Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/jgrd.50863
Correspondence to:
C. Serio,
[email protected]
Diurnal variation in Sahara desert sand emissivity during the
dry season from IASI observations
Guido Masiello1 , Carmine Serio1 , Sara Venafra1 , Italia DeFeis2 , and Eva E. Borbas3
1 School of Engineering and CNISM, Potenza Research Unit, University of Basilicata, Potenza, Italy, 2 IAC-CNR, Naples, Italy,
3 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin, USA
Citation:
Masiello, G., C. Serio, S. Venafra,
I. DeFeis, and E. E. Borbas (2014),
Diurnal variation in Sahara desert
sand emissivity during the dry
season from IASI observations,
J. Geophys. Res. Atmos., 119, 1626–1638,
doi:10.1002/jgrd.50863.
Received 17 MAY 2013
Accepted 22 SEP 2013
Accepted article online 30 SEP 2013
Published online 7 FEB 2014
Abstract The problem of diurnal variation in surface emissivity over the Sahara Desert during
non-raining days is studied and assessed with observations from the Infrared Atmospheric Sounding
Interferometer (IASI). The analysis has been performed over a Sahara Desert dune target area during July
2010. Spinning Enhanced Visible and Infrared Imager observations from the European geostationary
platform Meteosat-9 (Meteorological Satellite 9) have been also used to characterize the target area.
Although the amplitude of this daily cycle has been shown to be very small, we argue that suitable
nighttime meteorological conditions and the strong contrast of the reststrahlen absorption bands of
quartz (8–14 μm) can amplify its effect over the surface spectral emissivity. The retrieval of atmospheric
parameters show that, at nighttime, an atmospheric temperature inversion occurs close to the surface
yielding a thin boundary layer which acts like a lid, keeping normal convective overturning of the
atmosphere from penetrating through the inversion. This mechanism traps water vapor close to the land
and drives the direct adsorption of water vapor at the surface during the night. The diurnal variation in
emissivity at 8.7 μm has been found to be as large as 0.03 with high values at night and low values
during the day. At 10.8 μm and 12 μm, the variation has the same sign as that at 8.7 μm, but with a smaller
amplitude, 0.019 and 0.014, respectively. The impact of these diurnal variations on the retrieval of surface
temperature and atmospheric parameters has been analyzed.
1. Introduction
Diurnal variations in desert sand emissivity during the dry season have been brought to the public’s attention recently by Li et al. [2012] who performed an analysis with SEVIRI (Spinning Enhanced Visible and
Infrared Imager) observations from the European geostationary platform Meteosat (Meteorological Satellite). A recent analysis [Masiello et al., 2013b] performed by some of the authors of this paper with SEVIRI
data confirms this effect and also shows that the time variation of emissivity closely follows the daily cycle
for temperature.
Diurnal variation in surface emissivity is very likely to occur in the natural environment because emissivity
(𝜀) is influenced by soil water content (𝜃 ), which even in nonrainfall seasons, can change due to dew condensation at night, for example. However, the emissivity variation we are investigating occurs in desert areas,
during non-raining days when the surface temperature does not necessarily drop below the dew point
temperature at night.
There are a series of basic studies, which can help to explain the phenomenon of diurnal emissivity variations during non-raining days in the dry season and in environmental conditions that do not favor the
occurrence of dew formation on a bare sand dune. Agam and Berliner [2004, 2006] brought evidence of a
new mechanism of direct water vapor adsorption on land surface. They also showed that the resulting soil
moisture variations (with the soil moisture expressed by weight as the ratio of the mass of water present to
the dry weight of the soil sample, units of kg kg−1 ) follow the daily cycle [Agam and Berliner, 2004] and the
uppermost soil layer (0–1 cm) can change its water content from below ≈ 0.009 kg kg−1 (around midday)
to above ≈ 0.02 kg kg−1 before sunrise. Mira et al. [2007a, 2007b] showed that the thermal infrared emissivity of rich-quartz sand strongly depends on soil moisture content. For low values of soil water content
(around 0.02 kg kg−1 ), the incremental ratio, Δ𝜀∕Δ𝜃 , at wave numbers of reststrahlen absorption, is very
large: Δ𝜀 ≈ 0.05 per Δ𝜃 = 0.01 kg kg−1 [Mira et al., 2007a, 2007b].
MASIELLO ET AL.
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Journal of Geophysical Research: Atmospheres
10.1002/jgrd.50863
The present study is devoted to complement the analysis by Li et al. [2012] and to provide further evidence
that the mechanisms outlined in Agam and Berliner [2004, 2006] and Mira et al. [2007a, 2007b] are those
responsible for the satellite-based emissivity diurnal variations observed in arid regions. Toward this objective we have analyzed IASI (Infrared Atmospheric Sounding Interferometer) spectra recorded over a Sahara
desert area during the dry season (July 2010). Both descending (daytime) and ascending (nighttime) orbits
have been considered, and the surface emissivity spectrum has been retrieved simultaneously with skin
temperature and atmospheric parameters, namely temperature, water vapor, and ozone profiles, from IASI
radiances. Space colocated day and night IASI observations have been used to assess diurnal emissivity variations. A quantitative analysis of the impact of these variations on the retrieval of surface temperature and
atmospheric parameters is also carried out.
We stress that the effect of soil moisture over thermal infrared emissivity has been assessed both with laboratory measurements and satellite observations [e.g., Ogawa et al., 2006; Mira et al., 2010; Hulley et al., 2010].
However, the present study focuses on the phenomenon of diurnal variation in emissivity of desert sand in
situations in which soil moisture does not change because of rain or dew condensation.
Diurnal variation in emissivity has also been evidenced with IASI observations by Zhou et al. [2011], who
performed a retrieval of emissivity on a global scale. However, no attempt was made to assess the driving
mechanism of these variations for desert regions.
IASI was developed in France by the Centre National d’Etudes Spatiales (CNES) and is flying on board the
Metop-A (Meteorological Operational Satellite) platform, the first of three satellites of the European Organization for the Exploitation of Meteorological Satellite (EUMETSAT) European Polar System (EPS). IASI has
been primarily put in orbit for a meteorological mission; hence, its main objective is to provide suitable information on temperature and water vapor profiles. The instrument has a spectral coverage extending from
645 to 2760 cm−1 , which with a sampling interval Δ𝜎 = 0.25 cm−1 gives 8461 data points or channels for
each single spectrum. Data samples are taken at intervals of 25 km along and across track, each sample having a minimum diameter of about 12 km. With a swath width on Earth’s surface of about 2000 km, global
coverage is achieved in 12 h, during which the instrument records about 650,000 spectra. Further details on
IASI and its mission objectives can be found in Hilton et al. [2012].
Regarding the assessment of the origin and mechanism of diurnal variation in emissivity, the complementary role of IASI stands out in its atmospheric sounding capability, which provides information about the
thermodynamical state of the atmosphere together with surface parameters.
The retrieval of surface and atmospheric parameters from IASI spectral radiances is carried out with a
package that we call 𝜑-IASI, the methodological aspects and validation of which have been described in
many papers [Amato et al., 1995, 1999; Lubrano et al., 2000; Masiello et al., 2003; Masiello and Serio, 2004;
Grieco et al., 2007; Amato et al., 2009; Masiello et al., 2009; Grieco et al., 2010; Masiello et al., 2011, 2012a,
2012b]. The reader is referred to these papers for further details.
For our analysis, it was important to identify a target area with surface features characterized as much as
possible by sand rich in quartz and without vegetation. In addition, it was important to identify a series
of IASI observations over a relatively long sequence of clear sky days in order to perform the analysis in
non-raining meteorological conditions. To this end, together with IASI (level 1C) observations, we have also
used high rate level 1.5 image data from SEVIRI on board Meteosat-9. SEVIRI channel emissivity maps over
the North-West Sahara [Masiello et al., 2013b] helped to identify a target area characterized by sand dunes,
whereas time sequences of SEVIRI radiances were used to identify long periods of clear skies.
The present study is organized as follows. Section 2 shows the data used in the analysis, whereas section 3
describes the methodology. Section 4 is devoted to the results. Conclusions are summarized in section 5.
2. Data
The selected study area is located over the Sahara Desert, Ouargla Province, Algeria (see Figure 1). The area
extends from 4◦ to 8◦ of East longitude and 29◦ to 33◦ North latitude, at an average altitude of about 200
m. Sand dunes prevail with a low vegetative cover. IASI data have been acquired for the month of July 2010,
that is, in the middle of the dry season. Observed spectra correspond to the descending (day) orbits and
ascending (night) orbits. The IASI scan pattern of footprints for a typical day (ascending and descending
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
a)
10.1002/jgrd.50863
b)
Figure 1. (a) Sahara target area (Ouargla Province, Algeria) showing the IASI footprints for a select few daytime (white
circles) and nighttime orbits (black circles); (b) Example of day-night IASI footprints which overlap by more than 40%.
orbits) is shown in Figure 1. Normally, we have approximately 400 spectra per day, of which half are in the
morning (around 9 to 10 UTC) and the remaining half in the evening (around 20 to 21 UTC). The spectra were
qualified as clear sky using the stand alone IASI cloud detection scheme developed by Serio et al. [2000];
Masiello et al. [2002, 2003, 2004] and Grieco et al. [2007].
A select few day-night spectra are shown in Figure 2 for the IASI band 1. The daytime IASI spectrum shows
the characteristic peak at 8.7 μm (1149.40 cm−1 ), which corresponds to the center of the reststrahlen doublet of quartz. The nighttime spectrum clearly shows H2 O lines in emission in the atmospheric window
around 1100 cm−1 (9 μm), which signals a temperature inversion at the surface.
It has to be stressed here that ascending/descending IASI footprints (see also Figure 1a) are not perfectly
overlapped. Therefore, the surface seen in the morning orbit could be different from that seen during
the night. To limit as much as possible any bias due to spatial collocation, we have considered day-night
footprints with an overlap greater than 40% (see, e.g., Figure 1b).
IASI band 1 spectral radiance (r.u.)
Another source of possible emissivity variation is the zenith angle. According to García-Santos et
al. [2012], desert sand emissivity has an angular behavior, which becomes important for zenith
angles > 40◦ . For this reason we have considered IASI pairs with a field of view angle lower
than 40◦ . Based on the two selection
rules above, the number of day-night
0.2
Day Spectrum; (32.89° N, 5.94° E); FOV angle: 13.88°
couples available for each day of the
0.18
Night Spectrum; (31.86° N, 6.48° E); FOV angle: 20.12°
month are shown in Figure 3.
0.16
Reststrahlen peak at 8.7 μ m
0.14
0.12
0.1
0.08
0.06
0.04
0.02
H O lines in emission
2
0
700
800
900
1000
1100
1200
wave number (cm−1)
Figure 2. Example of select few day-night IASI spectra over the target
area (Ouargla Province, Algeria). Note 1 r.u. (radiance units) corresponds
to 1 W m−2 sr−1 (cm−1 )−1 .
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
Meteosat 9 high rate SEVIRI level 1.5
image data in the infrared for the same
target area and time period were also
extensively used to characterize the surface emissivity [Masiello et al., 2013b].
The SEVIRI instrument has eight infrared
channels with peaks at 13.4 (746.30), 12.0
(833.33), 10.8 (925.90), 9.7 (1030.9), 8.7
(1149.40), 7.3 (1369.9), 6.2 (1612.9), and
3.9 (2564.10) μm (cm−1 ), respectively.
The time resolution of the data is 15 min,
whereas each pixel has a size of 3×3 km2 .
Particularly interesting for the present
analysis is the SEVIRI channel at 8.7 μm
(1149.40 cm−1 ). In fact, this channel is
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Journal of Geophysical Research: Atmospheres
located just in the quartz doublet peak of
the reststrahlen band, which has a very
high contrast within the atmospheric
window. Using SEVIRI observations for
July 2010 [Masiello et al., 2013b], it was
possible to check that the selected study
area shows emissivity features common
to desert sand rich of quartz.
30
25
Number of IASI pairs
10.1002/jgrd.50863
20
15
In addition, SEVIRI data (colocated with
IASI footprints) have been also used
to identify clear sky through a direct
inspection of the time sequence at SEVIRI
5
window channels, such as that at 12 μm
(833.33 cm−1 ). An example is shown in
0
0
5
10
15
20
25
30
Figure 4, where the temporal evolution
Day of the month (July 2010)
of SEVIRI radiances at 12 μm is plotted
Figure 3. Number of pairs of day-night IASI spectra considered to
for the month of July. The figure correassess the diurnal emissivity variation.
sponds to SEVIRI pixels at 30.66◦ North
latitude and 5.56◦ East longitude. The
radiance sequence follows exactly the daily cycle. Deviations (even small) from the smooth signal expected
for clear sky allow us to detect the presence of clouds. From this plot we see that the first 10 days of the
month were characterized as clear sky, whereas clouds were present at the end of the month (around 25
July).
10
3. Methods
IASI spectra have been inverted for emissivity, skin temperature, and atmospheric parameters using the
𝜑-IASI package. As mentioned in section 1, the details of the package have been described at length in
a series of papers. Here we limit ourselves to describe the methodology we use for emissivity, which is a
relatively new aspect of the scheme.
We use the optimal estimation [Rodgers, 2000] with the emissivity spectrum represented with a truncated
Fourier transform series [Masiello and Serio, 2013a]. Emissivity Fourier coefficients, skin temperature (Ts ), and
atmospheric parameters, temperature (T ), water vapor mixing ratio (w), and ozone mixing ratio (o) profiles,
are simultaneously retrieved.
Month of July 2010, SEVIRI pixel at 30.66° N., 5.56° E
SEVIRI radiance at 12 µm (r.u. units)
0.18
0.16
0.14
0.12
0.1
0.08
0.06
Heavy Cloudiness
Light Cloudiness
0.04
0
5
10
15
20
25
30
Time (day of the month)
Figure 4. Sequence of SEVIRI radiance at 12μm for the month of July
2010. The continuity expected for clear sky helps to identify the presence of cloudiness. Note, 1 r.u. (radiance units) corresponds to 1 W m−2
sr−1 (cm−1 )−1 .
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
For the forward model, we use 𝜎 -IASI
[Amato et al., 2002], which can handle
the radiance term reflected from the surface either with a specular or Lambertian
model. For the analysis shown in this
paper, we use the Lambertian model.
Within the inverse scheme, an important issue is the background vector and
covariance matrix for emissivity. For the
purpose of developing a suitable background for emissivity, we have used the
University of Wisconsin (UW) Baseline
Fit (BF) Emissivity database (UW/BFEMIS
database, e.g., http://cimss.ssec.wisc.
edu/iremis/ [Seemann et al., 2008]). The
UW/BFEMIS database is derived from the
monthly mean operational Aqua/MODIS
(Moderate Resolution Imaging Spectroradiometer) products (called MYD11C3)
using a conceptual model called the
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Journal of Geophysical Research: Atmospheres
July 2010, location 32.85 N Lat, 7.61 E Lon.
1
0.95
Emissivity
0.9
0.85
0.8
0.75
0.7
IASI emissivity spectrum
Hinge points from UW/BFEMIS
800
1000 1200 1400 1600 1800 2000 2200 2400 2600
wave number (cm−1)
10.1002/jgrd.50863
Baseline Fit method developed from
laboratory measurements of surface
emissivity. This method is applied to
fill in the spectral gaps between the
six emissivity wavelengths available in
MYD11C3 products. The UW/BFEMIS
database is available from the year 2003
to 2012 globally with 0.05◦ spatial resolution at 10 wavelengths (3.6, 4.3, 5.0,
5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3)
μm. Those wavelengths were chosen as
hinge points to capture as much of the
shape of the higher resolution emissivity spectra as possible between 3.6 and
14.3 μm.
The available band emissivities cannot be straightly interpolated to the
IASI spectral bands, because this would
be too crude an approximation. This
problem has been addressed in Borbas
and Ruston [2010] where an Empirical
Orthogonal Function (EOF) regression
was applied between the UW/BFEMIS database and the first five eigenvectors at high spectral resolution of
a representative set of laboratory measurements of surface emissivity. In this study a similar algorithm has
been developed to interpolate a low spectral resolution emissivity spectrum to the IASI wave number range.
As in Borbas and Ruston [2010], the interpolation is based on the EOF decomposition of a suitable training data set of high spectral resolution emissivity spectra from laboratory measurements. For the present
study, we used a total of 134 emissivity spectra from the ASTER (Advanced Spaceborne Thermal Emission
Reflection Radiometer) Spectral Library version 2.0 [Baldridge et al., 2009] and the MODIS UCSB (Moderate
Resolution Imaging Spectrometer, University of California, Santa Barbara) Emissivity Library (http://www.
icess.ucsb.edu/modis/EMIS/html/em.html). An example of the interpolation from UW/BFEMIS to IASI is
presented in Figure 5.
Figure 5. IASI emissivity spectra derived from the UW/BFEMIS database
for a case corresponding to a site located within the Ouargla district.
The interpolation from the low spectral resolution emissivities to the
IASI emissivity spectrum is based on the EOF decomposition of a suitable training data set of high spectral resolution emissivity spectra from
laboratory measurements (see text for more details).
Derived from the UW/BFEMIS database, these IASI spectral resolution emissivity spectra have been used to
build up an appropriate a priori or background (mean and covariance) for the optimal estimation retrieval.
For the emissivity-covariance matrix, we only considered the diagonal elements (variances).
As noted, in our retrieval procedure, the emissivity spectrum is represented through decomposition in
a truncated Fourier cosine series. The truncation point can depend on the surface type. For desert sand,
we need to resolve the reststrahlen absorption band at 8.7 μm. For this reason, we have used 400 Fourier
coefficients, which correspond to render the emissivity spectrum with a spectral resolution of ≈ 5 cm−1 .
Comparison with laboratory measurements shows that this spectral resolution is enough to resolve the
spectral structures present within desert sand emissivity [Masiello and Serio, 2013a].
Further details on how we handle the retrieval of surface emissivity can be found in Masiello and Serio
[2013a]. Here we limit to show a retrieval example from one IASI spectrum recorded over desert sand. The
example (see Figure 6) is shown here also to exemplify the precision of the retrieval. The precision is computed as the square root of the diagonal of the a posteriori covariance matrix. An additional important
aspect of the methodology is that we can retrieve the emissivity spectrum over the entire IASI spectral coverage, even though we only used a limited number of IASI channels (see Figure 6). At wave numbers not
used for the mathematical inversion of IASI radiances, the retrieved emissivity spectrum is just an interpolation of the Fourier cosine series. It is also important to stress that as the IASI spectrum is mostly sensitive to
surface emission in atmospheric window spectral regions, the emissivity retrieval is most significant in those
regions.
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
a)
Emissivity
1
Emissivity retrieval
± 1 σ error bar
0.8
0.6
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
IASI spectrum (r.u.)
wave number (cm−1)
0.15
b)
IASI spectrum
Channels used for retrieval
0.1
0.05
0
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
wave number (cm−1)
Figure 6. (a) Example of one IASI surface emissivity spectrum retrieval
over desert sand. The emissivity was simultaneously retrieved with surface temperature, temperature, water vapor, and ozone atmospheric
profiles, using (b) the channels shown. Note 1 r.u. (radiance units)
corresponds to 1 W m−2 sr−1 (cm−1 )−1 .
10.1002/jgrd.50863
As described earlier, we use the optimal
estimation [Rodgers, 2000] to retrieve
the state vector. Within the context of
optimal estimation theory we can also
address the issue of the sensitivity of
the retrieval to the variation by a model
parameter, which, e.g., is not included in
the state vector.
The general problem of sensitivity to
a given interfering parameter can be
handled by considering the derivative
of the retrieved vector, 𝐯̂ with respect
to the perturbation introduced by the
interfering parameter, e.g., spectral
emissivity.
According to Carissimo et al. [2005], this
derivative can be written as
(
)−1 T −1
𝜕 𝐯̂
= 𝐒−1
+ 𝐊T 𝐒−1
𝐊
𝐊 𝐒e 𝐊X (1)
a
e
𝜕𝐗
where
1. 𝐯̂ is the retrieved state vector,
2. 𝐗 denotes the generic interfering parameter-vector,
3. 𝐒a is the state vector background matrix,
4. 𝐒e is the observational covariance matrix, (e.g., IASI radiometric noise),
5. 𝐊 is the Jacobian matrix of the state vector,
6. 𝐊X is the Jacobian matrix of the emissivity vector.
The sensitivity of the state vector, Δ𝐯̂ to a given perturbation, Δ𝐗 is obtained as
Δ𝐯̂ =
𝜕 𝐯̂
Δ𝐗
𝜕𝐗
(2)
As already mentioned, the way we handle the various Jacobian terms and covariance matrix has been
detailed at a length in previous papers. In particular, the reader interested in the details of 𝐒a , which we use
in our retrieval scheme, is referred to Masiello et al. [2012a].
If we identify Δ𝐗 with the diurnal variation in emissivity, the methodology above can be used to assess the
impact over the estimated state vector of this variation in case the emissivity is not retrieved and its value is
taken, e.g., from a suitable atlas, such as the UW/BFEMIS database.
Finally, we recall that the soil moisture (𝜃 ) is defined according to its gravimetric method of measurement,
𝜃=
wtw − wtd
wtd
(3)
where wtw is the mass of wet soil and wtd that of the dry soil.
4. Results
The IASI spectra corresponding to day-night pairs identified in Figure 3 have been inverted for the simultaneous retrieval of 𝜀, Ts , T , w, and o, according to the methodology outlined in section 3. The number of IASI
channels considered for the retrieval are those shown in Figure 6.
To make a proper comparison, e.g., with the results shown in Li et al. [2012] and also to capture salient
characteristics in the diurnal emissivity variation, the IASI emissivity retrieval has been transformed to the
SEVIRI channel emissivity at 12, 10.8, and 8.7 μm, by convolving the IASI spectrum emissivity with the SEVIRI
instrument response at 12, 10.8, and 8.7 μm, respectively. The results for the days for which we had available
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
pairs to allow computation are shown
in Figure 7. Averaging the results over
the whole month, we found an average
diurnal emissivity variation of 0.029 at 8.7
μm, 0.019 at 10.8 μm, and 0.014 at 12 μm.
0.08
12 μm
10.8 μm
8.7 μm
Diurnal Emissivity Variation
0.07
10.1002/jgrd.50863
0.06
0.05
0.04
0.03
0.02
0.01
0
−0.01
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
Day of the Month (July 2010)
Figure 7. Diurnal variation in emissivity at 12, 10.8, and 8.7 μm for the
month July 2010. For each day, the diurnal variation has been averaged
over the number of pairs shown in Figure 3).
From Figure 7, we see that the largest
diurnal variations are seen around periods with heavy cloudiness (compare with
Figure 4). If we focus on the most stable,
non-raining period, that is, the first 10
days of the month, we see that the diurnal variation is confined below 0.04 at 8.7
μm and, normally, well below 0.02 at 10.8
and 12.8 μm. According to Li et al. [2012],
we also see that the diurnal variation is
larger at 8.7 μm than at 10.8 μm, which,
in turn, is larger than that at 12 μm.
Details of the diurnal variation in spectral
emissivity for 6 and 20 July, which correspond to two clear sky days with the
larger number of couples of IASI day-night spectra and in the middle of two long periods of clear skies are
shown in Figures 8 (left) and 8 (middle), respectively.
A direct computation of the dew point temperature, derived from the IASI retrieval, shows that the diurnal
variation we see in Figure 8 cannot be due to the formation of dew, because the dew point temperature
is found well below the surface temperature. An example is shown in Figure 9 for 20 July 2010. A similar
behavior was also found by Li et al. [2012].
As a consequence, on the assumption that the observed diurnal emissivity variation seen in Figures 8 (left)
and 8 (middle) is due to soil moisture, according to Agam and Berliner [2004, 2006], the only mechanism
responsible for the change is that of direct water vapor adsorption at the surface.
To support this conclusion, we have analyzed the thermodynamical state of the atmosphere close to the
surface. For 6 and 20 July 2010, this analysis is shown again in Figures 8 (left) and 8 (middle). The daytime
retrieval does not show any inversion close to the surface and has a normal shape that favors evaporation.
Conversely, the nighttime temperature profiles show an evident inversion, which traps water vapor close to
the surface. This temperature inversion is consistent with the mechanism for H2 O adsorption and drives the
day-night emissivity variation. In the same figures, the water vapor profile is also shown. It is possible to see
that the day-night variation of H2 O is very low. For 6 and 20 July, the diurnal variation in water vapor column
amount (day-night) is of 0.23 cm and 0.16 cm, respectively. This very small variation leads us to conclude
that the atmosphere was very stable, and the presence of moisture by large scale atmospheric circulation
was likely absent.
To further support the above conclusion, Figure 8 (right) also shows the results for 26 July 2010. On this day,
the diurnal variation was very low (below 0.005 at 8.7 μm) and the emissivity retrieval at ≈9 UTC was almost
identical to that at ≈20 UTC. However, for this day, the nighttime temperature profiles do not show any evident inversion in the boundary layer (see again Figure 9, right). This result confirms that the mechanism
which causes the diurnal emissivity variation is governed by a boundary layer temperature inversion. Also,
a strong diurnal temperature difference seems to be important for the emissivity variation. This difference
was ≈12 K for 26 July, whereas for 6 and 20 July, this was 19 K and 20 K, respectively.
The temperature profiles shown in Figure 8 have been averaged over the number of soundings available
for the given days. This averaging operation tends to smooth the inversion in the lower troposphere. However, the inversion is very well seen in each single retrieval and takes place very close to the surface. This is
exemplified for one single temperature retrieval in Figure 10a, which also compares the IASI retrieved temperature with the time-space colocated ECMWF analysis. The ECMWF analysis at the canonical hours of 00,
06, 12, 18, 24 UTC were used for the time interpolation. It is seen that the ECMWF model greatly
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/jgrd.50863
Day: 06 July 2010
Day
Night
0.9
0.8
a)
0.7
700
800
Pressure (hPa)
0
900
1000
wave number (cm−1)
0
Day
Night
200
400
600
1200
Day
Night
400
600
800
800
c)
b)
1000
0
1100
200
Pressure (hPa)
Emissivity
1
5
10
15
H2O mixing ratio (g/kg)
1000
200
250
300
Temperature (K)
Emissivity
Day: 20 July 2010
Day
Night
0.9
0.8
0.7
a)
700
800
900
1000
wave number (cm−1)
200
400
600
Day
Night
200
800
400
600
800
b)
1000
0
1200
0
Day
Night
Pressure (hPa)
Pressure (hPa)
0
1100
c)
5
10
15
H O mixing ratio (g/kg)
1000
200
250
300
Temperature (K)
2
Emissivity
Day: 26 July 2010
Day
Night
0.9
0.8
0.7
a)
700
800
900
1000
wave number (cm−1)
200
400
600
800
Day
Night
200
400
600
800
c)
b)
1000
0
1200
0
Day
Night
Pressure (hPa)
Pressure (hPa)
0
1100
5
10
H O mixing ratio (g/kg)
1000
15
200
2
250
300
Temperature (K)
Figure 8. (a) Night and day time IASI emissivity spectra, (b) IASI H2 O retrieval, and (c) temperature retrieval for 3 days of
July 2010: (top) 6 July, (middle) 20 July, and (bottom) 26 July. The retrievals have been averaged over the number of IASI
pairs shown in Figure 3.
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
1633
Journal of Geophysical Research: Atmospheres
smoothes the temperature day-night
dynamic in the lower troposphere,
which, conversely, appears much more
pronounced in the IASI retrieval.
20 July 2010: Daytime
Temperature (K)
60
a)
40
10.1002/jgrd.50863
Dew Point Temperature
Surface Temperature
From Figure 10, we see that an inversion occurs in between 975 and 925 hPa,
0
which corresponds to a boundary layer
0
5
10
15
20
25
30
altitude of ≈ 500 m. It could be questionIASI sounding number
20 July 2010: Nighttime
able whether or not IASI has this high
60
vertical spatial resolution close to the
b)
Dew Point Temperature
Surface Temperature
surface. To demonstrate that in fact IASI
40
does have this high spatial vertical resolution, Figures 10b and 10c show the
20
averaging kernels for the two IASI temperature retrievals shown in Figure 10a.
0
0
5
10
15
20
25
30
The very highly resolved averaging kerIASI sounding number
nels close to the surface, as already
Figure 9. (a) Daytime and (b) Nighttime IASI dew point and surface
mentioned, are a result of the desert
temperature for 20 July 2010. The retrieval was obtained at each single
sand emissivity, which yields a strong
IASI footprint available on that day.
contrast throughout most of the IASI
bands 1 and 3. What is important to capture in Figures 10b and 10c is the fact that the averaging kernels
peak almost exactly at the corresponding layer pressure. This behavior shows that the retrieval is spatially
resolved in the vertical at those layers in the lower troposphere.
Temperature (K)
20
The results we have shown have been obtained over a target area, whose surface consists of bare sand
dunes. In these conditions, the emissivity is largely determined by a mixture of two components: soil water
content and sand. According to Hillel [1998], the process responsible for water vapor adsorption by soils is a
reversible physical adsorption, which allows us to deal with moist soil as if it were mainly a mixture of water
and sand. Chemical processes do not play an important role in this adsorption mechanism [Hillel, 1998].
In this respect, if these two components are additively combined, according to their abundance in the
mixture, we obtain a composite emissivity, 𝜀(𝜎) given by
𝜀(𝜎) = (1 − 𝜃)𝜀q (𝜎) + 𝜃𝜀w (𝜎)
(4)
where 𝜀q (𝜎) is the emissivity of sand and 𝜀w (𝜎) that of water.
Nighttime
a)
Temperature (K)
Daytime
700
700
740
760
780
800
820
840
860
880
900
920
940
960
980
1000
−0.1
P=979.2 hPa
P=966.2 hPa
P=952.7 hPa
P=939.3 hPa
P=928.9 hPa
P=919 hPa
P=906.5 hPa
720
740
Atmospheric Pressure (hPa)
P=979.2 hPa
P=966.2 hPa
P=952.7 hPa
P=939.3 hPa
P=928.9 hPa
P=919 hPa
P=906.5 hPa
720
Atmospheric Pressure (hPa)
Atmopsheric Pressure (hPa)
06−July−2010, 31.76° N, 7.85° E
500
Nighttime IASI, FOV angle 1.43°, UTC 20:35
525
Nighttime ECMWF
550
Daytime ECMWF
575
Daytime IASI, FOV angle 1.37°, UTC 09:18
600
625
650
Retrieved Surface Temperature
675
Day: 324.5 K
700
Night: 307.4 K
725
750
775
800
825
850
875
900
925
950
975
1000
260 265 270 275 280 285 290 295 300 305 310 315 320 325 330
760
780
800
820
840
860
880
900
920
940
960
b)
980
0
0.1
0.2
0.3
Averaging Kernels for Temp.
1000
−0.1
c)
0
0.1
0.2
0.3
Averaging Kernels for Temp.
Figure 10. a) Example of day-night IASI retrieval for temperature in the lower troposphere for a single IASI footprint. The figure also shows a comparison with
the colocated ECMWF analysis. (b) Nighttime and (c) daytime show the temperature averaging kernels, corresponding to the retrieval shown in Figure 10a. The
averaging kernels are shown for the lowermost seven tropospheric layers.
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
1634
Journal of Geophysical Research: Atmospheres
Under a variation 𝛿𝜃 of soil moisture, a
variation 𝛿𝜀(𝜎) is given by
06 July 2010
30
Day
Night
(
)
𝛿𝜀(𝜎) = 𝛿𝜃 𝜀w (𝜎) − 𝜀q (𝜎)
25
Field of View angle (degrees)
10.1002/jgrd.50863
(5)
Since 𝜀w ≈ 1 in the whole infrared range,
the difference 𝜀w (𝜎) − 𝜀q (𝜎) has a very
large amplitude (contrast) at the rest15
strahlen doublet of quartz, where 𝜀q (𝜎)
can be as low as 0.6 [Baldridge et al., 2009].
10
This is why we see that the effect of diurnal variation is larger at 8.7 μm than at
5
10.8 and 12.0 μm. In addition, the above
model states that a reverse sign in the
0
diurnal variation is possible in spectral
0
5
10
15
20
25
30
regions where the difference 𝜀w (𝜎) − 𝜀q (𝜎)
IASI sounding number
changes its signs. For the type of surface
Figure 11. Field of view angle of the 28 day-night IASI pairs for 6 July
we have analyzed, we expect that, within
2010.
the atmospheric window, water has the
larger emissivity. Therefore, the sign of the diurnal variation (nighttime-daytime) should normally be positive at window channels such as 8.7, 10.8, and 12 μm. However, especially at 12 μm, we may have land
features with emissivities larger than that of water and a reverse sign could be possible. This reverse sign has
been observed by Li et al. [2012], who examined a much larger area than that analyzed in this paper.
20
According to Agam and Berliner [2004, 2006], diurnal variations in the uppermost 1 cm soil layer moisture
due to the direct H2 O adsorption have amplitudes of approximately 2%. This variation might be not enough
to explain the diurnal variation we have observed in the atmospheric window. However, thermal infrared
measurements from satellite are sensitive to the top few micrometers. Thus, a 2% amplitude below the surface could not be representative of what is occurring in the topmost layer, where the effect could be larger,
and therefore explains the magnitude of diurnal variation we see at 8.7 μm.
It is also fair to say that a non-Lambertian behavior has been evidenced in quartz powder [Wald and
Salisbury, 1992], which at large angles (≥ 70◦ ) has the effect of decreasing the emissivity. A similar
behavior has been evidenced in a recent paper by García-Santos et al. [2012], who also claimed that
a non-Lambertian behavior begins to be
evident at zenith angle larger than 40◦ .
1
Emissivity
a)
0.9
0.8
0.7
IASI−day
UW/BFEMIS
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
wave number (cm−1)
1
Emissivity
b)
0.9
IASI−night
UW/BFEMIS
0.8
0.7
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
wave number (cm−1)
Figure 12. Day and night average emissivity computed on the basis
of the 28 pairs of IASI soundings for 6 July 2010. (a) Daytime average
emissivity; (b) nighttime average emissivity. The figure also shows the
average emissivity obtained by the UW/BFEMIS database.
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
However, as already discussed, to limit
possible angular dependence of the
sand emissivity, the IASI day-night couples we have analyzed in this paper refer
to zenith angles below 40◦ . In addition, the relative zenith angle, that is,
the day-night difference of the field of
view angle does not exceed 10◦ for any
single day-night pair analyzed in this
paper. In other words, the two IASI spectra of any single pair are observed with
almost the same field of view angle. This
is exemplified for 6 July 2010 in Figure 11.
4.1. Sensitivity of Skin Temperature
and Atmospheric Parameters Retrieval
to Diurnal Emissivity Variation
How significant are these diurnal variations and what impact do they have on
the retrieval of skin temperature, Ts and
1635
Journal of Geophysical Research: Atmospheres
atmospheric parameters, temperature,
water vapor, and ozone, (T , w, and o)?
0
a)
Pressure (hPa)
Number of Events
10
5
0
−3 −2.5 −2 −1.5 −1 −0.5
b)
500
1000
−4 −2
0
2
ΔTs
4
6
8
10
ΔT (K)
Pressure (hPa)
Pressure (hPa)
0
c)
500
1000
−2
−1
0
Δw (g/kg)
1
2
d)
10
10
0
2
−1
0
1
10.1002/jgrd.50863
These issues have been addressed
by applying the sensitivity analysis
illustrated in section 3 to the retrieval
obtained for 6 July 2010. This day is in
the middle of a non-raining period with
an average diurnal variation in emissivity
at 8.7 μm of 0.04. We have 28 day-night
IASI soundings for 6 July 2010, the day
and night average emissivity for which is
shown in Figure 12. For each single daytime sounding, we have computed Δ𝐯̂
corresponding to the perturbation,
2
Δo (ppmv)
Figure 13. IASI Retrieval sensitivity to the diurnal emissivity variation. The figure shows the impact on the retrieval by imposing for the
daytime retrievals the nighttime emissivity instead of that correct for
daytime; (a) histogram of ΔTs ; (b) average difference, ΔT ; (c) average
difference, Δw; and (d) average difference, Δo.
Δ𝐗 ≡ Δ𝜺 = 𝜺day − 𝜺night
(6)
In this way, we mimic the effect of
using the nighttime emissivity to invert
daytime IASI soundings.
The results have been averaged over
the 28 daytime IASI soundings and are shown in Figure 13. For the case of Ts , we find that this parameter
would be negatively biased, because we use an emissivity, which is larger than the (supposedly) correct one,
obtained within the simultaneous retrieval (section 4). The average negative bias for the surface temperature is equal to −1.5 K. For the atmospheric parameters we also see a consistent bias, which means that the
diurnal variation has a large impact over the retrieval of these parameters. The bias for temperature has a
peak value of 8 K in the lower troposphere, whereas for water vapor we observe a difference of ±1 g/kg. For
ozone we also observe a consistent bias of 1 ppmv.
A second example is obtained by considering a variation with respect to the emissivity obtained by the
UW/BFEMIS database. Derived from MODIS day-night products, the UW/BFEMIS database emissivity
assumes a constant emissivity between day and night in the retrieval. Thus, it does not represent an average
of diurnal variations, but it is simply one emissivity per day and, therefore, the present sensitivity exercise
is also important to check the quality of satellite products, where emissivity is assumed to be diurnally
invariant.
In performing the sensitivity exercise, the UW/BFEMIS emissivity is imposed on the retrieval and the difference is considered for the case in which emissivity is itself retrieved along with Ts , T , w, and o. This latter
retrieval exercise is that we have just shown in section 4.
For the 28 day-night IASI soundings, the average UW/BFEMIS emissivity is compared to the retrieved emissivity in Figure 12. In the case of the daytime IASI soundings, the emissivity perturbation is computed
according to
Δ𝜺 = 𝜺day − 𝜺UW/BFEMIS
(7)
In the same way for the nighttime soundings, we have
Δ𝜺 = 𝜺night − 𝜺UW/BFEMIS
(8)
The results of the sensitivity analysis are summarized in Figure 14. For the case of skin temperature, we
have that the assumption of imposing on the retrieval the UW/BFEMIS emissivity instead of simultaneously retrieving it with the other parameters, would result in a downward bias in Ts during the day and, the
reverse, an upward bias in Ts would result at night. However, on average, the degree of this bias is confined
to within ±1 K (−0.40 K in the daytime and +0.68 K at nighttime).
The impact on atmospheric parameters appears to be much larger. The temperature can be affected by
±5 K in the lower troposphere, the water vapor mixing ratio by ±2 g/kg and ozone by up to 2 ppmv in the
stratosphere.
MASIELLO ET AL.
©2013. American Geophysical Union. All Rights Reserved.
1636
Journal of Geophysical Research: Atmospheres
Night
Day
10
5
−2
−1
0
1
Pressure (hPa)
Number of Events
a)
0
5. Conclusions
0
15
b)
Night
Day
500
1000
−6 −4 −2
2
ΔTs
0
2
4
6
8
ΔT (K)
Night
Day
500
1000
−6
Pressure (hPa)
Pressure (hPa)
0
c)
10.1002/jgrd.50863
d)
Night
Day
0
10
2
10
We have addressed the issue of diurnal
variation in the emissivity spectrum of
desert sand with a series of day-night
IASI soundings recorded over the
Sahara Desert during July 2010. In our
analysis, great care has been taken to
ensure that (a) the target area included
only bare sand dunes, (b) there was
a long sequence of clear sky with no
rain, and (c) the IASI angle of views
were below 40◦ to minimize other
possible sources of emissivity variation.
In agreement with Li et al. [2012],
our analysis does find evidence of
diurnal variations in the emissivity specFigure 14. IASI Retrieval sensitivity to the diurnal emissivity variatrum. Nighttime emissivity is found to
tion. The figure shows the impact on the retrieval by imposing the
UW/BFEMIS emissivity instead of that correct for daytime/nightime; (a)
be systematically larger than that
histogram of ΔTs ; (b) average difference, ΔT ; (c) average difference, Δw; retrieved during the daytime, which
and (d) average difference, Δo.
leads us to conclude that the soil
moisture undergoes a daily cycle with a dip around midday and a peak at night.
−4
−2
0
2
Δw (g/kg)
4
6
−2
−1
0
1
2
Δo (ppmv)
This conclusion is consistent with previous results by Agam and Berliner [2004, 2006], who showed that H2 O
can be directly adsorbed at the surface without the formation of dew. During the dry season, the amplitude
of the soil moisture cycle is normally 1–2%. These values refer to the top few centimeters of soil moisture,
while thermal infrared measurements represent the top few micrometers. Accounting also for nonlinear
volume scattering effects and further experimental evidence [Mira et al., 2007a, 2007b] about the effect of
soil moisture on rich-quartz sand, we conclude that this amplitude is enough to yield diurnal variations in
emissivity at 8.7 μm as large as 0.04.
The question of how common this phenomenon is can be raised. Direct adsorption of water vapor has been
shown for arid and semiarid regions [Agam and Berliner, 2006]. However, the phenomenon depends on the
thermal and hydraulic properties of the surface and these properties can vary significantly for different soil
types. Normally, the presence of clay improves the adsorption mechanism [Agam and Berliner, 2006]. If we
limit to stable, dry meteorological conditions, the transport of water vapor is driven by moisture and temperature gradients. Thus, a temperature inversion close to the surface is important to transport moisture
toward the surface, as well, it is necessary that relative humidity of the soils pores is lower than the relative
humidity of the air. As far as the transport of atmospheric water vapor toward the surface is concerned, IASI
retrieval of the thermodynamic state of the atmosphere has shown that at nighttime an atmospheric temperature inversion occurs close to surface and creates a thin boundary layer which acts like a lid, trapping
water vapor close to land and supposedly driving the direct adsorption of H2 O at the surface during the
night.
Acknowledgments
IASI has been developed and built
under the responsibility of the
Centre National d’Etudes Spatiales
(CNES, France). It is flown onboard
the Metop satellites as part of the
EUMETSAT Polar System. The IASI
L1 data are received through the
EUMETCast near real time data distribution service. Work partially
supported through EUMETSAT contract EUM/CO/11/4600000996/PDW.
We wish to thank Dr Zhenglong Li of
SSEC, University of Wisconsin, for a
critical reading of the manuscript and
for the valid comments he provided to
us.
MASIELLO ET AL.
The results we have found lead us to conclude that the common belief that the desert sand emissivity is
stable during the year is not correct and that diurnal variations have to be properly taken into account for
a correct retrieval of surface and atmospheric parameters. In this respect, our findings specifically point
out the importance of using physically based algorithms to retrieve surface temperature and emissivity. Split-window type algorithms, which are commonly used to retrieve surface parameters from satellite
imaging radiometer instruments, such as MODIS and SEVIRI do not take these diurnal emissivity variations
into account, and neither does the MODIS/Terra Land Surface Temperature and Emissivity Daily Level 3
Global 5 km Grid (short name MOD11B1) day-night approach, which could result in large surface and lower
troposphere temperature errors.
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