A method for detecting cirrus clouds in terms of brightness temperature differences between narrow bands at 8, 11, and 12 mu m has been proposed by Ackerman et al. (1990). In this method, the variation of emissivity with wavelength for different surface targets was not taken into consideration. Based on state-of-the-art laboratory measurements of reflectance spectra of terrestrial materials by Salisbury and D'Aria (1992), we have found that the brightness temperature differences between the 8 and 11 mu m bands for soils, rocks and minerals, and dry vegetation can vary between approximately -8 K and +8 K due solely to surface emissivity variations. We conclude that although the method of Ackerman et al. is useful for detecting cirrus clouds over areas covered by green vegetation, water, and ice, it is less effective for detecting cirrus clouds over areas covered by bare soils, rocks and minerals, and dry vegetation. In addition, we recommend that in future the variation of surface emissivity with wavelength should be taken into account in algorithms for retrieving surface temperatures and low-level atmospheric temperature and water vapor profiles

Gao, Bo-Cai

Wiscombe, W. J.

English

NASA Technical Reports Server

i  7-7s9 . 4
Surface-Induced Brightness Temperature Variations and Their Effects on Detecting ThinCin'us Clouds
i'i
Bo-Cai Gao, and W. J. Wiscombe
Climate & Radiation Branch, Code 913, NASA Goddard Space Flight Center
Greenbelt, MD 20771
Abstract
A method for detecting cirrus clouds in terms
of brightness temperature differences between
narrow bands at 8, 11, and 12 _rn has been
proposed by Ackerman et al. (1990). In this
method, the variation of emissivity with
wavelength for different surface targets was not
taken into consideration. Based on state-of-the-
art laboratory measurements of reflectance
spectra of terrestrial materials by Salisbury and
D'Aria (1992), we have found that the brightness
temperature differences between the 8 and 11 _m
bands for soils, rocks and minerals, and dry
vegetation can vary between approximately -8 K
and +8 K due solely to surface emissivity
variations. We conclude that although the
method of Ackerman et al. is useful for detecting
cirrus clouds over areas covered by green
vegetation, water, and ice, it is less effective for
detecting cirrus clouds over areas covered by bare
soils, rocks and minerals, and dry vegetation. In
addition, we recommend that in future the
variation of surface emissivity with wavelength
should be taken into account in algorithms for
retrieving surface temperatures and low-level
atmospheric temperature and water vapor
profiles.
1. Introduction
Thin cirrus clouds partially scatter solar
radiation back to space and partially prevent the
escape to space of the longwave emission
originating from the surface and lower
atmosphere. They are difficult to detect in
images taken from current satellite platforms.
The NASA HRE Cirrus Program (Start, 1987) was
designed to improve our understudying of
physical and radiative properties of cirrus clouds
and their effects on the climate system.
Ackerman et al. (1990) developed a method
for detecting thin cirrus clouds using three narrow
channels at 8 (8.3-8.4), 11 (11.06-11.25), and 12
(11.93-12.06) _m. The method, referred to as the
'8-11-12 _ method' hereinafter, was based on
the analysis of data acquired with the Cloud and
Aerosol Lidar (CALS) (Spinhirne and Hart, 1990)
and the High-spectral resolution Interferometer
Sounder (HIS) (Revercomb et al., 1988) on board
an ER-2 aircraft over _/reas covered by vegetation
and water in Wisconsin during the first FIRE
Cirrus Field Experiment in October 1986. Figure 1
illustrates the method (Ackerman et al., 1990).
Basically, the cloud-free regions have negative
brightness temperature differences between the 8
and 11 _tm channels (A BT (8 - 11)) while the
areas covered by cirrus clouds have positive
brightness temperature differences (> 1 IO. The
additional use of brightness temperature
differences between the 11 and 12 _m channels,
A BT (11 - 12), is for the purpose of separating the
cirrus clouds from the low level water clouds.
When applying the 8-11-12 _tm method to a
similarly measured data set but over a different
geographic location, the method failed to detect
thin cirrus clouds (S. A, Ackerman, personal
communication, 1992).
During their development of the 8-11-12 _m
method, Ackerman et al. (1990) carefully
considered the absorption and scattering
properties of water and ice particles, and the
absorption and emission effects of atmospheric
water vapor. The variation of surface emissivity
with wavelength was not taken into
consideration, however.
Recently, Salisbury and D'Aria (1992) have
made state-of-the-art laboratory measurements
of reflectance spectra of terrestrial materials
between 2 and 14 _.m. We have studied surface-
induced brightness temperature variations in the
8-14 _rn region using these spectra. Some of our
findings relevant to remote sensing of cirrus clouds
are presented in this paper.
197
https://ntrs.nasa.gov/search.jsp?R=19940017868 2020-06-16T18:23:14+00:00Z
65
4
3
7
m 2
[-.,
rn I
<3
-1
-2
!
UDAA CLOOO H_
_" I0 ,% 9.5 [3 9 O 8.5 - 8 ,, 3 • 0
• + ."
a 2, + a
+ *
6 • ,"
° _OO O . '"
&_o " • WATER
o _ + •
5
,'(_ •
o .". o
o" " °
"%:
. . . [ . . i .... I .... L • , , , I .... I ....
o _ .... +.... :+ ++ _4_ ._ s
ABT (xt-lz)
Fig. 1: Scatter diagram of A BT (8 - 11) versus A
BT (11 - 12) (Ackerman et al., 1990). Each symbol
represents a range in cloud altitude determined
with the Cloud and Aerosol Lidar (CALS)
(Spinhirne and Hart, 1990).
2. Surface Reflectance Spectra
Directional hemispherical reflectance
spectra of a variety of rocks and minerals, soils,
green and dead vegetation, water and ice have
been made and summarized by Salisbury and
D'Aria (1992). They were measured using Fourier
transform spectrometers with integration sphere
attachments. Figure 2 shows examples of
reflectance spectra of three types of rocks; various
absorption features due to vibrational band
transitions can be seen. The reflectances in the 11
(11.06-11.25) grn region for ijolite and limestone
are greater than those in the 8 (8.3-8.4) pm
region, while the converse is true for sandstone.
Figure 3 shows examples of reflectance spectra of
three types of soils; various absorption features
can be seen. Figure 4 shows sample reflectance
spectra of green and dry vegetation. The green
vegetation has nearly a constant reflectance
value (0.02) in the 8-14 _ region, while the dry
vegetation has several absorption bands.
Figure 5 shows sample reflectance spectra of
sea water and sea ice (rough surface); the
D.)
Z
[--,
L)
f.x.,
0._'0 + I I I +
--- SANDSTONE
t_I ........... LIMESTONE
0.15 i
I _. .... IJ0UTE
0.10 s ._
L]'\'I " ii
0.05 "'l ..-'" [. _..+
. ..
•....... "......... ._.*.
0.00 ,j j I i J
8 9 I0 II 12 13 14
WAVELENGTH (/_m)
Fig.2:Examples ofreflectancespectraofthree
typesof soils(Salisburyand D+Aria,1992).
0.20 I I I # i
• -ENTISOL
\ ........... AR|DISOL
0.15 - - SPODOSOL
O
Z
,<
0.10
0.o°"'"-'...... ""......,
0.00 I l s i
8 9 I0 II 12 13 14
WAVELENGTH (_m)
Fig. 3: Examples of reflectance spectra of three
types of soils (Salisbury and D'Ana, 1992).
reflectances are small (between approximately
0.01 and 0.04). The reflectance minimum for sea
water is located near 11 gm, that for sea ice near
10.3 I_rn. The emissivity is defined as one minus
reflectance. It is easy to see from Figures 2, 3, 4,
and 5 thatt_e emissivities for soils,, rpc_ksand
minerals, and dry vegetation in the 8-14 Inn
region vary significantly with wavelength. The
emissivities for green vegetation, water, and ice
do not vary much with wavelength and are
generally greater than 0.96.
198
0.20
DRY VEGETATION
0.15 ........GREEN VEGETATION
{,j
z
o 0.10
0.05
...... • "*°. ....... .,... ......... . ................ , ..... 0 .... •
0.00 J J i J
8 9 I0 II 12 13 14
WAVELENGTH (Nm)
Fig. 4: Examples of reflectance spectra of dry and
green vegetation (Salisbury and D'Aria, 1992).
O.OB
_a 0.04
0
z
[-.
o
_ 0.02
I I I I )
-- SEA WATER
........SEA ICE (ROUGH SURFACE)
"...."
0.00 l i I l t
O 0 10 II 12 13
WAVELENGTH (/_m)
Fig. 5: Examples of reflectance spectra of sea
water and ice (Salisbury and D'Aria, 1992).
14
3. Sensitivity of Brightness Temperature to
Emissivity
The brightness temperature of a surface is a
function of both its kinetic temperature and
emissivity. We have studied the sensitivity of
the Planck function to changes in temperatures for
surface temperatures of 270, 280, 290, and 300 K.
From these sensitivities, we have derived the
sensitivities of brightness temperatures to
emissivities. The results show that, for kinetic
temperatures between 270 and 300 K, an error of
0.01 in assumed surface emissivity results in errors
"=4
"4
!
OO
v
b-,
en
IO I
+
+
+
+
+
+
+ o °o
+ _o
++ __[]
+ ++t_n
+
+ 11(
+ ROCKS ,t-MINERALS
_( SOILS
O DEAD VEGETATION
A GREEN VEGETATION
[3 WATER & ICE
l I
-6 -4
+
-5 )td-
w£
+++
-10 z 1
-0 -2 0 2
ABT(II - 12)
Fig. 6: Scatter diagram of surface-induced A BT (8
- 11) versus A BT (11 - 12) for rocks and minerals,
soils, dead and green vegetation, and water and
ice (rough surface).
of approximately 0.48 K in surface temperature at
8.35 p.m, 0.63 K at 11.155 _xn, and 0.67 K at 11.995
tan.
4. Results
Based on the sensitivity of brightness
temperatures to surface emissivity described in
Section 3 and using the reflectance data from
Salisbury and D'Aria (1992), we have quantified
the surface-induced brightness temperature
variations in the 8, 11, and 12 pan region for soils,
rocks and minerals, green and dry vegetation, and
water and ice. Figure 6 shows the scatter diagram
of the A BT (8 - 11) versus A BT (11 - 12). The
surface-induced A BT (8 - 11) can vary between
approximately -8 K and 8 K for rocks and
minerals, 03 to 7 K for dead vegetation, -6 and 1
K for soils. These differences are greater than the
A BT (8 - 11) in Figure 1.
The large differences in the surface-induced
A BT (8 - 11) are sufficient to cause confusion
when using the 8-11-12 tan method for detecting
cirrus clouds. For example, if the measurements
were made from high-altitude aircraft or
satellite platforms over areas covered by dead
vegetation but no cirrus clouds, the straight
forward application of the 8-11-12 Pan method
would misidentify the areas as being covered by
cirrus clouds. On the other hand, if the same
199
measurements were made over areas covered by
bare soils with a -4 K surface-induced
A BT (8 - 11) and also covered by very thin cirrus
clouds with a 1 K cirrus-induced A BT (8 - 11), the
application of the 8-11-12 _m method would
identify the areas as being clear.
5. Discussion
Because of the traditional lack of reliable IR
emissivity measurements (Salisbury and D'Aria,
1992) of terrestrial materials, algorithms
developed for retrieving atmospheric
temperatures and surface temperatures from
satellite IR emission channels typically assume
constant values of emXssi-v_t_o-u_-0Z_6) in the
7. Acknowledgments
The authors are grateful to J. W. Salisbury at
the Department of Earth and Planetary Sciences,
Johns Hopkins University for providing the
emissivity spectra of terrestrial materials in
digital form. This research was partially
supported by the NASA FIRE Project Office
through a grant to the NASA Goddard Space
Flight Center.
8. References
Ackerman, S., W. L. Smith, J. Spinhirne, and H.
Revercomb, The 27-28 October FIRE IFO cirrus
8-14 _ra spectral region. As a resultTe_6fs 6f i0 K ...... in theS-i2_-ii)indow, Mon. Wea. Rev_118,
or greater in the derived surface temperatures
from current satellite IR emissionmeasurements
exist (Wu and Chang, 1991).
6. Summary
We have studied surface-induced brightness
temperature variations in the 8-12 _m spectral
region using laboratory measurements of
emissivity of terrestrial materials (Salisbury
and D'Aria, 1992). It is found that the brightness
temperature differences between the 8 and 11
bands for soils, rocks and minerals, and dry
vegetation can vary between approximately -8 K
and 8 K. We conclude that although the 8-11-12
_n method of Ackerman et al. (1990) is useful for
detecting cirrus clouds over areas covered by green
vegetation, water, and ice, the technique is less
effective for detecting cirrus clouds over areas
covered by bare soils, rocks and minerals, and dry
vegetation. We suggest that the variation of
2377-2388, 1990.
Revercomb, H., H. Buijs, H. Howell, D. LaPorte,
W. L. Smith, and L. Sromovsky, Radiometric
calibration of IR Fourier transform
spectrometer: _ution_t0 a problem with the
High-spectral resolution Interferometer
Sounder, Appl. Opt., 27, 3210-3218, 1988.
Salisbury, J., and D. D'Aria, Emissivity of
terrestrial materials in the 8-14 _'n
atmospheric window, Remote Sens. Env., 42,
83-106, 1992.
Spinhirne, J., and W. Hart, The 27-28 October
1986 FIRE cirrus case study: ER-2 lidar and
spectral radiometer cirrus observations, Mon.
Wea. Rev., 118, 2329-2343, 1990.
Starr, D., A cirrus cloud experiment: Intensive
field observations planned for FIRE, Bull.
be taken into acc0unt in algorithr_s_fo-r retrieving
surface temperatures and low-level atmospheric
temperature and water vapor profiles.
Amer. Meteor. Soc., 67, 119-124, 1987.surface emissivity with wavelength should also
Wu, M.-L., and L. Chang, Differences in global
data sets of atmospheric and surface
parameters and their impact on outgoing
longwave radiafionand surface downward
flux calculations, J. Geophys. Res., 96, 9227-
9262, 1991.
200


Surface-induced brightness temperature variations and their effects on detecting thin cirrus clouds using IR emission channels in the 8-12 micrometer region

Surface-induced brightness temperature variations and their effects on detecting thin cirrus clouds using IR emission channels in the 8-12 micrometer region

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