ANALYSIS OF COMS INFRARED CHANNEL AND VISIBLE CHANNEL
CALIBRATION
Dohyeong Kim, Ho-Seung Lee, Won-seok Lee, Sunmi Na and Tae-Hyeong Oh
National Meteorological Satellite Center,
Korea Meteorological Administration,
Gwanghyewon-myeon, Jincheon-gun, Chungcheongbuk-do,
Republic of Korea
Abstract
COMS (Communication, Ocean, and Meteorological Satellite), the first Korean geostationary
th
meteorological satellite, was launched on 27 June, 2010, and has been fully operating its own
missions since the end of in-orbit test period in April 2011.
The inter-calibration system using COMS for infrared radiation based on the GSICS (Global Spacebased Inter-Calibration System) Coordinate Center (GCC) Algorithm Theoretical Basis Document
(ATBD). Reference instrument uses the hyperspectral Infrared Atmospheric Sounding Interferometer
(IASI) on the low Earth orbit Metop-A satellite. In Infrared channels calibrations, the results from Apr.
2011 to Mar. 2012 showed that the averages of temperature difference between COMS and IASI are
0.37K for IR1(10.8μm), -0.46K for IR2(12.0μm), 0.27K for IR3(6.7μm) and -0.22K for IR4(3.7μm).
KMA had developed vicarious calibration of visible channel using cloud-free ocean, cloud-free desert
(Australian Simpson), water cloud top and Deep convective cloud(DCC) targets whose method was
developed as part of COMS meteorological data processing system(CMDPS). The results from Sep.
2011 to Aug. 2012 showed that the average of reflectance difference between observed and simulated
are -0.8% for ocean target, +0.3% for desert target, -4.4% for water cloud target, -10.7% for deep
convective cloud target and the overall average shows about -4.0% for all targets. The slope trend of
forced intercept to zero shows about 2% of visible channel degradation from Sep. 2011 to Aug. 2012.
As one of visible-calibration methods, NMSC has observed moon twice a month and used this data
for monitoring COMS visible channel degradation trend since Feb 2011. The observed moon data has
been processed by Moon Processing system in NMSC’s IMPS(Image Processing Subsystem). In this
Moon Processing system, the total irradiance of observed moon data is computed and compared with
ROLO model value, which is Moon irradiance output model in USGS. The trend from this compared
data shows nearly 3% of visible channel degradation from Feb 2011 to Aug 2012.
INTRODUCTION
COMS (Communication, Ocean, and Meteorological Satellite), the first Korean geostationary
meteorological satellite, was launched on 27th June, 2010, and has been fully operating its own
missions since the end of in-orbit test period in April 2011. COMS is stationed at an altitude of 36,000
km above the Earth’s equator and at a longitude of 128.2°.
Even though COMS had been designed for having unform accuracy before launch, the performance
of the meteorological imager have been changed by optical alignment and change of sensitivity, etc,
during operation period. Meteorological Imager(MI) which is the meteorological instrument of COMS
has 1 visible and 4 infrared channels. In infrared channel, the blackbody and calibration monitoring
should be continuously conducted. The goal of the inter-calibration of infrared channel as a part of
GSICS (Global Space-based Inter-Calibration System) is to ensure the comparability of satellite
measurements provided at different time, by different instruments, and under the responsibility of
different satellite operators. The basic premise of inter-calibration is that measurements by two
instruments should be the same if they view the same target at the same time, with the same spectral
response and viewing geometry, or any and all such differences have been properly accounted for.
Consequently, inter-calibration requires a series of processes that collocate, transform, select, and
analyze the measurements by two instruments to produce corrections that homogenize all the
observations to a common reference (Wu and Yu 2011). KMA has checked the quality of infrared
channels from Apr. 2011 to Mar. 2012. We used IASI level1C data for reference data.
The light reached to satellite sensor are observed as DC values expressed with integer. For utilizing
the observation values, the radiometric calibration, from which way the DC values are changed to
radiance, is needed. The idealistic method for precise radiometric calibration is to ensure the indicator
for radiometric calibration in satellite instrument. But, in case that the ensurance of black body is
limited, the radiometric calibration can be conducted by outer value of satellite. By using the vicarious
calibration, which is the way using the other satellite data having well-known properties or the
corresponded theoritical radiance value with the observation point and time, the radiometric calibration
can be conducted. In this study, we used radiative transfer modeling for ocean, desert, cloud and DCC
(deep convective cloud) targets.
DATA
The meteorological mission and data service of COMS has begun since 1st April 2011. Normally
COMS MI measurement has two different observation modes: Full Disk (FD) and Extended Northern
Hemisphere (ENH). And COMS MI produces FD imagery every 3 hours and ENH imagery every 15
minutes. The COMS MI has one visible and four infrared channels.(Table 1)
Channel
Visible
Shortwave IR
(IR4)
Water Vapor
(IR3)
IR1
IR2
Wavelength
Center
Range(㎛)
0.55~0.8
Wavelength(㎛)
0.67
3.5~4.0
3.7
4
6.5~7.0
6.7
4
10.3~11.3
10.8
11.5~12.5
12.0
Table 1 : Specification of MI channels.
Spatial
Resolution(km)
1
4
4
For inter-calibration of COMS infrared, reference instrument uses the hyperspectral Infrared
Atmospheric Sounding Interferometer (IASI) on the low Earth orbit Metop-A satellite. IASI has been
designed for operational meteorological soundings with a very high level of accuracy being devoted to
improved medium range weather forecast. KMA has checked the quality of infrared channels from Apr.
2011 to Mar. 2012. For the vicarious calibration of the visible channel of COMS MI, ocean, desert,
cloud targets and DCC (deep convective cloud) are used. NCEP total water vapor, NCEP wind speed
and direction, OMI column ozone amount, MODIS aerosol optical thickness, and SeaWiFS monthly
climatology are used as inputs for ocean target. NCEP total water vapor, OMI column ozone amount,
and MODIS BRDF parameter are used as inputs for desert target. MODIS cloud products are used as
inputs for water cloud and DCC target. In this analysis, we used the data during the period from Sep.
2011 to Aug. 2012.
In Moon calibration, to compute the annual degradation of the COMS MI visible channel, it is
assumed that several Moon views (typically once per month) are available, which fulfill the following
geometric and radiometric conditions in order to integrate the full signal produced by the Moon : The
Moon view used in this method is a global Moon image. KMA obtains the moon images by using
LA(Local Area) observation mode which is Korean peninsula observation mode, one of the three
COMS MI observation mode. This data have been obtained twice a month. In this paper, we used the
data for Feb 2011 to Aug 2012.
Figure 1 : The process of taking a moonmask
There is no data in the Moon image from the Earth, the atmosphere, the Sun (no straylight) or any
star whatsoever. For this, we take off the moonmask(only moon part of the image) of above image.
The Sun must be far enough, i.e. the phase angle as seen from the Imager shall be less than 90 deg.
METHOD
The inter-calibration system using COMS for infrared radiation based on the GSICS Coordinate
Center (GCC) Algorithm Theoretical Basis Document (ATBD). In order to verify infrared channel
calibration, COMS and IASI data are selected if meet the collocation matching conditions. We checked
three types of condition. First, to check difference of observation time, we verified that the observation
time difference is within 5 minutes. Second, to check difference of SZA (Satellite zenith Angle), we
selected pixels which less than threshold value using SZA data both COMS and IASI. This threshold
value is defined as 0.01 in clear and 0.03 in cloudy weather condition that is correspond to a maximum
1% and 3% difference of SZA. The third is environment uniformity check. To reduce uncertainty
between the observation conditions of two satellites due to temporal and spatial mismatches, only
measurements over uniform scenes are selected.
The collocated COMS and IASI data cannot be compared without considering the sensor’s spectral
response difference. Therefore, IASI radiances are converted into a simulated COMS radiance
according to the spectral responses of COMS infrared channels using constraint method. The method,
called the constraint method, generates a super channel consisting of combination of the hyper
channel to imitate a broadband channel. For IR4 band (shortwave IR), considering the solar radiance
we used only the nighttime data. In order to calculate the simulated radiance of COMS using IASI data,
we produce the super channel having a very similar spectral response to a broadband channel in the
hyper channels within the spectral range of the broadband channel. And then we produce the weight
of the super channels. So, we could calculate the radiance (𝐼𝑣 ) of super channel using by Eq. (1):
(1)
where 𝑊𝑖 is weight for IASI and 𝐼𝑖 is radiance of IASI and 𝑖 is channel index number of IASI. The
spectral range of IASI data does not cover the spectral range of COMS SWIR channel data. So,
radiances of gap channels estimated by using valid hyper channel observations and simulated
radiances for 8 profiles (Tahara and Kato 2008).
For the vicarious calibration of the visible channel of COMS MI, ocean, desert, cloud targets and
DCC (deep convective cloud) are used. For the simulation of ocean and desert target, 6S (Second
Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model is used. NCEP total
water vapor, NCEP wind speed and direction, OMI column ozone amount, MODIS aerosol optical
thickness, and SeaWiFS monthly climatology are used as inputs for ocean target. NCEP total water
vapor, OMI column ozone amount, and MODIS BRDF parameter are used as inputs for desert target.
For the simulation of water cloud and DCC, SBDART (Santa Barbara DISORT Atmospheric Radiative
Transfer) radiative transfer model is used. MODIS cloud products are used as inputs for water cloud
and DCC target.
The Ocean targets that are homogeneous and dark should be considered only a clear pixel. In this
case, it is affected by aerosol, because the surface reflectance is so small. Therefore, aerosol optical
thickness has to be considered as an auxiliary data in radiative transfer modeling. In addition, it has
the advantage that radiance can be calculated without BRDF (bidirectional reflectance distribution
function) because the surface reflectance of the ocean is nearly homogeneous. In case of the cloud
target, atmosphere and surface effects can be minimized in the simulation of TOA radiance due to
strong reflection of cloud layer. And properties of cloud as an auxiliary data should be used in radiative
transfer modeling. Thus, in case of the target with the ocean or the cloud, it has the disadvantage that
has to match the COMS MI data with aerosol or cloud product data from other satellite (Ham and
Sohn 2010). On the other hand, in case of the target of the desert, aerosol effect can be minimized in
the simulation of radiance due to strong reflectance of desert and aerosol can assume the value of the
climate in RTM. Since surface reflectance varies with the positions of the sun and satellite, the BRDF
of target surface was required for radiative transfer calculation (Chun et al., 2012).
The visible calibration using moon is based on the lunar database provided by the RObotic Lunar
Observatory (ROLO) of USGS, that describes an empirical model of the lunar disk reflectance as a
function of its phase angle (the angle between the Moon-Sun direction and Moon-Earth direction) and
irrespective of the librations and the properties of the Moon surface. In order to monitor the
degradation of the Imager visible channel, it is proposed to use a method based on the comparison
between the global signal measured by the instrument and the model provided by ROLO. ROLO
measurements have very good relative performances, i.e. from one measurement condition to another.
The absolute accuracy is however not as good. Thus the best use of ROLO model is its application in
a multi-temporal approach. With a long-term set of observations, relative visible response trending of
the Imager have residuals about 0.1%. Then to monitor the behaviour of the instrument we define a
quantity which depends only of the instrument response. The instrument state at a given time is used
as a reference point; the Moon image is then used to monitor the instrument evolution with respect to
this reference.
P=
Where
•
𝐼𝑖𝑛𝑠𝑡𝑟𝑢𝑚𝑒𝑛𝑡
𝐼𝑟𝑒𝑓
𝐼𝑖𝑛𝑠𝑡𝑟𝑢𝑚𝑒𝑛𝑡 is the Moon irradiance as measured by the Imager
𝐼𝑟𝑒𝑓 is the Moon irradiance computed from the ROLO model, under the same conditions
All Imager responses are linearly trended over the various Moon images with the ratio
𝑃(𝑡)
expressed in percentage.
𝑘=
RESULTS
𝑃(𝑡0 )
The results of infrared inter-calibration from Apr. 2011 to Mar. 2012 showed the averages of
temperature difference between COMS and IASI are 0.4K for IR1(10.8μm), -0.4K for IR2(12.0μm),
0.3K for IR3(6.7μm) and -0.2K for IR4(3.7μm). For each channels, the difference between daytime
bias and nighttime bias is less than 0.1K. IR1 is the largest difference only 0.06K. Overall, there is little
difference between night and daytime. And COMS MI TB of all channels is correlated with IASI
brightness temp within 0.5K in figure 2. For IR1 band, the difference in low TB around 200K is about
1.4K, while the difference in high TB around 290K decrease in 0.2K. Namely, In low range of TB below
250K, the TB of COMS data is higher than IASI. But the difference is smaller and to zero in high TB.
On the other hand, for IR2 band, the difference between MI TB and IASI TB tend to negative bias. The
difference in low TB around 200K is about 0.3K, while the difference in high TB around 290K decrease
in -0.4K. For IR3, tendency of the bias is similar to IR1. For IR4, the difference TB is large in low TB,
but the bias is small about -0.2K in figure 3. The difference between COMS and IASI brightness
temperature shows positive bias for IR1, IR3 while negative bias for IR2, IR4. And for each channels,
the slope of monthly variation is less than 0.01K/month in figure 4. Although there is a slight bias,
infrared channels of COMS MI are correlated well with IASI data.
Figure 2 : Scatter plots of observed brightness temperature for COMS versus the corresponding brightness
temperature for IASI from Apr. 2011 to Mar. 2012.
Figure 3 : Scatter plots of the difference between observed brightness temperature for COMS and brightness
temperature for IASI from Apr. 2011 to Mar. 2012.
Figure 4 : Time series of monthly mean bias to COMS and IASI brightness temperature from Apr. 2011 to Mar. 2012.
The results of vicarious calibratoin from Sep. 2011 to Aug. 2012 showed that the average of
reflectance difference between observed and simulated are -0.8% for ocean target, +0.3% for desert
target, -4.4% for water cloud target, -10.7% for deep convective cloud target and the overall average
showed about -4.0% in figure 5. Therefore, the difference in low reflectance around 0.05 between
COMS reflectance and simulated reflectance is smaller and to zero. On the other hand, the difference
in high reflectance around 1 tends to be larger about 10%. And the slope average of forced intercept
to zero from Sep. 2011 to Aug. 2012 shows about 0.9 for all targets. The slope trend of forced
intercept to zero shows about 2% of visible channel degradation from Sep. 2011 to Aug. 2012 in figure
6. For obtaining more precise result, we need more data through the long term observation.
Figure 5 : Scatter plots of observed reflectance for COMS versus the corresponding simulated reflectance from Sep.
2011 to Aug. 2012 for all targets.
Figure 6 : Time series of monthly slope from Sep. 2011 to Aug. 2012 for all targets
The image responses graph of moon is shown in figure 7. This graph shows that the instrument
irradiance is degraded to about maximum 7% compared with ROLO values. Especially, we can
confirm degradation trend from Aug 2011 to Mar 2012 easily. But, in the results from Jun 2012 to Aug
2012, visible channel performance looks like improved. These phenomena are found in two point,
about Jun 2011 and Jun 2012. So, we assume that there is some seasonal effect in this period. In
advance, we have a plan to research whether this phenomena is truly related with seasonal effect. If
this is not related this, we are confirming whether any other problems with our methods and
calculations on the way of applying these methods are exist or not.
The regression line from image responses is shown in figure 7. From this regression line, there is
about 3.0345% visible channel degradation in total period of observing moon (Feb 2011 ~ Aug 2012).
Even though some data are contrary to overall trend in Jun 2011 and Jun 2012, visible channel has
some degradation patterns in total period. Since the number of moon observation is normally twice a
month. Total number of data is only 36. Therefore, for obtaining more precise result and confirming the
accuracy of data, more data and long term observation are needed.
Figure 7 : Image responses(left) and trend(right) from Feb 2011 to Aug 2012
SUMMARY
For confirming the radiometric performance of COMS, National Meteorological Satellite
Center(NMSC/KMA) has own infrared-calibration system using Metop-IASI data and visible-calibration
system using ocean, desert, water cloud, deep convective clouds and moon data.
In Infrared channels calibrations, COMS MI IR channels have low-bias within 0.5K accuracy. IR1 and
IR3 of difference between COMS TB and IASI TB showed positive bias while IR2 and IR4 showed
negative bias. Bias of COMS visible channel can be obtained by using ocean, desert and cloud targets.
In vicarious calibration of the visible channel, COMS visible channel underestimates about 4%. The
slope average shows about 0.9 for all targets. And the slope trend shows about 2% of visible channel
degradation. COMS MI visible channel degradation for Feb 2011 to Aug 2012 is shown about 3% from
the regression line of monthly moon data. But, some data are contrary to overall trend in Jun 2011 and
Jun 2012. Although there is a slight bias, infrared and visible channels match up well with reference
data. KMA has been doing research on calibration of visible and IR channels through the long term
observation.
REFERENCES
Chun, H. W., B. J. Sohn, D. H. Kim, M. H. Ahn, M. L. Ou, (2012) Solar channel calibration using desert
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of Japan, 90,191-205.
COMS.TN.00116.DP.T.ASTR.COMS MI Radiometric Model.p.30-41.
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channel using cloud targets: application to meteosat-8/9 and MTSAT-1R. Atoms. Chem. Phys., 10,
11131-11149
Tahara, Y., and K. Kato, (2008) Spectral Adjustment for Intercalibration between Broadband Sensor
and High Spectral Resolution Sounder. Japan Meteorological Agency Technical Note.
Wu, X., and F. Yu, (2011) GSICS algorithm theoretical basis document (ATBD) for GOES-AIRS/IASI
inter-calibration.