Radiometric measurements of cirrus clouds over sea and land surfaces
O. Lado-Bordowsky
Laboratoire d’Optronique, ENSSAT - Université de Rennes 1, Lannion, France
Lado. Bordowsky@yahoo.com
Y. Hurtaud
Laboratoire d’Optronique, ENSSAT - Université de Rennes 1, Lannion, France
Y.Hurtaud@yahoo.com
ABSTRACT
The DGA (Délégation Générale pour l’Armement) is interested in the determination
both of sky background characteristics and cirrus clouds. We have developed a
methodology for the determination of cirrus clouds using Advanced Very High
Resolution Radiometer (AVHRR3) on the polar_orbiter satellite. This methodology
involves the use of three channels (C1: 0.58-0.68 m; C2: 0.725-1.00 m; C4: 10.311.3 m). A 90 km x 90 km subregion of the radiometer scene was used in this
analysis; the region was centered over Lannion (48°44’N, 03°27’W) with the same
number of marine pixels and land pixels. Fractional cloudiness, radiance and reflectance
(R1, R2) were deduced for each pixel and from the two visible channels C1 and C2.
Radiance and brightness temperatures T4 were deduced from the thermal infrared
channel C4. Two algorithms related to the presence of cirrus clouds are presented. The
calculations were conducted for cirrus above sea background and for cirrus above land
background.
Key Words: DGA, NOAA and LUX2
1- Introduction
The imagery from the AVHRR31 on board NOAA-162 polar orbiting satellites allows
cloud cover, oceanic, and continental surfaces that are used by Météo-France for nowcasting
activities and as input for numerical weather prediction models, to be described. A cloud
classification called LUX2 classification was obtained using threshold tests applied to
different combinations of channels (Derrien et al. 1993). Ten cloud categories are defined in
LUX2: five opaque cloud classes according to their altitude (very low, low, medium, high and
very high), three semi-transparent classes according to their thickness (thick, mean and thin),
one class of semi-transparent clouds above lower clouds and one fractional clouds class.
Using three channels C1 (visible), C2 near-infrared), C4 (thermal infrared) from
NOAA16/AVHRR3 data, we have determined two algorithms related to the presence of cirrus
clouds (three classes according to their thickness: thick, mean and thin) by plotting the
reflectance ratio R1/R2 versus the brightness temperature T4 for different days. Each
algorithm was determined after using one mask to select only land pixels and another mask to
select only marine pixels.
2- Satellite data and reflectance ratio
We first have selected images related to days where cirrus clouds are present over a 90
km x 90 km sub region of the radiometer scene; the region is centered over Lannion with the
same number of marine pixels and land pixels. On 13 August 2003, using the LUX2
1 - Advanced Very High Resolution Radiometer/3
classification, the image (50 x 50 pixels) indicated the presence of clear sky (light grey),
fractional clouds (yellow), thin cirrus (light blue), moderately thick cirrus (mid blue),
cirrostratus (dark blue) and few red pixels for mid-altitude cumulus (Figure 1). Three kinds of
images are also used to determine the two algorithms: reflectance from channel C1 (0.58-0.68
m), also reflectance from C2 (0.725-1.00m) and brightness temperature T4 from C4 (10.311.3 m).
We have calculated a 2-channel ratio namely R1/R2 for each pixel. The ratio technique
using 2-channels was applied to derive the two algorithms: one for cirrus clouds above sea
and one for cirrus clouds above land. The development of the algorithms was based on the
following idea: when a satellite image for a clear day is analyzed, we can observe a sea area
where the brightness temperature T4 is quite constant and low (between 282 and 284 K for
the 24 April 2002) and a temperature T4 higher, above 292 K on the land surface. Figure 2
shows this brightness temperature T4 over the sub region of Brittany (Lannion is the pixel 26
x 26). Coastal pixels (light green and light blue pixels) were eliminated using a mask
determined from this T4 image.
FIG 1. LUX2 cloud classification: Cirrus clouds (blue), fractional clouds (yellow), clear sky (light grey) and cumulus (red).
FIG 2. Brightness temperature T4 (Clear sky).
One problem was the elimination of coastal pixels because of the important tide
amplitude in Brittany; for that we have determined a mask using surface temperature
thresholds (Alloune 2004). We have obtained two separated curves: the upper one related to
cirrus over sea and the lower one related to cirrus over land.
Over sea, the measured reflectance corresponds mainly to solar reflection and to Rayleigh
and aerosol scattering (assuming no sunglint), which is weaker in the near-infrared (0.725-
1.00 m ) than in the visible (0.58-0.68 m ). High reflectances are mainly due to clouds. Sea
surfaces have a low reflectance. In that case R1>R2 and the reflectance ratio R1/R2 >1.
Over land, the reflectance of land surfaces in channel 1 is much less than in channel 2 due
to vegetation spectral radiance behavior at these two wavelengths R1<R2. The reflectance
ratio for clear pixels R1/R2 depends on the atmosphere (scattering and absorption) and on the
land cover but also on the viewing geometry; this ratio is between 0.4 and 0.8 and is always
less than 1. This is due to the natural variability and anisotropy of the land surface.
3-Proposed algorithms
Cirrus analytical expression over sea was deduced from images where cirrus cloud cover
is important and where clear pixels (free-cloud pixels) are present. To take into account the
shape of the reflectance ratio curve deduced from satellite data (always the same shape), we
have defined the following expression y  
i
Ai
x2
.The analytical relation which corresponds to
the experimental values is finally:
y  A
B
C

(300  x) (300  x) 2
(Only by word equation editor)
(1)
where A, B, C, D, E and F are real constants.
The curve was shifted by using a new brightness temperature of 300K (a temperature
never obtained for cirrus clouds). The analytical expression is in red on figure 4 where we
give the results obtained for two different days namely 2002/10/26 and 2003/09/09; errors
bars are also indicated in red on each curve. The real constants A, B, C, D, E and F are in
Table 1.
Table 1. Constants of the analytical expression for cirrus clouds over sea.
2002/10/26
2003/09/09
A
1.1
1.1
B
- 15
- 19
C
1218
1417
D
- 32462
- 34781
E
412498
413751
F
- 1537371
- 1745293
Concerning the analytical expression for cirrus clouds above land surfaces, the following
algorithm is obtained: Y  A  B  exp( X ) . This initial algorithm was modified because we
observed that for all the data we have analyzed, the reflectance ratio is between a minimum
value of 0.4 and a maximum value just lower than 1. After a variable change: X  x  max( x)
where x are brightness temperature values over land surface, the final expression is:
 x  max( x) 
(2)
Y  A  B  exp 




 is a coefficient determined by the mean squares method so that the obtained analytical
curve is as correct as possible. Two examples are given in figure 3; they correspond to
2003/08/26 and 2003/02/22.
We have observed that fractional clouds (yellow crosses) are always between clear sky
(grey crosses) and thin cirrus. For fractional clouds on sea surface the error bars are
important; they are also important when the numbers of clear pixels are too small.
Day 26 October 2002
Day 9 September 2003
FIG 3. Reflectance ratio R1/R2 versus brightness temperature T4 for cirrus clouds over sea surface.
4- Conclusion (Conclusion is necessary)
In this paper, we have presented two algorithms to retrieve cirrus behavior above sea
surface and above land surface. These algorithms were obtained using NOAA16/AVHRR3
data over a 90 km 90 km subregion centered over Lannion where there is a ground-based lidar
station. We have only used a few sets of data for the determination of the two algorithms.
More results will be obtained from the SALIC database (Satellite-Lidar-Clouds) created two
years ago. The difficulty when determining the algorithms is to obtain enough clear pixels
above the sea surface and also above the land surface for each image.
Acknowledgments. This work was financed by the Délégation Générale pour l’Armement
(DGA) and Centre d’ELectronique de l’ARmement (CELAR) under contract PEA MIRA
2000 24 05. Many thanks to the Centre de Météorologie Spatiale (CMS) for satellite data; the
authors are grateful to the students who have participated in the satellite data analysis.
References
1. Alloune, M., 2004: Caractérisation des cirrus par l’analyse de mesures simultanées lidar et
satellite. Diplôme d’Ingénieur ENSSAT – PEA MIRA n°2000 24 05.
2. Derrien, M., B. Farki, L. Harang, H. LeGléau, A. Noyalet, D. Pochic, A. Saorouni, 1993:
Automatic cloud detection applied to NOAA-11/AVHRR imagery. Remote Sens. Environ.,
46, 246-267.
3. Mobley, C., D., 1994: Light and Water – Radiative Transfert in Natural Waters. Academic
Press.
4. Price, J. C., 1984: Land surface temperature measurements from the split-window channels
of the NOAA-7 Advanced Very High Resolution Radiometer. J.Geophys. Res., 36, 62-74.
5. Sobrino, J. A., N. Raissouni and Z. L. Li, 2001: A Comparative Study of Land Surface
Emissivity Retrieved from NOAA Data. Remote Sens. Environ., 75, 256-266.
6. Sobrino, J. A., Z. L. Li, M. P. Stoll and F. Becker, 1994: Improvements in the splitWindow Technique for Land Surface Temperature Determination. IEEE Transactions on
Geoscience and Remote Sensing, 32, 243-253.
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