001

3.1.5 ATSK16

A. Algorithm Outline（１）Algotrithm Code: ATSK16（２）Product Code: CLFR（３）PI names: Tamio Takamura（４）Overview of algorithm
Abstract
The algorithm described in the ATSK16 can classify clouds into several types using data from
ATSK3_r, based on the ISCCP categories. The characteristics of this algorithm have an index ofcloud shape and an additional classification of cirrus. The cloud shape can be determined bysum of spatial differences between each pixel in an area of 0.25゜ｘ 0.25゜in Lat. and Lon., soa high difference means cumulus-type and a low one stratus-type. The split window technique
can separate a cirrus cloud from other clouds.
The cloud information by the ATSK16 algorithm can be used for estimation of surface
radiation budget as a research productBackground
Clouds can adjust climate variability due to reflection of solar radiation and emission ofthemselves. Therefore, the Earth radiation budget at the surface and TOA is controlled by
global distributions of cloud characteristics, such as amount(or cloud fraction), cloud top height
and optical thickness. As well known, a major part of clouds basically play a role of parasol
because of their relatively thick in optical depth.

In some cases, however, clouds with thin

optical thickness and higher altitude such as cirrus can make the Earth surface warm, in otherwords greenhouse effect, due to weak reflection of direct solar radiation and relatively highemmisivity for terrestrial radiation.

So the global distribution of cloud type and its

characteristics is decisive information for the energy budget and hydorological pattern of the
Earth.
Cloud response for climate change will be expected to stabilize the Earth-atmosphere system
due to increase in cloud fraction with the global warming as a negative feedback. And also
cloud pattern of its appearance in the regional area will be changed according to locally-thermal
unbalance induced by the global warming. This has possibilities to affect the local weather andmodify the local climate pattern. It is one of reasons to know the cloud statistics in the global
and regional scale.
GLI Algorithm Description

3.1.5-1

The radiation budget and the water budget at the surface are dependent on the complex
motion of cloud and precipitation in the atmosphere, where cloud appears in the combined
motion of the global and local circulation. So, it is important to know the yearly and spatial
variation, resulting on understanding the change of circulation systems.
In addition to the cloud response to the global warming, climate effects of the polluted
atmosphere including suspended particles from human activities are worried due to the cloudincrease in amount and optical characteristics.

This indirect effect has been estimatedinsufficiently, as pointed out by ISCCP, and is another important issue for human activity effect.
Therefore, statistical difference in cloud characteristics between inside and outside of a polluted
atmosphere may give an index of its climatic effect.
The ISCCP program has been running under importance of global energy balance affected bycloud. The ISCCP can categorize clouds into 9 types according to their optical thickness andcloud top height (or cloud top temperature). Our algorithm is basically accordance with the
ISCCP algorithm which has separation boundaries of 440 and 680hPa in cloud height, and 3.6
and 23 in cloud optical thickness. The cloud top temperature can be derived from the infraredwindow channel, and the optical thickness from the visible reflectance.
In our algorithm, cirrus cloud can be detected using a split window technique before the cloudclassification routine by the ISCCP algorithm, because of its detection difficulty of very thin andhigh cloud. This technique uses a small difference in transmittance of 10um window region for
ice and has an advantage of no dependence of surface conditions.
Moreover, cloud inhomogenity routine has been added in our algorithm, which can produce
cloud type of stratus- or cumulus-type. This means a cloud multi-layerness or overlapping index.
Cloud overlapping information is useful for estimation of surface radiation because satellitepassive sensor cannot give any information of multi-layer cloud.

B. Theoretical Description(1) Methodology and logic flow
In this algorithm, cloud pixels are classified by the cloud top pressure and by the cloudoptical thickness into 9 water cloud types. Before the classification, the ice cloud is separated by
the split window method. The index of cloud imhomogeneity is also derived. The values of thecloud optical thickness in each class for same cloud top pressure are averaged.
The border values of cloud top temperature are 440 hPa and 680hPa, and that of the cloudoptical thickness are 3.6 and 23.0. These values are originated in Rossow and Garder (1996).
The sum of spatial differences of the cloud top temperature is calculated for the index of
GLI Algorithm Description

3.1.5-2001

imhomogeneity. The index is derived from the sum of the absolute value of temperature
difference in latitudinal direction and in longitudinal direction. The index shows that the cloudin a segment tends to be stratus or cumulus, and is referred in the cloud over-lapping model inthe radiation transfer calculation.
CLTP_w_r, CLTT_w_r, CLOP_w_r, CLFLG(result of split window) and CLOP_i_r areprovided from storage unit ‘ work_wr’.
The logic flow is following:
1. Separation of ice cloud by the split window ( in ATSK3_r ).
2. Classification of cloud pixel by the cloud top pressure (CLTP_w_r) into 3 categories.
3. Averaging the cloud top temperature (CLTT_w_r) and the cloud optical thickness(CLOP_w_r) in the each category.
4. Classification of 3 cloud top pressure categories by the cloud optical thickness(CLOP_w_r) into 9 categories and counting number of pixel in each category
5. Calculating the index of imhomogeneity from all the cloud top temperature

GLI Algorithm Description

3.1.5-3

001

001

yes

Ice cloudno

Average CLOP_i_r
CLFR_18

Index of

CLTP

imhomogeneityclassification

Count pixel number:of CLTT: CLFR_19

CLFR_10

CLTP=<440

440<CLTP=<680

680<CLTP

Average CLTT :Average CLTT:

Average CLTT:

CLFR_12

CLFR_13

CLFR_14

Average CLOP:

Average CLOP:

Average CLOP:

CLFR_15

CLFR_16

CLFR_17

CLOP

CLOP

CLOP

classificatio

classificatio

classificatio

n

n

n

3.6>CLOP

3.6>CLOP

3.6>CLOP

count pixel number:count pixel number:count pixel number:

CLFR_1

CLFR_4

CLFR_723>CLOP

23>CLOP

23>CLOP

>=3.6

>=3.6

>=3.6

count pixel number:count pixel number:count pixel number:

CLFR_2

CLFR_5

CLFR_8

CLOP>=23

CLOP>=23

CLOP>=23

count pixel number:count pixel number:count pixel number:

CLFR_3

CLFR_6

CLFR_9

GLI Algorithm Description

3.1.5-4

001

Those results in 4days are averaged or the latest result in 4days is stored. The algorithm has
a parameter for selection of those two method.
This algorithm also works as the post process of ATSK3_r and ATSK3_e. CLER_w_r,
CLHT_w_r, CLTT_w_r, CLOP_w_r, CLWP_w_r and CLOP_i_r from ATSK3_r are averaged ina segment and also averaged in 4 days. CLER_i_e,CLOP_i_e and CLTT_i_e from ATSK3_e
are averaged in 4 days.
(2) Physical and Mathematical aspects of the algorithm
Classifying cloud pixel by the cloud top temperature and by the cloud optical thickness isuseful to estimate the effect on global radiation budget from cloud, although the methodologies
of classification and averaging are simple in this algorithm. The borders of the classification isbased on ISCCP ( International Satellite Cloud Climate Project ), hence the product is available
to compare with the data in ISCCP.
The product is designed to be useful data to calculate the radiation transfer. The cloud isclassified into three vertical layers in which cloud is generated by different reason and theoptical thickness is compiled in the layer, because of the limit of computer resource, although itis the best method that all the cloud optical thickness in a segment are calculated.

GLI Algorithm Description

3.1.5-5

001

C. Practical Considerations(1) Programming, Procedural, Running Considerations
Program Requirements:

The following table shows information about the expectedsoftware generated from this algorithm.
Program Memory
Program Size
Required Channels
Necessary/Ancillary Data
Expected Disk Volume
Special Programs or
Subroutines

GLI Algorithm Description

300 MBytes
Bytes

3.1.5-6

001(2) Calibration and validation(a)Approach

Atmospheric validation data are obtained by continuous measurements at several validationsites, intensive field experiments performed several times after launch, and data that alreadyexist.
Basic approach of the validation with those data sets is based on physical simulation ofradiation fields. For this purpose a good radiative transfer model has to be used with input of
optical parameters retrieved from validation efforts.
This plan will include a validation not only for cloud but also for aerosol, because both ofthem can affect the radiation field in the atmosphere, independently and/or cooperatively.
(b) Measured Quantities

The following quantities are useful to be measured by GLI-ATMOS.
(a) Spectral direct and sky radiances: Sky radiance measurements will be performed for
retrieving column aerosol size distribution, optical thickness, and phase function. This is also
effective for vicarious calibration under clear sky condition.
(b) Shortwave and longwave radiant fluxes at ground surface: Surface radiative radiant energy
fluxes are important to assess the validity of retrieved atmospheric products, which should
suitably reproduce the surface energy fluxes.
(c) Spectral radiances at surface and several heights of atmosphere: Surface and airborne
spectrometers are useful for measurements to provide spectral radiances for studying GLI
algorithms and validation.
(d) Aerosol microphysical parameters: Aerosol samplers and instruments for measuring total
scattering cross section, absorption cross sections and upward scattering fraction are useful for
providing geophysical parameters to synthesize atmospheric path radiances. Lidar is also
important for measuring aerosol stratification and backscattering cross section.
(e) Cloud microphysical parameters: Microwave radiometers and airborne particle probes areimportant for validation of cloud optical thickness and effective particle radius retrievals. Lidaris also useful for measuring cloud stratification of thin clouds and bottom height of thick clouds.
GLI Algorithm Description

3.1.5-7

These are important for simulating surface radiant energy fluxes. Cloud amount validation is
also an issue to be conducted.
(f) Water vapor: GLI can derive high spatial-resolution data of water vapor distribution. Water
vapor and temperature fields should be provided by validation effort. Sonde data will be used.
FTIR type spectrometer is useful for evaluating the water vapor effects on the radiation field.
(g) Precipitation: GLI can provide warm precipitation over land. Rain validation data from radarand rain-gauge should be collected.
Quantities for validation/vicarious calibration are shown in Table 1.
===============================================================
Table 1: Quantities for validation/vicarious calibration. S, R and V in level-column mean
standard product, research product, and validation/vicarious product, respectively. Other
satellite sensors are also listed for comparison.
Quantity

Level

Accuracy

Methods
Note
CLFR
S
5%
Surface obs., Lidar, CPR,GIO, AVHRR
CLOP
S
10%
Aircraft, SWAR, POLDER, MODIS,
AVHRR
CLWT
S
20%
Aircraft, MCWR, AMSR,SSM/I
CLRE
S
20%
Aircraft, MODIS, AVHRR
CLTP
S
0.5K
Sonde, Lidar
CLHT
S
1km
Sonde, Lidar
CLBH
R
1km
Sonde, Lidar
WTVA
R
0.2g/cm2
Sonde, GPS, Sun photometer
AMSR, SSM/I
AROP
S
10%
Sky
radiometer/Sun
photometer,
POLDER,MODIS, AVHRR
FSSRF
R
5W/m2
BSRN, GLI-ATMOS
FSTOA
R
5W/m2
CERES instantaneous
FLTOA
R
5W/m2
CERES instantaneous
PRCP
R
Factor 2
Radar, CPR#1: Sensor names: GEO (Geostationary satellites), CPR (Cloud Profiling Radar), SWSR
(ShortWave Spectral Radiometer), MCWR (MiCroWave Radiometer)#2: Product name: CLFR (Cloud fraction); CLOP (Cloud optical thickness); CLWT (Cloud
water path); CLRE (Effective cloud particle radius); CLHT (Cloud top height); CLBH (Cloudbottom height); WTVA (Column water vapor amount); AROP (Aerosol optical thickness);
FSSRF (Shortwave surface radiative flux); FSTOA (Shortwave TOA radiative flux); FLTOA(Longwave TOA radiative flux); PRCP (Precipitation).
GLI Algorithm Description

3.1.5-8001001(c)Sites

Validation sites have to be selected in order to cover various atmospheric/surface conditionsand various scientific interests. Candidates of validation sites that should be invested by GLIATMOS are listed in Table 2. Chinese and South-East Asian sites are important for obtaining
data for anthropogenic air pollution and aerosol loading effects in the Asian region. Ocean
measurements by vessels are needed for obtaining data in clean airmass and studying maritime
aerosol and cloud fields.
===============================================================
Table 2: Validation site candidates for GLI-ATMOS and main program names for operating thesite. OP OL, and TS in the third column mean operating, planned, and testing stage,
respectively.
Code
L1
L2
L3
L4
L5
L6
L7
S1
S2
S3

Name
Sri Samrong
Shou-Xian
Yinchuan
Mandalgovi
Fukue-jima
Miyako-jima
Bukittinggi
Ship-Mirai
Line Australia
Line Persia

Status
OP
OP
OP
OP
TS
PL
OP
TS
TS
TS

Programs
GAME, GLI-ATMOS, SKYNET
GAME, GLI-ATMOS, SKYNET
SKYNET
SKYNET
GLI-ATMOS
GLI-ATMOS
GAW
Frontier
GLI -ATMOS
GLI -ATMOS#1: Program name: GAME (GEWEX Asian Monsoon Experiment); SKYNET (SKY radiometer
NETwork); GAW (Global Atmospheric Watch program); Frontier (Frontier Program)(d) Instrumentation

Instruments listed in Table 3 should be deployed at the sites in Table 2 for performing the GLI
validation. The packages have to be designed meeting with requirements for network
development. Shipborne system design is also important. Since the operating sites have some ofthese instruments already as listed in Table 2, it should be carefully investigated what
instruments should be added to each site.
===============================================================
Table 3: Instruments important for GLI-ATMOS

GLI Algorithm Description

3.1.5-9

Instrument
Sky radiometer
SW, LW fluxmeter
Pyrheliometer
SW spectral radiometer(SWSR)FTIR
Microwave radiometer(MCWR)GPS
Aerosol sampler
Aethalometer
Total nephelomer

Quantities for validation (see Table 1)
Sky radiance, AROPFSSRF, Longwave surface radiation
FSSRF, AROP, WTVAFSSRF, CLRE, CLOP, CLWT
IR surface radiation budget
CLWT, WTVAWTVA, support for radiative transfer calculation
aerosol chemical characterization,
support for aerosol retrievals
aerosol single scattering albedo,
support for aerosol retrievals
aerosol extinction coefficient,support for aerosol retrievals

Sky radiometer is important for obtaining column values of aerosol size distribution andscattering phase function. The result is useful for retrieving GLI-received radiances forvicarious calibration. Aerosol samplers, aethalometer and total nephelometer are important for
obtaing single scattering albedo of aerosols, that are needed for radiative transfer calculation of
GLI-received radiances and the radiation budget at surface and top of atmosphere (TOA).
Microwave radiometer is important for obtaining column water vapor amount and cloud liquidwater path. GPS is useful to obtain the water vapor amount at the site. This should be regarded
as supplementary data sets, rather than validation of water vapor amount, for radiative transfer
calcuation. SW spectral radiometer is a radiometer to measure nadir radiances at several
wavelengths from 0.3 to 2.2 microns for obtaining cloud optical thickness, effective particle
radius and liquid water path combining with the microwave radiometer data. Satellite productsof clouds, aerosols and water vapor should be checked whether they can produce surface
radiation budget measured by fluxmeters, pyrheliometer and FTIR.
(e) Intensive Field Experiments

Intensive field experiments are important to integrate validation efforts in atmosphere, ocean,land and cryosphere groups. GLI-ATMOS is interested in performing field experiments withother groups, since the atmosphere interacts with ocean, land and snow/ice surfaces. Aircraft
measurements of upwelling radiances are important for vicarious calibration. Table 4 lists
airborne facilities usefulfor GLI-ATMOS. GLI-ATMOS should invest in airborne

instrumentation such as AMSS, AMR and PMS-probes. Also it should be implemented withinternational collaboration.

GLI Algorithm Description

3.1.5-10001

===============================================================
Table 4: Airborne measurement facilities useful for GLI-ATMOS
GLI-ATMOS
Thailand
MODIS
LaMP
Nagoya & Hokkaido Univ,
Meteor. Res. Inst.

AMSS, AMR, (PMS)
PMS, CCN, King Air x 2, G21
MAS, HIS
PMS, PVM, NephelometerPMS

6 months, 12 months, 18 months, and 24 months after launch are suitable candidates fortiming of the experiments. 12 and 24 month timing should be an intensive field observation
campaign with other GLI groups to cover atmosphere and earth's surface.

GLI Algorithm Description

3.1.5-11

001

001

(3) Quality Control and Diagnostic Information(4) Exception Handling(5) Constraints, Limitations, Assumptions(6) Publications and Papers

D. References
Rossow, W. B. and A. W. Walker, D. E. Beuschel and M. D. Roiter, 1996: International satellite
cloud climatology project (ISCCP), documentation of new cloud datasets. World
Meteorological Organization. 115 pp.

GLI Algorithm Description

3.1.5-12