Thermal And Near infrared Sensor for carbon Observation (TANSO)
onboardthe Greenhouse gases Observing SATellite (GOSAT)

Research Announcement

Appendix B
GOSAT/TANSO Calibration and Validation Plan and Overview of Processing Algorithms

B-1 GOSAT/TANSO Calibration Plan
B-1.1 Outline“Calibration” is an evaluation process to clearly characterize the sensor and to ensure that theaccuracies of radiance, geometry, spectral characteristics, image quality, etc. meet the target values,
using Level 1 (L1) products out of the standard products. The specific methods of evaluation
include:・ Evaluation of performance in ground tests (before the launch, using ground test data)
・ In-orbit calibration (after the launch, using cal-mode data), and・ Vicarious calibration (after the launch, using actual observation data, etc.)
Based on the results of these calibration activities, L1 products will constantly be upgraded byreflecting the revised calibration coefficients and the corrected L1 processing algorithm in the
ground processing system.
B-1.2 Calibration Schedule
The evaluation of the sensor performance on the ground will be completed by approximately one
year before the launch. The initial in-flight calibration will be carried out during theoperational phase, between three and six months after the launch. The calibrated Level 1
products will be released as Version 1, expectedly within six months from the launch. Thus,
the data to be collected and examined during the initial calibration period only covers a periodof less than a year, and it is important to thoroughly examine match-up conditions, etc. for dataacquisition during this period.
B-1.3 Pre-launch Performance Test
The main functions and capabilities of TANSO-FTS and TANSO-CAI will be evaluated before
the launch. The evaluation is basically conducted by checking the following items in groundtests.
(1) Sensitivity and signal-to-noise ratio (SNR)
The sensitivities of FTS at short wavelength infrared bands and CAI, an indicator for thephotoelectric conversion efficiency, and SNR are calibrated and evaluated by using an evaluatedintegrating sphere. The evaluation of the integrating sphere is carried out by using a comparative
spectrometer in comparison with a fixed point blackbody furnace coated with such material aszinc. The sensitivity calibration for FTS thermal infrared (TIR) band and SNR evaluation are
performed by using a blackbody cavity at the thermal vacuum test.
(2) Polarization sensitivity
The polarization sensitivity of FTS at short wavelength infrared bands and CAI will be carriedout in the following steps: i) install a polarizer at the exit port of the integrating sphere to form
linearly polarized light, ii) rotate the phase of the linearly polarized light by rotating the 1/2
wavelength plate attached behind, and iii) introduce rotating linearly polarized light so as toB-1

obtain the polarization sensitivity. In case of TANSO-FTS, the incoming light is polarized
mainly at the reflector of the pointing mechanism and the beam splitter of the Fourierinterferometer mechanism.
(3) Instrument function
FTS’s instrument function serves as an indicator for the spectral resolution. The function is
obtained by introducing diffused light from a tunable laser for each band. As for CAI, thespectral characteristics are obtained by introducing monochromatic light.
(4) Field of View (FOV) and Modulation Transfer Function (MTF)
The FOVs of FTS and CAI, which serve as indicators for optical capabilities, and the MTF of
CAI are evaluated by setting up a point light source and a slit light source at the collimator.
The alignment to the sensor reference axis and band-to-band/pixel-to-pixel alignment are also
evaluated at the same time.
In addition, FTS is equipped with a high-resolution monitoring camera (CAM) to check its FOV.
The CAM has the same look direction as FTS. The alignment of the CAM to the sensor
reference axis will also be evaluated.
B-1.4 Post-launch calibration
Calibrations after the launch will be carried out in two modes: in-orbit calibration and vicarious
calibration. The former is performed on the calibration mode data, whereas the latter uses
actual observation data.
The cal-mode data acquired during in-orbit calibration are precisely defined in Chapter C-2.4.3“Operation in the calibration mode” of Appendix C ”Operation Policies of GOSAT and Basic
Observation Plan of the TANSO Sensor”. The cal-mode data are acquired in accordance withthe operation policies provided thereof and used for L1 processing, trend evaluation, etc.
B-1.4.1 Calibration of the TANSO-FTS (short wavelength infrared (SWIR) bands)
The in-flight calibration of TANSO-FTS will be carried out in three aspects: radiance, geometry
and spectral characteristics. The following items will be evaluated with respect to each of P
and S polarization bands in short wavelength infrared (SWIR) bands (Bands 1-3).
(1) Radiance evaluation(a) Calibration of the sensitivity
In-orbit calibration of SWIR bands will be performed through solar irradiation
calibration using the diffuser plate for every orbit, and deep-space calibration in parallelwith TIR band is performed. In L1 processing, it is planned to convert the observed
values into radiance values using the sensitivity calibration coefficients obtained fromground tests. The coefficients will be revised based on an analysis of the results fromthe solar irradiance calibrations, lunar calibrations (to be discussed in more details later),and vicarious calibrations.
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(b) Lunar calibration
The purpose of lunar calibration is to calibrate the efficiency of the overall opticalsystem for SWIR bands by using the reflected solar radiation from the moon surface as
the calibration source. The lunar calibration will be performed on the orbit when the
average of lunar surface radiance becomes the largest (at lunar age of 14.8±0.1). In
this calibration, the satellite will be oriented to the moon so that FTS’s FOV catches themoon on the full scale, and FTS will keep pointing at the moon with its pointing
mechanism for at least two minutes.
The moon is an extremely stable reflector with a radiance variance of 10 -7/year. The
stability has been reported so high that the fluctuation is less than 1%, based on the
results from the sensitivity trend evaluations for those sensors observing ultra violet toshort wavelength infrared bands, such as MODIS, ASTER, etc., that have been
conducted as part of lunar calibrations (USGS, 2006). The evaluation methods
adopted in these past evaluations are as follows:
・ The sensitivity trend was evaluated at each time of calibration using a stable lightsource.
・ The absolute sensitivity was evaluated based on ROLO data—ROLO (RObotic Lunar
Observatory) is USGS’s reflectance database for lunar calibration.
(c) Linearity
The linearity for the response of the optical system, including temperature dependence,
will be checked based on the data observed while uniform light with different radiancelevels is introduced into the FTS optical system. Listed below are candidate targets
that have uniform radiances.
・ Offset:
Deep-space calibration
・ Low radiance:
Ocean, snow and ice (B2-3)
・ Medium radiance: Forestry
・ High radiance:
Solar irradiation calibration, snow and ice (for B1 only), dry saltlakes (for B1 only), desert, playa(d) Absolute radiance
The radiance observed by FTS is compared with the radiance of ground surface
radiation obtained from the experiments in vicarious calibrations or synchronous
observations with a well-calibrated sensor on board other spacecraft or aircraft. In
order to match the radiances of the reference instrument and FTS, the sensor radiancemodel, and calibration coefficients are adjusted and absolute radiometric accuracies areevaluated.
There are two major approaches to this evaluation method:・ To use in-situ data
・ To use global data(i) Using in-situ data
Specific physical parameters, such as the ground-surface reflectance, as to a
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specific location on the earth are obtained accurately and synchronously in avicarious calibration experiment. The GOSAT-observed radiance is evaluated incomparison with the radiance at the top of atmosphere computed by using theabove mentioned parameters. Since the observation bands contain absorption
bands of CO2, CH 4 and other species, it is desirable to synchronize the validation
experiments with this evaluation.
(ii) Using global data
The synchronized data are extracted from the data acquired by a well-calibrated
sensor on board other spacecraft for radiance comparison. Since the radiance isinfluenced by the observation timing or geometry, the FTS data and the reference
data may not easily fit when directly compared. In such a case, the radiance isevaluated by comparing the GOSAT-observed radiance with the radiance at the
top of atmosphere computed based on the physical parameters, such as
ground-surface reflectance.
Currently, Aqua/MODIS and OCO (Orbiting Carbon Observatory) are listed as
candidates for reference satellites in this comparative evaluation.
(e) Relative radiance between the sensors
FTS and CAI both observe the 1.6μm band in Bands 2 and 4, respectively. The relative
radiance accuracy between the two sensors is evaluated by assessing the temporalvariation and temperature dependence per gain.
(2) Geometric evaluation(a) Pointing accuracy
The alignment of the CAM, attached in the FTS sensor for checking its FOV, and FTS is
assumed to be known and the CAM has an FOV of approximately 30 km and a spatial
resolution of approximately 100 m in the VGA mode. FTS’s geometric accuracy isevaluated using imagery taken by the CAM over GCPs with known coordinates as well
as clear radiance boundaries between bright and dark. The following scenes are to be
used in this evaluation:
・ Land/sea boundaries (scenes containing coastal lines also covering projections from theland, such as peninsulas and capes)
・ Islands (scenes containing the whole land of an island within the FTS’s FOV of 10 km)(b) Pointing stability
The pointing accuracy of the pointing mirror during an interferogram measurement(typically for four seconds) is evaluated by acquiring data by the CAM in the movie
mode. The following scenes are to be used in this evaluation:
・ Land/sea boundaries (scenes containing coastal lines also covering projections from theland, such as peninsulas and capes)(c) Geometric accuracy between the sensors
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The geometric accuracy between FTS and CAI is evaluated by assessing thedisplacements of common GCPs on CAM and CAI images. Further, since the sensors
observe the same wavelength region, the geometric accuracy of FTS can becomparatively assessed against CAI by searching the position of FTS Band 2 (10-km
resolution) on a CAI Band 4 (1.5-km resolution) image with the radiance matching
function.
(3) Evaluation of spectral characteristics(a) Instrument function
The instrument function of Band 2 is evaluated by irradiating 1.55μm semiconductor
laser light onto the diffuser plate and observing the diffused light.
(b) Wavelength accuracy
The wavelength shift is evaluated by referring to the wavelength position of a known
atmospheric absorption band.
(c) Polarization sensitivity
The radiance ratio between the P and S polarization bands of the target with known
polarization characteristics is evaluated using the following data.
・Solar irradiation calibration data (natural light of the sun is unpolarized.)(d) SNR
Targeting a uniform high-radiance range, the SNR is evaluated as to each band detector
and gain. The following data will be accumulated and used for the evaluation.
・ Solar irradiation calibration data
・ Desert, snow and ice (for B1 only)
B-1.4.2 Calibration of TANSO-FTS (TIR band)(1) Radiance evaluation(a) Calibration of the sensitivity
In-orbit calibration of TIR band will be performed using deep-space radiation(equivalent to a radiance temperature of 3 K) and the blackbody on board the satellite(equivalent to a radiance temperature of app. 290 K). The L1 processing algorithm forthis calibration is currently assumed to employ the following methods:
・ The blackbody and deep-space radiances are assigned to the highest level and the lowest
level, respectively, for fitting based on a high-order regression curve. The values
obtained in the ground tests are used as the factors in the high-order terms.
・ Since the sensitivity is subject to the influence of background radiances inside theinstrument, phase correction should be taken into consideration, while at the same time
directing attention to the combination of data to be used in the calibration, the monitored
temperature of the sensor, etc.

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(b) Linearity
The offset and linearity of the response of the optical system, including temperaturedependence, will be checked based on the observation data taken while uniform lightwith different radiance levels is introduced into the FTS optical system. The following
lists up candidate data to be used for this evaluation.
・ Offset: Deep-space calibration
・ Low radiance: Snow and ice
・ Medium radiance: Forestry, oceanography
・ High radiance: Blackbody calibration, deserts, playa(c)Absolute radiance
Similarly to SWIR bands, the absolute radiance of TIR band will be evaluated by thefollowing two approaches:・ To use in-situ data
・ To use global data(i) Using in-situ data
A high-accuracy calibration will be performed by acquiring the atmospheric profile,including temperature of air, water vapor, etc., while targeting the locations where thesea surface temperature is measured with buoys.
(ii) Using global data
The synchronized data are extracted from the data (equivalent to Level 1) acquired bya well-calibrated sensor on board other spacecraft for radiance comparison.
Furthermore, the accuracy of calibration is evaluated over a wide area of the ocean byusing sea-surface temperature and meteorological data on the global scale with a high,consistent accuracy.
Currently, Aura/TES and Aqua/AIRS are listed as candidates for reference satellites inthis comparative evaluation, and NOAA’s sea-surface temperature (SST) analysis of
Reynolds, etc. are planned for the reference database.

(2) Geometric evaluation
Since the same optical system is used for acquiring FTS SWIR and TIR band data, the geometricevaluation of TIR band is conducted following that of SWIR bands.
(3) Evaluation of spectral characteristics(a) Wavelength calibration
The wavelength shift is evaluated by observing known atmospheric absorption bands.
(b) SNR
Targeting a uniform high-radiance range, the SNR is evaluated as to each gain.
following data will be accumulated and used for the evaluation.
・ Blackbody calibration
・ Desert
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TheB-1.5 Calibration of TANSO-CAI(1) Radiance evaluation(a) Calibration of the sensitivity
CAI does not carry any special functions associated with the sensitivity calibration inorbit. Thus, the calibration is performed only with the sensitivity calibration
expression to be obtained in the ground tests. However, the following data will beacquired as solely for the offset (dark signal output).
・ Nighttime observation
・ Optical black element(b) Lunar calibration
The lunar calibration for CAI will be performed on the same orbit as that of FTS. It is
intended to calibrate the sensitivity of the devices of the linear array detectors where
lunar calibration light enters. In this calibration, CAI imagery will be acquired while
scanning an area of ±2.55deg or greater covering the entire moon surface twice in bothways (four scans) continuously at an angular velocity of 0.1 deg/sec or less in the Y-axis
direction.
(c)

・
・
・
・

Linearity
The linearity of the response of the optical system, including temperature dependence, isevaluated based on the observation data taken while uniform light with different
radiance levels is introduced to the CAI optical system. Listed below are candidate
targets that have uniform radiances.
Offset: Nighttime observation, optical black element
Low radiance: Ocean, snow and ice (for B4 only)
Medium radiance: Forestry
High radiance: Snow and ice (B1-3), dry salt lakes (B1-3), desert, playa(d) Absolute radiance
Similarly to FTS, the absolute radiance of CAI will be evaluated by the following two
approaches:・ To use in-situ data
・ To use global data(i) Using in-situ data
The calibration coefficients are determined through optimization based on aerosol datameasured at as many locations as possible by the AERONET (NASA, 2006) and
SKYNET (Chiba Univ., 2006), both of which are deployed worldwide.
(ii) Using global data
The synchronized data are extracted from the data (equivalent to Level 1) acquired by awell-calibrated sensor on board other spacecraft for radiance comparison. Furthermore,
the accuracy of calibration is evaluated by using the ground surface reflectance, aerosolparameters, climatological values, and other physical values (equivalent to Level 2) or
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databases providing these values, acquired by other spacecraft on the global scale with ahigh, consistent accuracy.
Currently, Aqua/MODIS and Aura/OMI are listed as candidates for reference satellites
in this comparative evaluation, and the ground surface reflectance datasets of GOME
(Global Ozone Monitoring Experiment) and TOMS (Total Ozone Mapping
Spectrometer), etc. are planned for the reference database.
(2) Geometric evaluation(a) Pointing accuracy
The geometric accuracy of CAI imagery is evaluated using imagery taken over GCPs
with known coordinates as well as clear radiance boundaries between bright and dark.
The GCP database to be used for GOSAT should be chosen beforehand from coastline
database GSHHS.
The following scenes are to be used in this evaluation:
・ Land/sea boundaries (scenes containing coastal lines also covering projections from theland, such as peninsulas and capes)(b) Band-to-band registration
The offset between bands is determined relatively by evaluating the pointing accuracy
as to each band, as described in (a) above.
(3) Image quality evaluation(a) Calibration of the pixel-to-pixel sensitivity
The coefficients for correcting the pixel-to-pixel sensitivity are calculated byaccumulating data in each pixel direction, deriving the average values, and comparing
the averages in the line direction, using data taken over wide-area targets with uniform,high radiances. Since GOSAT’s swath stretches for 750-1,000 km, it is infeasible toperform the calibration at once. Thus, more than one site must be selected for thecalibration. The following lists up the candidate target sites.
・ Snow and ice (B1-B3), desert(b) MTF
In order to extract edge and point spectra, the MTF is calculated using
geometrically-corrected observation data covering edges, such as coastal lines, andpoint sources, such as islands. Based on the derived MTF, the spatial resolution
dependent on the optical characteristics is evaluated. The following scenes are to be
used in this evaluation:
・ Coastal lines
・ Islands(c)

SNR
Targeting a uniform high radiance range, the SNR is evaluated as to each band detector
B-8and gain. The following data will be accumulated and used for the evaluation.
・ Snow and ice (B1-3), dry salt lakes (B1-3), desert, playa
B-1.6 Preparation for Calibration(1) Examination of the calibration approach with the use of in-situ data
In vicarious calibrations using TANSO observation data and ground observation data, it isimportant to select test sites in view of the uniformity (across the FTS’s FOV of 10 km),
temporal stability, probability of clear sky, and accessibility. The following candidate sites
have been found as satisfactory for the calibration purpose.
・Railroad Valley Playa, Nevada, U.S.A.
・Strzelecki Desert (Tinga Tingana), South Australia, Australia
These sites have been scrutinized in terms of appropriateness as the calibration sites. Railroad
Valley has constantly been used as a vicarious calibration site for Landsat, MODIS, ASTER, etc.
by the University of Arizona for a long time. The OCO project likewise is considering the useof the valley as its calibration test site. Tinga Tingana in Strzelecki Desert is a highly uniform
site where the CSIRO (Commonwealth Scientific and Industrial Research Organisation) of
Australia has set up a part of the AERONET.
Figure B-1-1 below shows MODIS images over Railroad Valley and Tinga Tingana. The redcircles indicate the FTS’s FOV of 10 km when it is pointing at the sites. Each image stretchesfor 30 km×30 km. While the uniform area in Railroad Valley just barely covers the 10-km
FOV of FTS, Tinga Tingana is covered with uniform land that amply extends over the FOV.
Figure B-1-2 plots the probability of clear sky throughout 2004 at these sites, and the graphapparently suggests that the probability drops during wintertime at both sites. Thus, it isdesirable to secure calibration sites in both north and south hemispheres in consideration of thelaunch schedule. It is still under examination whether these sites are selected for GOSAT’s
calibration sites or not.

B-9Railroad Valley

Tinga Tingana

Figure B-1-1 MODIS band 7 (2.1μm) images over the candidate calibration sites(2) Examination of the calibration approach with the use of global data
In order to select most appropriate sites for cross-calibration with data taken by other missions,the spatial uniformity, temporal stability, etc. at the candidate sites are evaluated based on theexisting global data observing these sites.

Figure B-1-2 Clear-sky rate at the candidate calibration sites(Aqua/MODIS data in 2004)

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Figure B-1-3 indicates an average reflectance distribution on the 5 x 5 pixel basis (app. 25
square kilometers) of MODIS band 7 (2.1μm) data and the standard deviation among 25 samples.
The samples were retrieved from the global reflectance data (average of 16 days) of MODIS on
board Aqua and Terra. There are several spots with uniform reflectance in the Sahara Desert
and the Rub’ al khali Desert. In addition, as the reflectance in this band is susceptible to soil
moist and decreases with it, a high reflectance is obtained only in deserts or bare lands.

Figure B-1-3 Survey on the spatial uniformity using the ground surface reflectancedata acquired by MODIS band 7 (2.1μm)
Upper: Average reflectance of 5×5 pixels (app. 25km)
Lower: Standard deviation of 25 samples
Figure B-1-4 shows an annual average of the ground surface reflectance in the Sahara Desert (ofapp. 22 samples), and the standard deviation for the year. POLDER (POLarization and
Directionality of the Earth's Reflectances) targeted the sites marked with x, namely Egypt 1(N27.12, E26.10) and Algeria 3 (N30.32, E7.66), in its vicarious calibration. (Hagolle, 1999)
Figure B-1-5 plots the reflectance at Egypt 1 and Algeria 3 during the year in the time series. It
reveals that those sites selected by POLDER have a high temporal stability and, as shown in
Figure B-1-3, a high spatial uniformity.
Based on the analyses of these existing data, the GOSAT Project will select the most appropriate
sites for its calibration activities in light of the resolutions and swaths of TANSO-FTS and CAI,
and collect data accordingly.
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Figure B-1-4 Investigation on the temporal stability based on the ground-surface
reflectance data of MODIS band 7 (2.1μm)
Upper: Annual average (app. 22 samples)
Lower: Standard deviation

Figure B-1-5 Chronological plotting of the ground-surface reflectanceat the specific observation points for one year
Left: Egypt 1 site
Right: Algeria 3 site

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B-2 GOSAT/TANSO Validation Plan
B-2.1 Basic philosophy for the validation“Validation” of GOSAT products means an evaluation of uncertainties of GOSAT L2, L3 and L4
products, as provided in Table 1 of the Research Announcement document *, compared with otherdata with less uncertainties that were acquired independently of GOSAT. “Comparison”, on theother hand, means an evaluation of uncertainties of GOSAT products against observation data withequivalent uncertainties or data estimated by simulation models; thus it should be distinguished from“validation” above. Both evaluation methods, however, are intended to assess the data quality of
GOSAT products (data quality assurance), and at the same time play a significant role in evaluating
the appropriateness of the data processing algorithms. Therefore, any findings from the evaluations
will be reflected in revising the algorithms, as necessary.
Additionally, the “calibration” is an evaluation of L1 product data to be performed mainly by JAXA
and focuses on calibration of radiance spectra (e.g., absolute radiance, linearity, offset, wavelength,
instrument function, SNR, etc.) However, it excludes an evaluation of spectral parameters and the
Fraunhofer lines, which are necessary for retrieval of the CO2 and CH4 column abundances based on
the radiance spectra obtained from TANSO-FTS observation data, since these values have been
evaluated at the algorithm development stage.
In formulating a validation plan for GOSAT TANSO, it is important first to assign priority orders to
the validation items because the manpower and budget for observation and analysis activities inconnection with the validation are not unlimited.
The appropriateness of the processing algorithms must be verified prior to the launch of the satellite.
This evaluation is called “algorithm validation”. The algorithm validation has been conducted
through observational experiments by the FTS sensor installed at a high elevation and on an aircraft.
These observational experiments must continually be performed in the future to improve thealgorithms.
After launch, another important evaluation, in addition to product validation, is to verify theappropriateness of parameters, such as optical characteristics of aerosols, which are relevant to errors.
This post-launch evaluation is called “parameter validation”. Although GOSAT makes greatersocial contribution with its higher-level products than lower-level products (L2), validation of thelatter is of primary importance as it provides the foundation for generating the former. Thus, it ispreferred to perform validation of lower-level products first followed by that of higher-levelproducts.
The priorities of GOSAT product validation and comparison have been set as follows:(1) L2 SWIR CO2 and L2 SWIR CH4 column abundances(2) L2 TIR CO2 and L2 TIR CH4 vertical profile of concentration(3) Global distributions of L3 SWIR CO2 and CH4 column abundances(4) Global distribution of L4A CO2 flux(5) Global distribution of L4B CO2 concentration
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uncertaint y  (accuracy ) 2  (precision) 2 accuracy  bias
The basic policy on collection of validation data is to prioritize the use of diverse observation data
being acquired routinely in all parts of the world, considering that the GOSAT itself observes theentire earth on varied conditions in terms of ground surface, meteorology, and atmosphere. To thisend, certain specific observation points will be set as “super sites” for focused and continued
observation for the validation purposes of products and algorithm, and necessary observing
equipment are installed at these sites. Furthermore, campaign observations will be planned andcarried out, which include air-borne observation on various meteorological conditions.
The data to be validated are those that have been taken on the following geographical andmeteorological conditions:a. Land area with clear sky
b. Sea area with clear sky (sunglint observation)c. Land area with thin cirrus clouds and aerosols
d. Sea area with thin cirrus clouds and aerosols (sunglint observation)*Excluding areas covered with thick clouds.
In addition, the validation must be conducted in light of certain standards for match-up conditions interms of space and time.
Of all the analytical work in the validation, NIES will be responsible for the basic validations
analysis and incorporation of the results into the revision of algorithms, as part of the GOSAT
Project. In the meantime, it is expected that more research activities concerning the validation
analysis of the GOSAT data products will be carried out by the GOSAT Science Team members,scientists participating in the RA, and other users at large.
B-2.2 Experimental observation for the algorithm validation
The processing algorithm to derive the CO2 column abundance must be verified prior to the launchthrough experiments simulating the satellite observation geometry, more specifically, by observing
solar light reflections from the earth surface with an FTS, a similar sensor to the one on board thesatellite, installed at a high-elevation ground point or on board an airplane.
(1) Observation at a high elevation
NIES carried out two observational experiments using an FTS sensor, similar to TANSO-FTS,
installed near the summit of Mt. Tsukuba Japan at an elevation of 800 m in 2005 and 2006. In
these experiments, the CO2 concentration calculated from the data acquired by FTS was comparedwith the measurement made by a light airplane, which took off from near the farmland close to thefoot of the mountain and flew up to an altitude of 3,000 m carrying the CO2 in-situ instrument. The
results demonstrated an accuracy of about 1% of the FTS’s observation with respect to the CO2column abundance in the atmospheric layers at an altitude of 3 km and lower. It is vital to continue
observational experiment in this manner on various meteorological conditions in the future, pursuing
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an improved accuracy.
(2) Air-borne observation
An algorithm validation is made by the spectroscopic observation using the FTS mounted on theaircraft at a very high altitude, simultaneously with the in-situ CO2 concentration measurement
onboard an aircraft. This method more directly simulates the behaviors of the FTS on board thesatellite in nadir looking observation from a very high altitude than the observation at ahigh-elevation ground point, although the vibration of the aircraft remains as a problem.
B-2.3 SWIR/TIR Product Validation(1) Validation of CO2 and CH4 column abundances by SWIR observation
The observing instrument to be used for validating the CO2 and CH4 column abundances must be
based on fully-established technology with no bias, assuring data quality as well as achieving an
excellent observation accuracy of about 1 %. An observation method which uses absorption of
direct solar light is the best technique for observing the column abundance directly. Thus, ahigh-resolution FTS installed on the ground proves to be the most appropriate method among all
available measuring instruments. FTS can observe spectrum anytime as long as there is direct solar
irradiance for approximately 10 minutes.
The footprint of TANSO-FTS extends for as long as 10.5 km. In order to reduce observation errors,
observation points where the surface cover is flat and uniform within this size are ideal for the
purpose of the validation. Some of FTS’s observation points are situated in mountainous areas;thus it is essential to collect surrounding information of the observation points and select flat areasthat will least contribute to observation errors.
If there is an obvious bias in the TANSO-FTS retrieval results or if the measurement accuracy falls
below a certain level, it is necessary to identify the cause of the error and improve the dataprocessing algorithm. Of the various parameters that affect the retrieval of column abundances,namely cirrus cloud, aerosol, surface albedo, air pressure, air temperature, water vapor, andconcentrations of other gases, the effects of cirrus cloud and aerosols are the most dominant. The
surface albedo can be obtained fairly accurately from the TANSO-FTS observation data by means ofa retrieval analysis and it does not substantially affect the derived column abundance value.
Therefore, the priority of the surface albedo is set low. Furthermore, the priority of meteorologicalparameters, such as air pressure, air temperature, and water vapor, is also low since they are usuallytaken from meteorgical reanalysis data.
Measuring cirrus cloud and aerosol requires i) a sky radiometer that can estimate the optical
thickness, the grain size distribution and the complex refraction index averaged in the nadir direction
and ii) a polarized lidar that can identify the vertical distribution of cirrus clouds and aerosols anddistinguish between sphericity and non-sphericity. Thus, it is important, in terms of validation
strategy, to set up these instruments at the ground based FTS observation sites to enable
simultaneous observation.
The vertical distribution data directly measured (sampled and analyzed) by a taking-off and landing
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regular passenger airplane provide an effective reference for the derived CO2 column abundance.
However, this measurement can gather data for the vertical profile of CO2 concentration between the
ground level and an altitude of approximately 11 km. As about 22 % of CO2 exists above the
11-km altitude, other methods to either correctly estimate or measure the CO2 level above thisaltitude are needed for calculating the column abundance. If the fluctuation of CO2 abundance inthe higher altitudes remains within 4 %, the model climatological values derived from the past actual
measurements can be used. Furthermore, CO2 concentration on the ground surface needs to bemeasured in order to ascertain the concentration between the ground level and the minimum altitude
where aircraft can measure. Moreover, it is desirable to use the sky radiometer and lidar in thevicinity of the validation sites for measuring cirrus cloud and aerosol. At some airports, however,
the air is significantly contaminated. Therefore, meteorological conditions of the sites must be
carefully examined when selecting validation sites.
As for CH4, there is a possibility that air-borne observation (bottle sampling) data is effective.
Moreover, it can be effective in validation as there are most recent data up to an altitude of about 35
km based on balloon observation.
Sunglint observation basically takes place on the ocean. However, validation of product bysunglint observation may be replaced by data acquired by a high-resolution FTS installed nearcoastal lines and/or data obtained by aircraft flying above the vicinity of coastal lines.
WDCGG provides ground-based CO2 and CH4 measurements. This dataset may well serve as
collateral evidence if used with consideration given to seasonal variation and spatial
representativeness. Among others, in the south hemisphere, where the seasonal variation ofconcentration is very small, this dataset is highly effective and may even serve as validation data.
One of possible scientific validation methods is to synthesize (or assimilate) the CO2 distribution in
the surrounding of the observation point based on the concentration profile measured by air-borne
using regional atmospheric transport models, calculate the concentration profile up in the sky overthe flat place with uniform albedo, which corresponds to the GOSAT observation point, and compare
the findings with GOSAT product. However, the appropriateness of the method itself needs to beverified, first of all, and there are several other problems that remain to be addressed with thismethod.
The following sets forth policies concerning validation data collection.
・Install a number of observation instruments (high-resolution FTS, lidar and sky radiometer)at several specific observation points and perform observation with priority at these “focal
observation points”.
・Effectively utilize “campaign observation with aircraft if necessary” for observation data onsuch meteorological and ground surface conditions that are not covered by the “focal
observation points”.
・Utilize the observation data routinely acquired, such as those taken by the existinghigh-resolution FTS installed on the ground, air-borne observation data, and WMO
WDCGG ground observation data, as the GOSAT observation coverage is the entire globe.
・The column abundances in sea areas are derived based on sunglint observation. As theconcentration variation is smaller in sea areas than land areas, data covering the validation
B-16

sites on the ground that are located near the sea can be used with consideration given to thewind direction. Moreover, observation data acquired in islands and by vessels can also be
used for indirect evaluation.
Based on the above policies, data at typical observation points are acquired; the specific criteria for
selecting such observation points can be summarized as follows:・Latitude: low , middle , high・Amount of water vapor: high, low
・Albedo: uniform, nonuniform
・Aerosols: variant, invariant
At present, if 10 validation points for ground-based FTS observation and another 10 for aircraft
observation can be acquired, minimum requirements in the validation can be satisfied. The total 20
are expected as sufficient to minimally cover the above conditions. However, if a handy validation
observation instrument which satisfies the validation requirements is developed, more points may be
selected and the validation can more largely be deployed.
Data measured at each observation point will be evaluated in light of both temporal and spatial
variations to identify the uncertainties in the products. However, the evaluation of biases requires
roughly three months at the sites where seasonal variation is small and six months in general. The
evaluation results will be sorted according to the above criteria and quality will be assured by data
category. In case that this approach is difficult, alternative effective methods include a trajectory
analysis with consideration given to temporal and spatial variations and a validation analysis basedon regional CO2 transport models.
The following describes specific plans of observation and data acquisition for the validation purpose.
a) Validation by FTS observation(i) Observation by ground-based high-resolution FTS
Observation by ground-based high-resolution FTS will be performed at several observation
points in Japan, on the regular basis.
In conducting the validation, it is additionally useful that the observation data acquired by
the ground-based high-resolution FTSs are analyzed and evaluated using the same dataprocessing algorithm (radiative transport equation) as the one used for the analysis of
TANSO-FTS.
(ii) Utilization of data acquired by ground-based high-resolution FTS networks etc.
Data from the ground-based high-resolution FTS observation networks will be used.
The Project will also arrange the use of data at other observation points through direct
negotiation with the pertinent data providers, RA-based joint researches, and so forth.
When selecting ground observational sites for the GOSAT validation, the most ideal sites at
the initial validation stage should preferably have flat and uniform surface and extend for awide range commensurate with the 10.5-km footprint of TANSO-FTS, whereby error
factors can maximally be avoided.
B-17

Thus, the validation sites will be selected based on the following criteria.
・Seasonal variation: small (south hemisphere)→ large (north hemisphere)
・Air pollution: minor → major・Land surface: simple → complex
The options are:・Sunglint
・Snow and ice(iii) Observation by small FTSs
If moving observation is necessitated, these small-sized FTS can be used, after
evaluating the measurement accuracy, data quality and operational stability.
b) Validation by air-borne/balloon-borne observation(i) Utilization of data from the air-borne observation
The continuous CO2 measurement equipment on board aircraft acquires the spatial(horizontal) distribution of CO2 concentration at very high altitudes and the vertical profiles
CO2 concentration around the airports, since it can acquire data while not only flying
horizontally but also taking off (ascending) and landing (descending).
Additionally, as aircraft travels in the horizontal direction for approximately 100 km while
ascending up to an altitude of about 10 km where it flies horizontally, and 200 to 300 km
while descending from the stable altitude, spatial representativeness of the concentration
should be taken into consideration at the time of analysis. To this end, transport models
can be used for this evaluation. Furthermore, since the concentration in the lower
atmosphere near the airport grows due to effects from the urban air in many cases, it isnecessary to judge effectiveness of data in view of the wind direction. Moreover, thefluctuation of the CO2 concentration in the layers above the highest altitude that the aircraft
can reach must also be evaluated. So far, the findings from balloon-borne observation forthe stratosphere have revealed that the fluctuation of the column abundance is around 0.5％,
very roughly. More details will be investigated in continued studies to follow.
(ii) Data acquisition by balloons
Balloon-borne observation data, including the past data, must be obtained in order toascertain the characteristics of the variation of the CO2 column abundance in the layers
above the upper limit of air-borne observation.
c) Others(i) Observation by small balloons
The CO2 sonde to be installed on board a small-sized balloon, which is being developed,
may be used for the validation after evaluating the measurement accuracy, data quality andoperational stability and ensuring that it is practical in terms of cost, etc.
(ii) Observation by compact spectrometer
Fabry-Perot Etalons based spectrometer, currently being developed, may be used for thevalidation after evaluating the measurement accuracy, data quality and operational stability
and ensuring that it is practical in terms of cost, etc.
B-18

(2) Validation of CO2 and CH4 products by TIR observation
The data acquired for validating L2 SWIR CO2 and CH4 will also be used for validating L2 TIR CO 2
and CH4 products. Additionally, the validation will be more effective if combined with airtemperature, air pressure, humidity profile, and ground emissivity data. As for nighttime
observation, the data already acquired will be utilized as far as possible. The validation plan will
be further detailed in consultation with the parties responsible for TIR.
In addition, since air temperature and H2O and O3 concentration profile by TIR are products to begenerated internally by JAXA for the research purpose, JAXA will carry out necessary evaluations
on data quality.
(3) Comparison in the global distribution of CO2 and CH4 column abundances based on SWIR
observation
L3 products do not require special evaluation since they are derived by temporal and spatial
averaging of the validated L2 products.
(4) Comparison of CO2 flux
The global distribution of L4A CO2 flux and L4B CO2 concentration are positioned as subject tocomparison due to the following reasons, and it is preferred that these are evaluated as part ofresearch activities, including RA, instead of validating in the Project.
・L4A and L4B are both calculated by models.
・The reliability of the transport models to be used is not firmly established and is still underdiscussion among scientists. Ensemble averaging among multiple models will enable a
comparative evaluation.
・Flux observation is intended for the research purpose, and it requires substantial efforts to obtainthe necessary data.
・The comparison of the flux observation with models requires scaling-up, which is a process stillat the research level.
・The sea flux varies with the exchange coefficient.
L4A products provide an estimation of the monthly average of CO2 flux in each of the 64 regions
into which the globe has been divided.
As for comparison of L4A products with other observation data, data acquired by vessels are
effective since the flux in sea area (latitude 5° × longitude 5°) is regarded as almost uniform.
On the contrary, the flux in land area is calculated based on the mesh size of latitude 1°× longitude
1° in the calculation process, in assumption of the flux distribution patterns within the regions. On
the other hand, the ground-based observation of CO2 flux is made on the “point” basis, with asignificant regional difference and the smallness in number and spatial unevenness of theobservation points, and thus it is difficult to use these points directly for the validation of L4A
products. In order to solve this problem, a scaling-up method using land-area ecosystem models
plays a vital role. Many countries, including Japan, have been engaged in research on such
scaling-up methods and further progress in this effort is expected to be seen in the near future.

B-19

As for research on CO2, the following comparisons can be conducted.
a) Use of data on the CO2 flux between the air and the ocean
The CO2 flux between the air and the ocean is calculated by measuring the partial pressuresof CO2 in the surface sea water and in the air and applying the exchange coefficient. The
exchange coefficient depends on the wind velocity, temperature of water at the sea surface andsalinity.
b) Use of the observation data acquired at flux monitoring sites
There are several flux monitoring sites in Hokkaido, and although there is a problem in theunevenness in topography and vegetation, the data obtained there by utilizing the land-area
ecosystem model may possibly be used in comparison with the flux (L4A) in GOSAT global
carbon balance estimation model.
(5) Comparison of the global distribution of CO2 concentration
L4B products to be offered are a monthly average of 3D concentration distribution obtained asmesh data of latitude 1°× longitude 1°. The comparison of the L4B products with otherobservation data is made on the one-hour value or monthly average value of the latitude1°×
longitude1° grid, which is the base data of the L4B products. As for the reference data, thedata used as the reference in the L2 validation (column abundances acquired by theground-based FTSs and concentration profile acquired by air-borne observation) and dataacquired by several other kinds of ground and marine observations can be generally andcomprehensively utilized.
(6) Comparison with other satellite data
Comparison with CO2 and CH4 column abundance data acquired by other satellites, such as the OCO,
is instrumental for evaluation of the data quality of GOSAT products.
a) OCOOCO is scheduled to be launched in December 2008 by NASA/JPL (Jet Propulsion
Laboratory). It will observe the CO2 column abundance in short wavelength-infrared bands,
as with GOSAT. The satellite will fly at the forefront of the so-called A-train constellation of
the U.S. EOS program and it can obtain necessary physical quantities, such as aerosols, fromthe other member satellites. Therefore, it focuses only on observation of the CO2 (and O2)
column abundance.
OCO’s spectrometer is a grating type and the observation bands are 1.61, 2.06 and 0.76 μm,
which almost coincide with those of GOSAT’s.
In the future, it is necessary to scrutinize the possibility of concluding a cooperative agreement
or framework between the two missions and examine detailed methods of data acquisition andother procedures.
b) SCIAMACHYSCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY)
B-20

is an on-board sensor of the ENVISAT of the ESA. Its primary purpose is to observe theatmospheric trace components necessary for chemistry in the troposphere and the stratosphere.
The spectrometer is a grating type with eight channels sensitive to ultraviolet, visible, andshort wavelength infrared bands. The observation modes range from nadir, solar/lunar
occultation, and limb scattering. It observes both the concentration profiles and column
abundances of O3, CO, CH4, etc.
In addition, as the derivation of CO2 is also being attempted, it may serve as effective
reference for comparison with the GOSAT data.
Even other satellites that are not originally intended to measure greenhouse gases, such as
AIRS and IASI, can also be used as referential data sources.
B-2.4 Validation of parameters related to error factors in SWIR observation(1) Aerosol, thin cirrus cloud
In order to evaluate the appropriateness of the L2 processing algorithm for TANSO-FTS (SWIR)
data, the parameters related to error factors, namely the optical characteristics of aerosols, such as
optical thickness, wavelength index, and single scattering albedo, and the vertical distribution, areextracted via the networks referred to hereunder or otherwise, in addition to the validation of data
quality. It must be noted, however, that it takes a certain lead time for the latest data to be uploadedon the Website, and hence in case that it is urgent to obtain the data, a request for data acquisition
must be made directly to the observer.
Furthermore, it is necessary to install sky radiometers and lidars at those locations where
ground-based FTS or air-borne observation is carried out. The lidar can measure the vertical
distribution of aerosols and thin cirrus clouds with an accuracy of approximately 15 m, anddistinguish between sphericity and non-sphericity based on the extinction of polarization, thereby
identifying whether the substance is rain cloud, cirrus cloud, or sand dust. The sky radiometermeasures the direct solar irradiance and forward scattering in the sky. The measurements can also
be used for estimating not only the optical thickness, particle size distribution, and complex
refractive index, but also the single scattering albedo and phase function. These data are useful tothe validation of TANSO-CAI products.
(2) Ground surface reflectance
The accuracy of the ground surface reflectance retrieved from SWIR data will be confirmed with theuse of the following data. The observation points for this purpose must be selected where the land
surface is homogeneous.
a) MODISMODIS (Moderate Resolution Imaging Spectroradiometer) is an optical sensor on board Terra
and Aqua, NASA’s earth observing satellites. It observes a wavelength range of 0.4 to 14 μm
in 36 bands with a 2,330 km swath and spatial resolutions of 250 to 1,000 m. The Science
Team at NASA developed the processing algorithms and each data center is generating
various products including the ground surface reflectance.

B-21b) ASTER
ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) is an opticalsensor on board Terra, NASA’s earth observing satellite. It consists of the Visible
Near-Infrared Radiometer (VNIR), Short-Wave Infrared Radiometer (SWIR) and Thermal
Infrared Radiometer (TIR). It observes a wavelength range of 0.52 to 11.65 μm in 14 bands
with a 60-km swath and spatial resolutions of 15 to 90 m. The standard products generated
by the Japan side include the ground surface reflectance, land surface temperature, land
surface emittance and DEM.
(3) Oxygen column abundance
The oxyten column abundance, obtained in Band 1(O2 A band in 0.76 μm), will be validated.
a) Ground air pressure and rawinsonde data
The validation will be performed using the data routinely taken at observation points for theupper atmosphere together with new observation data to be acquired as necessary. The
rawinsonde data will also be used for addional other validations.
b) Meteorogical data reanalysis
Since the observation points for thte upper atmosphere are not evenly distributed among theoceans, developing countries and polar areas, meteorogical reanalysis results are also used in
the validation.

B-2.5 Validation of CAI Products
TANSO-CAI is a supplementary sensor to TANSO-FTS. In other words, the CAI products(concerning parameters related to error factors) that relates to the validation of TANSO-FTS L2
products should be validated with a priority because these CAI data are necessary for generating
TANSO-FTS L2 products. On the other hand, the validation of the other CAI products is rated at alower priority, while, in the meantime, the validation of cloud mask products is quite essential.
CAI L1B products are generated by NIES, and the evaluation of the position, band-to-bandregistration, radiance, etc. is extremely important.
Since the CAI products and validation data of parameters related to error factors in short wavelength
infrared are common in part, the detailed validation plan will be determined in coordination with theparties in charge of CAI.
B-2.6 Campaign Observation
Air-borne observation by the direct observation instrument is important to be performed
simultaneously with observation by the remote sensing instruments, such as FTS, lidar or otherwise,in order to confirm the accuracy of TANSO-FTS measurements.
The evaluation will be performed as to whether temporal variation of the accuracy of each
instrument is ascertained, by acquiring high-accuracy CO2 concentration profile data by the aircraft
which will ascend to a high altitude in synchronization with the satellite and calculating the column
B-22

abundance obtained therefrom. The CO2 concentration above the highest altitude where the aircraft
can reach varies with the altitude of the tropopause. Therefore, it is crucial to calculate the altitudeof the tropopause correctly. To this end, interpolation of simultaneous observation data taken by
the rawinsonde or meteorological reanalysis data is performed. It is also needed to measure theCO2 concentration at the ground level, because the aircraft cannot measure the concentration
between the land surface and the lowest altitude which the aircraft can observe.
Moreover, it is considered as effective to simultaneously carry out direct observation by aircraft on
aerosols, which act as error factors.

Figure B-2-1 Concept of the validation observation for CO2 & CH4 column abundancesB-23

B-2.7 Validation Schedule
Table B-2-1 lists the validation schedule based on the assumption that GOSAT will be launched
January 2009.
This schedule is a plan for preparing the validation activities and securing the necessary data, as of
the issuance of this document.
Table B-2-1 Validation Schedule
First half of2007
4Second half ofFirst half ofSecond half ofFirst half of
2007
2008
2008
2009
9 10
34
9 10
34
9

Survey and plan for validation
Calibration of ground-based high-resolution FTS
Evaluation of the validation data and acquisition
Planning of validation analysis methods
Evaluation of ＪＡＬ project data
Construction of validation data processing and acquisition system
Examination of validation data processing and acquisition system
Preparation of observation for validation
LaunchExamination of validation processing
system using GOSAT data（3 months）
Initial validation（ 3 months）
Substantial validation
Field observation for algorithm
verification before the launch

B-24

Field campaign observation

B-3 Overview of Processing Algorithms
This document describes processing flow and basic concepts of processing algorithms for
GOSAT/TANSO data
B-3.1 Overview of the overall processing system
Figure B-3-1 shows an overview of the overall data processing flow together with the data level ofthe product produced at each step. More details of the processing levels (L1, L1A to L4B) of the
GOSAT/TANSO data products are given in Table 1 of the RA Document.

L1A

L1B

L1→L2

L4B

L4A
L2
↓
L3

Figure B-3-1 Data processing flow and data product levels
The data measured with the TANSO-FTS and TANSO-CAI sensors are processed to several kinds ofproducts according to the flow given in Fig. B-3-1. Interferograms (FTS L1A), observed values by
FTS, are converted into spectra (FTS L1B), while the CAI L1 data are processed to cloud andaerosol properties (CAI L2). These data, at observation points under almost clear sky conditions,are combined to estimate the column abundances of CO2 and CH4 (FTS L2), and further to draw a
global concentration map (FTS L3), and still further to estimate global carbon flux (emission andabsorption ) distribution (L4A) and three dimensional (3D) distribution of CO2 (L4B) after analyses
utilizing the atmospheric transport model.
The following sections of this Appendix outline the data processing steps and algorithms for the FTS
and CAI data, focusing on L1 and L2 processing.
B-3.2 Level 1 processing of the TANSO-FTS data
(1) L1A data (interferogram)
B-25

L1A data consist of the interferograms of both observation data (the scattered light byearth-atmosphere system) and calibration data (the solar irradiation from outer space using thestandard solar diffuser of the TANSO sensor), time data, mission telemetry data (temperatureinside the instrument, pointing angle of the tracking mechanism, etc.), scale-conversion-relateddata, and so on. The L1A data, however, are regarded as “internal products”, and will not bereleased to general users, but a part of them will be provided for the RA Investigators who
research on themes in the field of calibration/validation or development of algorithms.
(2) L1B data (spectrum)L1B data consist of basic data (number of datasets, gain value, scanning direction, etc.), spectraof observation data (voltage value per wave number [V/cm-1]) for the short wavelength infrared“SWIR” bands, and spectral radiance [W/cm2/cm-1/str] for the thermal-infrared (“TIR”) band),calibration data, instrument function, time data, mission telemetry data, scale-conversion
coefficients, quality data, geometric information data (the latitude/longitude of the center of theobservation point), and so on. Further, the calibration data on SWIR sensitivity will be
provided.
(3) Overview of L1A to L1B processing
The data of the SWIR bands (Bands 1, 2, 3) are processed in the following steps.
・Engineering value conversion. (Conversion from discrete-valued digital numbers to voltage
values)・Spiky noises are detected and corrected. Basically, no non-linear correction is performed on the
SWIR band data, though the correction function itself is equipped.
・The zero path difference (ZPD) position in the above interferogram is detected.
・Fourier-transformed.
・A flag is generated by confirming the low-frequency components in the spectrum. (A flag is set
when the spectrum contains large values around the low-frequency region, to indicate that the
FOV fluctuated and/or small vibration occurred during the scanning.)
・(In case that a flag is set) the interferogram is corrected by removing the low-frequency
components of spectrum.
・By applying a given window function to decrease spectral resolution, the data are complex
Fast-Fourier transformed (FFT) every 0.2 cm-1. The phase angle is calculated from the ratio
between real and imaginary parts after the transformation, while the phase information isderived from the transformed values.
・The interferogram is now zero-filled to yield 76,545 (= 37×5×7) data points (after correction of
the low-frequency components), complex Fourier-transformed without phase correction, androtated by the phase angle derived from the above to produce the phase-corrected spectra(spectra of the real part). Note that the L1B data to be distributed will contain both real andimaginary parts.
The data of the TIR band (Band 4) are processed in the following steps. However, these steps
represent just a method, and are subject to change in the future.
・Engineering value conversion. (Conversion from discrete-valued digital numbers to voltage
B-26

values)・Spiky noises are detected and corrected, followed by non-linear correction after subtracting
direct current (DC) components.
・The zero path difference (ZPD) position in the above interferogram is detected, FFT shifted.
・(Similarly to the SWIR data) the necessity for correction for the low-frequency components
due to FOV fluctuation is checked from their spectrum without phase correction, and thecorrection is made if necessary.
・After the phase correction, the number of data points in the interferogram is adjusted to 38,400(= 2９ ×3×52) through zero-filling, and the data are Fourier-transformed. Furthermore, byusing the deep-space and blackbody observation data in the TIR band, observation spectral
data (the real part data after Fourier-transformation) are converted into spectral radiance(W/cm2/cm-1/str).
As for Bands 1, 2, 3, if the interferogram exhibits a DN value of 65,400 or larger at the ZPD position,
the data are regarded as saturated and a quality flag is set. As for Band 4, if the digital DN at the
ZPD position is 65,400 or larger or 136 or smaller, the interferogram is regarded as saturated and aquality flag is set.
Moreover, the wavelength calibration is performed for the TANSO-FTS, due consideration to (i) the
oscillation wavelength (1310 nm (nominal)) of the semiconductor laser varies with the temperature,and (ii) the alignment may be affected by the temperature variation in orbit.
B-3.3 Level 1 processing of the TANSO-CAI data
TANSO-CAI has four observation bands: Bands 1-3 have a wavelength width of 20 nm, spatial
resolution of 0.5 km, and 2,000 elements, and Band 4 (the center wavelength is 1.60μm) has awavelength width of 90 nm, spatial resolution of 1.5 km, and 500 elements.
L1A data are provided with information necessary for radiometric correction and geometriccorrection.
L1A data ,however, are classified as internal products of the Project, and will not be released togeneral users. However, some of them will be provided for the RA Investigators who carry out
research on calibration/validation or development of algorithms.
(1) Radiometric correction
L1A digital data are converted into radiance values (W/m2/μm/str), using scale conversion
coefficients. Since the detectors are of diode type, it is, in principle, not necessary to perform
non-linear correction of the output values. In addition, the gain setting for the CAI observation
bands will be adjusted, based on the gain switch table to be generated on the monthly basis, so
as to avoid saturation of output signals.
(2) Geometric correction
Information on the relative scales (expansion, compression and rotation) for datasets of different
B-27

bands makes it possible to determine the band-to-band registration. By making Band 3 thereference, other bands’ data are expressed relatively as the displacement from the reference.
The number of center pixels for Bands 1-3 is 1,024, and that for Band 4, 256, and the
band-to-band registration error after correction is targeted at 0.2 pixel or lower. In addition, L1
data are supplemented with the latitude and longitude information of the representative
elements.
(3) L1B data
TANSO-CAI L1 data are supplemented with the above-mentioned information necessary forcorrection before release. L1B data are generated as standard products with the observation
positions corrected, after scale conversion based on the information, band-to-band registration,and ortho-rectification per pixel based on the elevation information (Digital Elevation Model:
DEM).
(4) L1B+ data
CAI L1B data are then projected on a map, on a certain scene scale, and their stored radiancedata per pixel/band are resampled to generate L1B+ image data (standard products) for release.
B-3.4 Level 2 processing of the TANSO-CAI data
The CAI data are processed to standard products which provide cloud flag information.
Research products, which provide parameters of cloud and aerosol characteristics, are derived
from these products.
First of all, optically thick clouds on the sea surface and on relatively dark land surfaces are
discriminated by using two visible bands of the CAI sensor. If they are found to be clouds,
specifically water clouds, cloud characteristics are analyzed. The cloud characteristics to be
estimated are the cloud optical thickness (at a wavelength of 0.5µm) c, which is a non-dimensional
value that represents the optical thickness of clouds, and the effective radius of a cloud particle e.
Table B-3.4-1 lists up the cloud products to be derived from the CAI data.

B-28

Table B-3.4-1 Cloud products derived from the CAI sensor
Estimated parameter
Cloud flag

Unit

Band
No.

Remarks-

2, 3, 4*2, 4

Optically thick clouds on the sea
surface and relatively dark land
surface
Water clouds and ice clouds

2, 4Water clouds and ice clouds

Cloud optical
thickness (@0.5µm)
Effective radius ofµm
cloud particles
Vertically integrated
g/m2
total amount of cloudwater* Band 4 will be used for studies.

2, 4Secondly, aerosol characteristics are analyzed by combining data from all CAI observation bands
with ancillary data, including meteorological data. There are various types of aerosols existing on
the globe; the primary aerosols include anthropogenic aerosols, such as sulfates, black carbons, andorganic carbons, and dust aerosols and sea salt aerosols. In estimating the aerosol characteristics, it
is assumed that anthropogenic aerosols are composed of internal mixing of sulfates and black
carbons, and the three types of aerosols, i.e. anthropogenic, dust, and sea salt aerosols, are mixed
externally to constitute a particle system. Based on these assumptions, the optical thickness of eachtype of aerosol (at a wavelength of 550nm), τa, and the component ratio (volumetric ratio) of black
carbon to anthropogenic aerosols, γsoot will be estimated. These are summarized in Table B-3, 4-2.

Table B-3.4-2 Aerosol parameters estimated from the CAI data
Estimated parameters
Anthropogenic aerosol mode
Aerosol optical thickness
Dust aerosol mode
(Wavelength: 550nm)
Sea-salt aerosol mode(over the sea only)*
Volumetric ratio of black carbons in the anthropogenic aerosol mode

τacm
τdst
τslt
vsoot
*Except sunglint region

The following provides an outline of individual processing algorithms.
(1) Cloud flag algorithm
Multi-wavelength imagers, such as MODIS and GLI, have water vapor channels and infrared split
windows to discriminate altostratus and cirrus, in addition to many visible channels, and thus canaccurately discriminate clouds as a single sensor. In contrast, the CAI sensor has only four bands
with a wavelength range of near-ultraviolet to short wavelength infrared, making its discrimination
accuracy potentially inferior to MODIS or GLI. For this reason, users of cloud flags arerecommended to appropriately select and employ the several types of cloud discrimination test
B-29

results provided by CAI and the cloud flags provided separately by FTS observations and others.
Of the CAI observation bands, Band 2 (0.670µm) and Band 3 (0.865µm) are the primary bands fordiscriminating clouds. Although it is possible to use the short wavelength infrared 1.6µm band
(Band 4) to discriminate between ice clouds and water clouds in the presence of optically thickclouds, this is currently at the examination stage. Two types of threshold-value tests are currently
planned using the cloud flag algorithm: tests to examine the reflectances in visible bands 2 and 3 anda color test for the ratio of the reflectances between these two bands. The former is adiscrimination method that utilizes the fact that the reflectance on clouds is higher than that on thesea surface or on relatively dark land surfaces, while the latter stands on the fact that the reflectanceson clouds in Bands 2 and 3 are almost equal.
The CAI cloud flag algorithm will be used to conduct the four tests listed in Table B-3.4-3 below,
and the test results will be stored as data.

Test No.
Test 1
Test 2
Test 3
Test 4

Table B-3.4-3 CAI cloud flag tests (Draft)Contents to be implemented
R[B2] > (R2+A[B2])R[B3] > (R3+A[B3])0.9 < (R[B2]/R[B3]) < 1.1
If “true” on Test 1 and Test 3, examine R[B4]/R[B2].

Here, R[B2], R[B3], and R[B4] represent the reflectance in CAI Bands 2, 3, and 4Note 1), respectively;
A[B2] and A[B3] represent the ground-surface albedo in Bands 2 and 3; and R2 and R3 are themargin values of the ground-surface albedo. Tests 1 to 3 diagnose the possibility that object pixels
are clouds. The results of Test 4 provide information conducive to discriminating between ice
clouds and water clouds, on an examination basis. The accuracy of cloud flag achieved by the CAI
sensor alone may be subject to the following limitations: Optically thin clouds, particularly cirrus,
cannot be completely identified by this method; and discrimination of clouds by CAI is limited onbright ground surfaces, such as surfaces covered with snow and ice or desert, and in regions where
the ground surface state changes rapidly with time.
Note 1) The reflectance is defined as L /(F0 cos  0 ) here. L is observed radiance, F0 is solar irradiance basedon the instrument function, and  0 is the solar zenith angle.

(2) Cloud characteristicsalgorithmThe principle for obtaining
optical thickness of the cloud and effective radius of cloud particles are
as follows. Light is scarcely absorbed in CAI Band 2 (0.670µm) even if multiple scattering by
cloud particles occurs since the imaginary part of the complex index of refraction of water droplets isvery small and is approx. 10-8. Therefore, the light injected into the cloud layer repeats scattering
due to cloud particles and ultimately the light that scatters toward the satellite from the top of thecloud is observed. A greater amount of light is observed in CAI Band 2 as the cloud optical
thickness increases, augmenting the light scattered upward from the top of the cloud. The
probability of upward scattering as a result of multiple scattering will reach the upper limit when the
optical thickness increases to around 70, and the observed radiance will be saturated in the meantime.
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On the other hand, CAI Band 4 (1.6µm) is used to estimate the effective radius of cloud particles.
The imaginary part of the complex index of refraction of water droplets in this wavelength range isover 10-5, which is one thousand times greater than that in visible light. Therefore, the shortwavelength infrared light that enters the cloud layer is absorbed during the multiple scattering due tocloud particles. The light observed by the CAI Band 4 gets weaker, when cloud particles becomelarger and therefore absorb light more strongly. The absorption in the cloud layer becomes
gradually saturated when the effective radii of cloud particles increase to around 30µm, which is theupper limit.
In remote sensing of cloud characteristics, the optical thickness and effective radius of particles
of clouds are calculated from observed radiances in visible and infrared bands. The radiances
directly observed include radiative components that are essentially unnecessary for estimation ofclouds characteristics, such as the ground surface reflection components and thermal radiation
components from the cloud layer. These components are therefore removed in calculation byusing the transmittance of the atmosphere and cloud, satellite zenith angle, solar zenith angle,
relative azimuth angle, etc. Estimations of the physical values using satellite observations areaccomplished by comparing the simulated radiance obtained by using the radiative transfer code
(Nakajima and Tanaka, 1986, 1988) and the observed radiances by CAI. A database (lookup table,
LUT) table of angle dependent radiance, transmittance of atmosphere and cloud, and sphericalalbedo has been prepared in advance for various optical thickness of cloud and the effective radius ofcloud particles, and it is used in this algorithm which performs iterative calculations using the
Newtonian method.
(3) Aerosol characteristics algorithm
CAI has observation bands at 670 nm and 870 nm, which are sensitive to aerosol scattering and often
utilized for remote sensing of aerosols, as well as the band at 380 nm, which is insensitive toreflection from the ground surface and sensitive to absorptive aerosol, and the band at 1.6 μm, which
is sensitive to large-particle aerosols. The aerosol characteristic parameters will be estimated by the
maximum a posteriori (MAP) method, calculating the satellite signals of these four bands
theoretically.
In addition, as the estimation of aerosol parameters based on the CAI data is tightly restricted,
aerosol information on the locations which are not covered by CAI estimation will be obtained fromthe estimation results based on the aerosol transport model being developed by the GOSAT Project.
This model is a 3D dynamic simulator for aerosols developed for the GOSAT Project, having
Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) as its core.
B-3.5 Level 2 processing of the TANSO-FTS-SWIR data
In the three FTS SWIR bands, the sensor observes signals on the two polarization planes, which areperpendicular to each other, in each band. That is, it acquires data (interferogram) for six bands, intotal. The FTS SWIR data, combined with various ancillary data, are used to calculate the column
abundances of CO2 and CH4 within target accuracies (i.e. relative errors of 1% for CO2 and 2% forB-31

CH4 in a certain period). The retrieved column abundance [molecules/cm2] is converted to XCO2
[v/v] (volumetric mixing ratio), which represents the ratio of CO2 in the atmosphere with no water
vapor, or a relative humidity of 0%, (dry atmosphere). In many cases it is this converted quantitythat is used for application. This conversion requires ground-surface pressure information, thus thecolumn abundance of oxygen (or ground-surface pressure) derived from FTS Band 1 data and theground-surface pressure obtained from objective analysis data at the observation time and location
are used.
Figure B-3-2 on the next page depicts the data processing flow for generating L2 column abundance
products from L1B (spectra) data of FTS SWIR observation.

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FTS SWIR data
(L1B)
DEM
(GTOPO30)
CAI data (L1A)

Pre-processing

Pre-processing of
meteorological data
CAI L1B processing

CAI L1B
Intermediate file

Pre-processing of thecalculation results from theatmospheric transport modelCalculation results

Calculation by theatmospheric transport
model

CAI L2 cloud
discrimination

CAI L2cloud flag

Meteorological data
(GPV)

FilteringCalculation results

CAI L2 processing

Calculation by theaerosol transport model(SPRINTARS)(Aerosol characteristics)

CAI L2aerosolparameters

Setting of initial and a priori
values

MODIS
Ground-surface
reflectance data

Calculation of gaseousconcentrations

Product generation

FTS SWIR data
(L2)

Figure B-3-2 Data processing flow from TNASO-FTS-SWIR L1B to L2 products
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(1) Use of polarization information
The FTS SWIR observation can measure polarization signals on two perpendicular axes, which
are called the P (parallel) axis and the S (senkrecht—a German word for “perpendicular”) axis.
The P and S observation data are planned to be applied in the data processing flow, by means of
the following three approaches. “Approach 1” will be adopted in the initial version of theprocessing algorithm. A series of simulations have found that the degree of polarization is low
in observation of reflection from the earth surface, whereas it is high in sunglint observation
over the sea surface.
【Approach 1】 The two polarization components of the observed light are compounded toprovide total intensities. In this approach, the scalar radiative transfer code, which does
not take the polarization state into consideration, will be used in the theoretical calculation.
【Approach 2】 The radiance spectra of the two polarization components of the observed
light are used independently. In this approach, the vector radiative transfer model (Pstar2bcode), developed by the GOSAT Project, will be used in the theoretical calculation. The
Pstar2b code runs at a speed a few times slower than conventional scalar radiative transfer
codes, however, it is a code which runs much faster than any other polarization calculation
code, after being improved for fast running.
【Approach 3】 The radiance spectra of the two polarization components of the observed
light are approximated by total intensities. In this approach, it is assumed thatpolarization-related parameters, such as ground-surface albedo, scattering phase matrix,
etc., are different between the two polarization components, whereas the other parameters,
including the optical thickness and gaseous concentration, are identical. Based on theseassumptions, the radiative transfer is calculated in an approximate manner, using a scalarradiative transfer code per polarization axis.
(2) Retrieval algorithm
In the processing algorithm to estimate column abundances from L1B to L2, the first calculation
is for the vertical distribution of the volumetric mixing ratio of the gas of interest as anintermediate parameter, in preparation for achieving the column abundances of CO2 and CH4 asfinal outputs. The information necessary for this retrieval will be supplied by objective
analysis data for the meteorological parameters, such as the vertical distributions of air
temperature and pressure, measured at the same location at the same time as the GOSAT
observation. In addition, absorption bands in the FTS SWIR bands do not have such radiancespectral signals that have a weighting function structure which is steep enough in the altitude
direction to require vertical distribution. Therefore, the intermediate product, the vertical
distribution of the volumetric mixing ratio, may have an extremely unstable structure.
Nevertheless, the column abundance can be stably obtained. The reason is, though the
explanation is not rigorous as it disregards the impact of multiple scattering or pa th radiances,that the size of the area obtained by integrating the absorption depth of the absorption line
structure within the radiance spectrum observed is deeply linked with the amount of column
abundance.
The L1B to L2 retrieval algorithm is based on a constrained Gauss-Newton method of a
B-34

non-linear least squares method to the observed radiance spectra. In other words, the solutions
are obtained stably from the observation information together with a priori information. The
solutions to be derived consecutively are expressed as the following equation.

x i1  xi  K iT S1K i  S a1  KiT S1 y  F xi   S a1  xi  x a 1B-3-(1)

Where x i is the vector of the parameter to be estimated at the ith time, x a is its a priori
information (the a priori value which can be used as the initial guess at the beginning of
estimation), S  is the error variance-covariance matrix (no. of wavenumber points × no. ofwavenumber points) of the observed spectra, S a is the variance-covariance matrix (no. ofparameters to be estimated × no. of parameters to be estimated) of the a priori information, y is
the vector of observed spectrum (no. of wavenumber points), F xi  is the simulated spectrum
theoretically calculated using a radiative transfer code when the variable is x i , and K i is theJacobian matrix (no. of wavenumber points × no. of parameters to be estimated), which
expresses the divergence of spectra against the unit change of the target parameter. Note also thatK  y x here.
Furthermore, Equation B-3-(1) above corresponds to an adoption of such a solution that gives
the minimum value for the cost function given as below (Equation B-3-(2)). It is called theMaximum A Posteriori (MAP) estimation method.

Jx   y  F x S1 y  Fx  x  x a  S a1 x  x a   min.
T



T

B-3-(2)The values of elements of S  will be determined based on the characteristics of the sensor (e.g.
SNR). The values of the diagonal elements of S a are given by a priori information regarding
how freely the target parameters can vary with altitudes, while those of the non-diagonal
elements are given by the information regarding the inter-constraints of variation among thetarget parameters (e.g., correlation between vertical distributions of each gaseous species).
Basically, the molecular spectroscopic parameters provided by the HITRAN 2004 database will
be used for calculating theoretical spectra in B-3-(2), except CO2 of Bands 2 and 3 and partly
CH4 of the 1.67-m band, for which the 2008 version of line parameters and data generated in
the GOSAT Project will be used, respectively. Moreover, when performing spectral-fitting in
B-3-(2), channel selection will be performed in order to eliminate the impact of Fraunhofer lines
in the solar atmosphere and of water vapor in the earth atmosphere. The information on solar
Fraunhofer lines in the 1.6-m and 2.0-m bands will be supplied by the data generated by Dr. R.
L. Kurucz, Harvard-Smithonian Observatory in the U.S., in response to the request from the
GOSAT Project.
Research and development of this algorithm have been promoted using the radiative transfer
code improved based on HSTAR code, an extended RSTAR code developed by Professor
Teruyuki Nakajima and his research group at Center for Climate System Research, University ofTokyo, which can calculate the effects of (multiple) scattering and attenuation by atmospheric
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molecules, aerosol, and clouds particles present on the optical paths. With the help of this code,
the column abundance can be estimated at an expected precision even in the presence of
optically-thin clouds and aerosols, as demonstrated in numerical simulations. Consequently,
the “filtering” process in the processing flow provided in Figure B-3-2 is the process carried out by
the program to determine whether or not the observation data under processing is a clear-sky
scene or a scene with optically-thin clouds or aerosols. The data processed in this way thenundergo L2 processing.
In addition, the ground-surface albedo is also added as a target parameter and retrieved
simultaneously by means of Equation B-3-(1). With this processing, it is possible to flexibly
estimate the column abundances of CO2 and CH4 for any spectrum structure of theground-surface albedo.
(3) Fast retrieval algorithm
In order to realize the above-mentioned estimation algorithm, a new data processing system which
combines the discrete ordinate method as a radiative transfer and the MAP method, is being
developed. Additionally, the Project is promoting the research and development on a fast andhigh-accuracy technique called the Photon Path-length Probability Density Function (PPDF) method,which is based on a simple radiation model by combining the Monte Carlo method and theEquivalence theorem and retrieves PPDF parameters simultaneously with other unknown quantities,
such as CO2, from the observed spectra, as a future version of GOSAT operational processing
algorithm.
B-3.6 Level 2 processing of the TANSO-FTS (thermal-infrared band) data
Data from thermal-infrared sensors on board spacecraft platforms have been used mainly for
analyzing CO2 concentrations. With this technique, however, it is difficult to detect significant
change in the CO2 concentration in the boundary layer, such as decrease due to absorption byvegetation or increase due to artificial CO2 sources, although the changes in concentration in the
middle and upper layers of the troposphere can be analyzed with a high accuracy. This is because
the difference between the atmospheric temperature at the altitude where concentration changes
occur and the equivalent radiance temperature of the background radiation is smaller in the lower
layers of the atmosphere, making it more difficult to detect.
For FTS thermal-infrared observation (referred to as “TANSO-FTS-TIR” hereafter), it is expectedthat the vertical profile of CO2 concentrations down to altitudes of 700 to 750 hPa from the upper
atmosphere can be obtained under clear-sky conditions, though it depends on the vertical profile ofatmospheric temperatures. Figure B-3-3 plots a typical vertical resolution function (Averaging
Kernel) in medium latitudes.
(1) Vertical profile of CO2 concentrations
Independent temperature information is required for deriving the concentrations of CO2 from thethermal-infrared band data. The TANSO-FTS TIR observation uses objective analysis data because
there are no simultaneously-operated microwave sensors.
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Figure B-3-3 Averaging kernel of CO2 analysis based on the TANSO-FTS-TIR data -Typicalexample in mid- latitudes (Saitoh et al., 2006)
For retrieval analysis of CO2 concentrations, a physical analysis technique, more precisely the
Maximum A Posteriori (MAP) method as in Eq.B-3-(2), will be employed, similarly to FTS SWIR
observation.
The results obtained by statistically processing the output of the CO2 transport model will be used as
the initial guess values and covariance values of the CO2 concentration. Since the Jacobian matrix
is a function of vertical profiles of atmospheric temperatures and CO2 concentrations, the optimal
Jacobian matrix is calculated for each region, season, and local time from the vertical profiles ofatmospheric temperature and CO2 concentration from the objective analysis and theabove-mentioned initial guess value, respectively.
(2) CO2 column abundance
The radiance spectrum of the CO2 hot band close to 10 μm is utilized for this analysis; namely, thefact is used that the decrease in radiance depends on the column abundance of CO2 with thebackground of a warm ground surface or sea surface as in Solar Occultation.
First, the Jacobian matrix in the 10 μm band of CO2 is calculated based on the vertical profiles ofatmospheric temperatures and volumes of water vapor in each FTS FOV that was analyzed using theobjective analysis value as the initial guess value. To reduce errors in estimating the volume ofwater vapor, baseline compensation is performed such that the systemic gap between the simulated
spectrum and the observed spectrum is compensated using a wavelength range with a small volumeof absorption by gases. The column abundance of CO2 is derived by applying the Jacobian matrix
B-37

to the difference between the simulated spectrum and the observed spectrum after this baseline
compensation. The wavelength dependence of the ground-surface emissivity is required tosimulate the radiance spectrum; the results estimated from database for the ground-surface
emissivity and the meteorological data obtained before and after the observation are utilized for suchdata. For the concentration of ozone, which influences this wavelength band, data of other satellite
sensors such as OMI, are utilized.
(3) Cloud screening
The fractional cloud coverage in the FTS FOV is estimated based on the analysis results of the CAI
sensor during the daytime. However, the analysis results of the FTS TIR data are also usedconcurrently because thermal infrared radiation spectra are superior in detecting thin ice clouds suchas cirrus clouds. During the nighttime, however, clouds are discriminated solely by the FTS TIRband because the CAI sensor is not operated.
There are three methods for analyzing clouds using the FTS thermal-infrared data.
1. Slicing method
2. Threshold value method
3. Split window method
The slicing method estimates the altitude at the top of cumulus clouds by applying the method ofMenzel et al. (1983), which uses the 15 μm band of CO2. The threshold value method, the second
item above, discriminates clouds based on the difference between the value provided by thesea-surface temperature database, storing the analyzed results of data from the AVHRR on NOAA
satellite and/or MODIS, and the sea-surface temperature estimated from the FTS TIR data with theassumption of no cloud. Although this method is superior over the sea, these data will be used only
as references for land areas due to a large error in the ground-surface temperature of objective
analysis data. The split window method, listed last, performs an analysis using the combination ofdata of two wavelength bands, 8 to 10 μm and 11 to 13 μm, which enables to judge cloud altitudes
and distinguish between ice and water particles in the areas that are identified as cloudy.
B-3.7 Level 3 processing and more advanced (higher-level) processing
L3 product is generated as a global map, by using the column abundances of CO2 and CH4 (L2)
calculated from the TANSO-FTS-SWIR data after applying temporal/spatial interpolation andappropriate statistical processes to the GOSAT observation data for fixed sections (e.g. 2.5° meshes(TBD)) during a given period of time, to calculate the value for each section. Similarly, TIR L3
product is planned as a global map, but the details are yet to be determined.
L3 processing of the CAI data is also planned, though very roughly, to provide standard products on
global radiance maps (with cloud cover), global radiance maps (without cloud cover), and vegetationindex maps (for a certain observation period), and research products on aerosol characteristics maps
and cloud characteristics maps (for a certain observation period) . Details will be fixed hereafter.
More advanced-level products are also planned, targeting the release just after one year, at the
shortest, after the satellite launch. These include global flux maps (L4A) in 64 divided regions,B-38

which will be generated through inverse calculation, based on atmospheric transport models ,of
GOSAT L2 or L3 data and CO2 and CH4 concentration data acquired at monitoring stations on the
ground , and 3D distribution maps of CO2 and CH4 concentrations (L4B) calculated from L4A datausing atmospheric transport models. The details of processing algorithms for these products havenot been finalized yet, and hence are excluded from this Appendix.

B-39