Monitoring and Quantitatively Retrieving Dust Storm from Satellite
Thermal Infrared Measurements
Zhang P.1, Li J.2, Lu N. M.1, Hu X. Q.1, Schmit T. J.3
1 Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites
(LRCVES)/CMA
2 Cooperative Institute for Meteorological Satellite Studies/University of Wisconsin-Madison
3 Office of Research and Applications/NESDIS/NOAA
No. 46, South street of Zhong-Guan-Cun, District Haidian, Beijing, 100081, China
[email protected]
Tel. 86-10-68409671, Fax. 86-10-62183844
Abstract
In this paper, the possibility of monitoring and quantitatively retrieving dust storm from
satellite thermal infrared measurements was investigated. It was found from satellite
observations that for the dust clouds, the observed 11 μm minus 12 μm brightness temperature
difference (BTD) is always negative, while the BTD of 8.5 μm minus 11 μm varies from
positive to negative depending on the dust concentration. Based on the threshold
classifications, a dust mask algorithm to identify the dust storm initiation and spatial extent
was developed. The algorithm can be used to process data for both the daytime and nighttime.
Under the Mie spherical scattering theory, the thermal radiation transfer through the single
dust layer were performed by the widely used forward model DISTORT. Our calculations
show that the dust-like aerosols can fairly well explain the observed BTD although both of the
complex refractive index and particle size of aerosols will significantly influence the BTD.
When the complex refractive index is fixed (dust-like aerosols in this paper), the dust optical
thickness and effective radius of dust particles can be retrieved from the brightness
temperature (BT) of the 11 μm channel and the BTD of 11 μm minus 12 μm channels,
respectively. The integral dust columnar density can be also derived from retrieved dust
optical thickness and effective radius. The algorithm developed here has been applied to the
MODIS/Terra (polar satellite) and the SEVIRI/MSG (Geostationary satellite) data.
Keywords: Dust storm, Thermal Infrared, Satellite, Brightness temperature
INTRODUCTION
Satellite monitoring is a powerful tool for studying the properties of large-scale dust storms. Since the
1970s, scientists have succeeded in identifying the outbreaks of dust storms from satellite images by
use of the thermal infrared (TIR) window technique (Shenk and Curran, 1974; Ackerman, 1989). More
recently, Wen and Rose (1994) developed an algorithm based on the TIR technique for the retrieval of
particle size, optical thickness and total mass of volcanic cloud by using the AVHRR band 4 and 5.
The concept of their algorithm is similar to that for the retrieval of cirrus using the BTD method (Wu,
1987; Giraud, et al., 1997). Dust aerosols have the very similar radiative properties with volcanic
aerosols. Therefore, the principle is applicable to remote sense the dust storm.
In this paper, we have developed an algorithm to identify and physically retrieve the dust storm with
three satellite TIR window channels. Because only TIR channels are used, the algorithm is applicable
to both daytime and nighttime conditions. The algorithm developed here has been applied successfully
to the MODIS/EOS (polar satellite) and the SEVIRI/MSG (Geostationary satellite) data.
DUST STORM MASK ALGORITHM
Threshold method is used to identify the dust storm initiation and spatial extent. Because of the
surface emissivity, different thresholds are selected over non-desert area and over desert, listed in
Table 1 and Table 2. The dust storm mask results are given in Fig. 1 and Fig. 2 respectively. Fig. 1
shows the dust storm monitored by MODIS/Terra on 7 April, 2001 over most of northern China, which
is the strongest among year 2001. Fig. 2 shows the dust storm monitored by SEVIRI/MSG on 3
March, 2004 over Sahara desert. As the comparison, the satellite RGB color composition images have
been given too.
Over non-desert area, the brightness temperature difference of 11 μm minus 12 μm (BTD11-12) alone
can work well to identify the dust storm from the background. However, it is insufficient over desert
area because the desert surface and dust layer have the very similar behaviors at 11 μm and 12 μm.
As an important remediation, 8.5 μm can provide the additional information to distinguish the
suspended dust from the desert surface effectively. Moreover, the brightness temperature difference
of 8.5 μm minus 11 μm (BTD8-11) is a pretty good index to indicate the strength of dust storm.
By combination the thresholds of BTD11-12 and BTD8-11, the mask algorithm can automatically
identify the outbreak and the relative strength of dust storm from these three TIR observations.
Because of the spectral variation of surface emissivity, the surface usually contributes about -1.0 to 0.5 K in BTD11-12. Therefore, the threshold of BTD11-12 was set as -0.5 K over non-desert area and
-1.0 K over desert separately. There is a substantial advantage to use BTD as the criteria to identify
dust storms because BTD is less dependent on the surface temperature than BT.
a
b
Fig. 1 Dust storm on 7 April, 2001 over most of northern China
a) RGB composition image from MODIS channel 1 (645nm), 4 (555nm) and 3 (469nm); b) Dust storm mask results by
threshold method listed in Table 1.
b
a
Fig. 2 Dust storm monitored on 3 March, 2004 over Sahara desert
a) RGB composition image from SEVIRI/MSG 11 μm, 11-12 μm, 8-11 μm; b) Dust storm mask results by threshold
method listed in Table 2.
Threshold
BTD11-12<-0.5 and
BTD8-11>0
BTD11-12<-0.5 and
BTD8-11<0
BTD11-12>0 and
BTD8-11>0
BTD11-12>0 and
BTD8-11<0
0>BTD(11-12)>-0.5
Mask Flag
1
Description
relative strong dust region
2
relative weak dust region
3
ice cloud
4
low cloud or surface
5
uncertain region
Table 1 Thresholds for the identification of dust storm over non-desert area
Threshold
BTD11-12<-1 and
BTD8-11>-5
others
Mask Flag
0
Description
Dust
1
No Dust
Table 2 Thresholds for the identification of dust storm over desert area
FORWARD MODEL AND SIMULATION
A two-layer TIR band forward model is developed to simulate thermal infrared radiation transfer
through dust layer and to simulate the satellite measurements (Fig. 3). The forward model consists of
the DISORT model (Stamnes et al., 1988) and a Mie scattering code (Zhang and Shi, 1997). With the
selected dust aerosol parameters (i.e., complex refractive index, particle size distribution and particle
shape), the single scattering albedo, ω0, asymmetry factor, g, and extinction coefficient ratio, k, of the
dust layer can be calculated from the Mie code. These three optical parameters of dust aerosol are
necessary for the radiation transfer calculation. By inputting them together with given optical thickness
of dust layer into DISORT, the satellite observation can be simulated for arbitrary wavelengths.
Fig. 3 Conception of two-layer IR band forward model
In the two-layer forward model, satellite observation in the TIR band is composed of two parts, the
radiance emitted from the dust layer and that transmitted from the underlying surface. The observed
radiance by the satellite through such semitransparent dust layer can be expressed as
I i = ε c B(Tc ) + t atm B (Tg )
(1)
where Ii is the observed radiance in an given channel, εc, and tatm are emissivity and transmissivity of
the dust layer in the central wavelength of given channel, Tg and Tc are the underlying surface
temperature and equivalent top temperature of the dust layer. The emissivity of surface is assumed as
blackbody in (1).
Several assumptions are made to simplify the calculation. These are: 1) the underlying surface is
homogeneous; 2) the dust cloud is a single layer parallel to the surface; 3) the atmosphere above the
dust layer and between the surface and the dust layer are clear windows and 4) the dust layer
completely fills the field of observation. Other assumptions in the simulation also include spherical
particle shape, logarithm normal particle size distribution, and complex refractive index of dust-like
aerosol for aerosol model.
Fig. 4 shows the simulation results from the forward model. Green, blue, black lines represent the
results calculated from Tg = 290 K and Tc = 270 K, Tg = 290 K and Tc = 250 K, Tg = 290 K and Tc =
230 K, respectively. Lines with †, ◊ and □ represent the results obtained by assuming dust effective
radius to be 13, 8 and 2 μm, respectively. In each line, 20 optical thicknesses from 10 to 0.1 are
considered in the direction of abscissa.
Fig. 4 Simulation results calculated by the forward model.
Abscissa is BT11 and ordinate is BTD11-12. Green, blue, black lines represent the results calculated from Tg = 290 K
and Tc = 270 K, Tg = 290 K and Tc = 250 K, Tg = 290 K and Tc = 230 K, respectively. Lines with †, ◊ and □ represent the
results obtained by assuming dust effective radius to be 13, 8 and 2 μm, respectively. In each line, 20 optical
thicknesses from 10 to 0.1 are considered in the direction of abscissa.
From Fig.4, it can be seen that BT11 decreases with increasing optical thickness while BTD11-12
decreases with the increase of particle effective radius. They indicate that it is possible to retrieve the
optical thickness and particle size of dust from the combination of BT11 and BTD11-12 information.
Fig. 4 also shows that BTD11-12 decreases with the decrease of the temperature difference between
Tg and Ts. The larger the temperature difference of Tg minus Tc is, the larger the BTD11-12 caused
by dust particle size. They imply that both Tg and Tc will strongly affect the calculation of BT11 and
BTD11-12. Therefore, the accuracy of Tg and Tc will determine the reliability of the retrieved results.
DUST STORM RETRIEVAL ALGORITHM
An algorithm has been developed to retrieve the optical thickness and particle effective radii of the
dust layer (Zhang, et al., 2005). The retrieval processing relies on the BT11 and BTD11-12 pattern.
Forward simulations have showed that BT11 and BTD11-12 are sensitive to the optical thickness and
particle size of the dust layer. Whilst, the underlying surface temperature, Tg, effective top
temperature of dust layer, Tc and the chemical composition of dust aerosol, i.e., the complex refractive
index of dust aerosol will influence the calculated BT11 and BTD11-12 too. However, if Ts, Tc and
refractive index can be derived from the pre-knowledge, BT11 presents an almost linear relationship
with dust optical thickness and BTD11-12 presents an almost linear relationship with dust particle size.
By fitting the satellite observations to the pre-calculated BT11 and BTD11-12 look-up table, the optical
thickness and particle effective radii of the dust layer can be retrieved. When dust optical thickness
and particle effective radius have been retrieved, dust column density can be obtained by using of
following formula uniquely
n(r ) =
⎡ (ln r − ln r0 )2 ⎤
1
exp ⎢−
⎥
2
2(ln σ ) ⎥⎦
2π r ln σ
⎢⎣
N0
(2)
m = ρ sandsV0 = ρ sands ∫ v(r )dr
(3)
4
v(r ) = πr 3 n(r )
3
(4)
r2
r1
N0 =
τ
∫
r2
r1
Qeπr 2 n(r )dr
(5)
()
where in (2), the lognormal distribution (LND) is selected for dust aerosols, n r is the particle number
size distribution, N0 is the number of the particles per cross section of atmospheric column (m-2), r is
the particle radius, r0 is the modal radius of LND, ln σ is the standard deviation of ln r. From (3) to (5),
m is the column aerosol loading in mass,
ρ sands = 2.5 g / cm
3
()
ρ sands
is the mass density of sands and we take
in this paper. v r and V0 are the volume size distribution and the volume of the
particles per cross section of atmospheric column, Qe is extinction coefficient of particle decided by
Mie scattering. It is clear that the dust column density, N0, is related to the optical thickness, τ, by (5).
In addition, the inversion of the optical thickness to the dust column density will be influenced by both
the complex refractive index and the particle size distribution of aerosols.
The methodology of the fitting and retrieval process is illustrated by Fig. 5. The kernel of the process is
a pre-calculated look-up table (LUT). The LUT is calculated with 9 Ts (from 310 K to 270 K in 5 K
increment), 9 Tc (from 270 K to 230 K in 5 K increment), 20 optical thickness (from 10 to 0.1 in equal
logarithm increment), 29 particle effective radius (from 0.01 μm to 10 μm in equal logarithm increment)
and fixed refractive index in this paper.
The dust retrieval algorithm has been applied to both MODIS/Terra and SEVIRI/MSG data. Two
examples are showed in Fig. 6 and Fig. 7, corresponding to the retrieval results from China case on 7
April, 2001 by MODIS/Terra and from Sahara case on 3 March, 2004 by SEVIRI/MSG respectively.
Fig. 5: Flow chart of fitting and retrieval process
Fig. 6 The retrieval results on 7 April, 2001 over most of northern China
Fig. 6a, b, c show the retrieved dust optical thickness (dimensionless), effective radius of dust particle (in µm) and
integral dust columnar density (in 106 µg m-2) respectively.
Fig. 7 The retrieval results on 3 March, 2004 over Sahara desert
Fig. 7a, b, c show the retrieved dust optical thickness (dimensionless), effective radius of dust particle (in µm) and
integral dust columnar density (in 106 µg m-2) respectively.
CONCLUSION
A dust storm mask and retrieval algorithm has been developed. Because the algorithm is based on the
satellite TIR measurements, it can be used in both daytime and nighttime conditions for automatic
processing. The algorithm has been applied to both MODIS/Terra and SEVIRI/MSG data. The dust
mask and retrieval results from the case study have showed that:
1) BTD11-12 alone is not sufficient to identify the outbreak and the spatial extent of the dust storm. 8.5
um channel provide the additional information. Over desert, this channel is especially helpful to
distinguish the suspended dust from the desert surface effectively. While over non-desert area, the
brightness temperature difference of 8.5 μm minus 11 μm (BTD8-11) is a pretty good index to indicate
the strength of the dust storm.
2) Optical thickness, particle effective radii and integral dust column density can be retrieved from the
satellite measured BT11 and BTD11-12. While, the effective surface temperature of the underlying
(including the surface emissivity effect), the effective top temperature of the dust layer, and the
complex refractive index of the dust aerosol will considerably influence on the reliability of the retrieval
results. The uncertainties of these factors will decrease the accuracy of the retrieval results. The
detailed error analysis is required.
The algorithm will be improved in the future on 1) the more accurate forward model to include
atmospheric profile and surface emissivity effect, 2) better determination of the effective surface
temperature of the underlying and the effective top temperature of the dust layer, 3) Validation from
ground-based measurements and other satellite results, such as TOMS results.
REFERENCES
Ackerman, S., (1989） Using the radiative temperature difference at 3.7 and 11 μm to track dust
outbreaks. Remote Sens. Environ. 27(2), 129-133.
Giraud, V., Buriez, J.C., Fouquart, Y., Parol, F., (1997) Large-scale analysis of cirrus clouds from
AVHRR data: assessment of both a microphysical index and the cloud-top temperature. J. Appl.
Meteorol. 36 (6), 664–675.
Stamnes, K., Tsay, S.C., Wiscombe, W., Jayaweera, K., (1988) Numerically stable algorithm for
discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Appl.
Opt. 27(12), 2502-2509.
Shenk, W.E., Curran, R.J., (1974) The detection of dust storms over land and water with satellite
visible and infrared measurements. Mon. Weather Rev. 102(12), 830-837.
Wen, S., Rose, W.I., (1994) Retrieval of sizes and total masses of particles in volcanic clouds using
AVHRR bands 4 and 5. J. Geophys. Res. 99 (D3), 5421-5431.
Wu, M.L., (1987) A method for remote sensing the emissivity, fractional cloud cover and cloud top
temperature of high-level, thin clouds. J. Clim. Appl. Meteorol. 26(2), 225-233.
Zhang, P., Shi, G.Y., (1997) Optical characteristic Mie scattering calculation using an aerosol
distribution observed by balloon. Proceedings of the 6th Chinese Aerosol Conference, pp.57-61.
Zhang, P., N. Lu, X. Hu, C. H. Dong., (2006) Identification and physical retrieval of dust storm using
three MODIS thermal IR channels. Global and Planetary Chance, 52, 197-206.