cover in the summer also make it a good time to attempt cloud re

als. In both May and July, the standard deviation of emissivity values in the multiyear ice is fa

small (at 90 GHz) over the horizontal scales examined here. Thus, in cases where leads ca

detected through cloud cover, it might be feasible to assume a constant value ofεs for multiyear

ice pixels in cloud retrieval algorithms. This possibility should be explored with a larger data

and resulting errors in cloud retrievals should be quantified.

5) The spectral variation ofεs is dependent upon surface properties, so usingεs at low frequencies

(which are more transparent to the atmosphere) to estimateεs at high frequencies where cloud

retrievals could be performed would require independent information about the surface. Ot

sensors, such as Synthetic Aperture Radar (SAR), might be useful for this purpose.

19

IRE

r

for

con-

Acknowledgments: This research was supported by the NSF SHEBA program, the NASA F

program, and a NASA Earth System Science Fellowship. We would like to thank J. Wang fo

making the MIR data available to us and C. Walther of the NCAR Remote Sensing Facility

assistance with the AIMR data. Discussions with G. Liu and J. Maslanik were very helpful in

ducting this research.

20

Sur-

I:

infra-

llime-

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23

A

rature

(c)

ts on

he C-

t: (a)

siv-

May

The

mate

FIGURE CAPTIONS:

Figure 1: Flight tracks of the NASA ER-2 and NCAR C-130 aircraft in the vicinity of the SHEB

ship on May 20, 1998.

Figure 2: Surface temperature corrections as a function of flight altitude and surface tempe

as measured by the KT19 radiometer: (a) corrections for May 20, 1998; (b) May 24, 1998;

July 8, 1998.

Figure 3: Sea ice emissivity at nadir for 37 and 90 GHz as derived from AIMR measuremen

May 20, 1998. The five discrete segments correspond to the east-west flight segments of t

130 shown in Figure 1.

Figure 4: As in Figure 3, but for July 8, 1998

Figure 5: Frequency distributions of sea ice emissivity at nadir along a C-130 flight segmen

37 GHz emissivity on May 20,1998; (b) 90 GHz emissivity on May 20,1998; (c) 37 GHz emis

ity on July 8, 1998; (d) 90 GHz emissivity on July 8, 1998.

Figure 6: Frequency distributions of sea ice emissivity at nadir along the ER-2 flight track on

20, 1998; (a) 89 GHz; (b) 150 GHz; (c) 220 GHz.

Figure 7: Images of sea ice emissivity from AIMR at 37 GHz and 90 GHz on May 20, 1998.

flight track is along the vertical axis of each image and passes over the SHEBA ship. Approxi

dimensions of the images are 17 km by 50 km.

24

Table 1: Microwave penetration depths in cm at -10°C

37 GHz 89-90 GHz 150 GHz 220 GHz

Dry Snow 92 22 10 5.5

Multiyear Ice 7 4 2 1.5

First Year Ice 1.4 0.94 0.75 0.61

Table 2: Emissivity at 50° for multiyear ice (MYI)

37 GHz (H) 37 GHz (V) 90 GHz (H) 90 GHz (V)

AIMR (late May) 0.90 0.93 0.73 0.76

Carsey (dry MYI) 0.706 (0.079)a

a. Standard deviations given in parentheses

0.764 (0.115) 0.650 (0.011) 0.680 (0.105)

Comiso (MYI, late May) 0.75-0.88 0.77-0.90 -- --

Table 3: Emissivity at 50° for summer melt conditions

37 GHz (H) 37 GHz (V) 90 GHz (H) 90 GHz (V)

AIMR (early July) 0.92 0.96 0.89 0.93

Carsey (summer melt) 0.93 (0.010)a

a. Standard deviations given in parentheses

0.97 (0.015) 0.92 (0.010) 0.95 (0.020)

Comiso (MYI, July) 0.82-0.92 0.89-0.92 -- --

25

Figure 1

-170 -168 -166 -164 -162 -160Longitude (degrees)

75.0

75.5

76.0

76.5

77.0

77.5

Lat

itude

(de

gree

s)

26

Figure 2(a)

KT19 corrections for May 20, 1998 (RF06)

262 264 266 268 270 272 274 276Tkt19 (deg K)

-2

-1

0

1

2

DT

=T

s-T

kt19

(de

g K

)

27

Figure 2(b)

KT19 corrections for May 24, 1998 (RF07)

262 264 266 268 270 272 274 276Tkt19 (deg K)

-2

-1

0

1

2

DT

=T

s-T

kt19

(de

g K

)

28

Figure 2(c)

KT19 corrections for July 8, 1998 (RF09)

262 264 266 268 270 272 274 276Tkt19 (deg K)

-2

-1

0

1

2

DT

=T

s-T

kt19

(de

g K

)

29

Figure 3

Emissivity at 37 and 90 GHz (May 20, 1998)

21.0 21.2 21.4 21.6 21.8 22.0Time (hrs)

0.50

0.60

0.70

0.80

0.90

1.00

Emiss

ivity

30

Figure 4

Emissivity at 37 and 90 GHz (July 8, 1998)

21.00 21.10 21.20 21.30 21.40Time (hrs)

0.50

0.60

0.70

0.80

0.90

1.00

Emiss

ivity

31

Figure 5 (a,b)

Emissivity Distribution (May 20, 1998)

0.50 0.60 0.70 0.80 0.90 1.00Emissivity

0

100

200

300

400

500

Num

ber

of P

ixel

s

90 GHz

37 GHz

32

Figure 5(c,d)

Emissivity Distribution (July 8, 1998)

0.50 0.60 0.70 0.80 0.90 1.00Emissivity

0

50

100

150

Num

ber

of P

ixel

s

37 GHz

90 GHz

33

Figure 6 (a,b,c)

Emissivity Distribution (May 20,1998)

0.50 0.60 0.70 0.80 0.90 1.00Emissivity

0

50

100

150

Num

ber

of P

ixel

s

89 GHz

150 GHz

220 GHz

34

Figure 7