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)[email protected]
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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from
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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