Summary of the GLIAtmosphere Analyses
GLI Algorithm Integration Team (GAIT)
Atmosphere Group
NASDA EORC

NASDA Earth Observation Research Center

Contents:
1.
About GLI atmosphere analyses
2.
System flow of GLI atmosphere3.
GLI algorithm list (Atmosphere)4.
GLI standard product list (Atmosphere)5.
GLI channel specification
6.
Summaries of GLI algorithms (Atmosphere)
7.
References

Abuot GLI atmosphere analyses
Cloud, aerosol, and water vapor parameters
Our inability to adequately model cloud processes is a major obstacle in accurate simulations of global warming
scenarios. Low-level clouds and upper-level clouds play different roles in the climate system. How they change
through the global warming process is, at present, not fully understood. Aerosols are also important for the
global warming issue, as revealed by several recent studies on direct aerosol and indirect cloud-aerosolinteraction effects on the Earth's climate. It is, therefore, important to generate global archives of geophysicalparameters related to cloud, aerosol, and water vapor fields. Using these data sets, we should study the global
distribution, annual variability, and feedback mechanisms of the climate field.
*Water vapor algorithms and products will be appeared in future.

NASDA Earth Observation Research Center

System Flow of GLI Atmosphere

Generated by Order

Generated by Planning

Level-1B- Simple, scene -- More details, global -Algorithm module

ATSK1,2 / CTSK1OATSKD

Data

Level-2A_OA

CLFLG_p
ATSKD

segment data

ATSK3_r

ATSK3_e
ATSK5

ATSK_3p

CLOP_p

ATSK16

CLFR
foreach cloud type

NASDA Earth Observation Research Center

DaytimeNighttime

CLTT CLER
CLOP CLHT
CLWP

CLOP
CLER
CLTT

AROP
ARAE

GLI Algorithms (Atmosphere)# Category
A: Standard Algorithm (Scene Analysis, approx.1600kmx1600km)
B: Standard Algorithm (Global Segment Analysis, 0.25degs lat. lon.)
Principal
Product Code Name
Category
Investigator
ATSKD

A

ATSK1, 2

A

ATSK3_pA

ATSK3_r

B

ATSK3_e

B

ATSK5

B

ATSK16B

NASDA

Atmospheric Segment Data

Remarks
Global Segment Data
Generation

Steven
CLFLG_p
Cloud Mask (Scene)
Ackerman
Teruyuki
CLOP_pCloud Properties (Scene)
Nakajiima
Teruyuki
CLOP, CLER, CLHT, CLTT, Cloud Properties (Global,
Nakajima
CLWP
water cloud)
Cloud Properties (Global, thin
Teruyuki
CLOP, CLER, CLTT
ice cloud)
Nakajima
Teruyuki
AROP, ARAE
Aerosol Properties (Global)
Nakajima
Tamio Takamura
CLFR
Cloud Fraction

NASDA Earth Observation Research Center

GLI Standard Product List (Atmosphere)
Product Name Global(4days) Global(16days) Global(1 month)
CLFLG_p Cloud Flag
Cloud
Fraction for
CLFR
o
o
o
each cloudtype
Cloud Optical
CLOP(_p)oooThickness
Cloud
Effective
CLER
o
o
o
Particle
RadiusCloud Top
CLHT
o
o
o
HeightCloud Top
CLTT
o
o
o
Temperature
Cloud Liquid
CLWP
o
o
o
Water Path
Aerosol
OpticalAROPoooThickness at
500 nmAerosol
ARAE
AngstromoooExpoment
Data aviilability
Global : Global distribution (0.25degs resolution), generaged by planning
4days : 4 days average
16days : 16days average
1 month : 1 month average
Scene : 1 scene product (approx.1600km x 1600km), generated by order

NASDA Earth Observation Research Center

Scene
o

Unit
-

-

-

o (_p)--

micronSample:
CLOP by Global (1 month)
-

km

-

K

-

g/m^2

-

-

-

-

Sample:
CLOP_p by SceneGLI channel specification

CH

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29

Wavelength
nm
380
400
412
443
460
490
520
545
565
625
666
680
678
710
710
749
763
865
865
460
545
660
825
1050
1135
1240
1380
1640
2210

Bandwidth
nm
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
8
20
10
70
50
60
110
20
70
20
40
200
220

Lmax
(H/L gains)2
W/m /sr/µm
683
162
130
110/680
124/76964
92/56996/596
39
39
31
33
522
24
369
17
473
13
339
691
585
156
287
227
184
208
153
76
32

Lstd

SNR

2

W/m /sr/µm

IFOV

Primary Targets

m /rad.
59
70
65
54
54
43
31
28
23
17
13
12
12
10
10
7
6
5
5
36
25
14
21
8
8
5.4
1.5
5
1.3

467
1286
1402
893
880
1212
627
611
1301
1370
1342
1293
235
1404
300
991
293
1309
386
241
141
255
218
381
412
303
192
298
160

1000/
1.25

250/
0.3125

1000/
1.25
250/
0.3125

DOM (Dissolved Organic Matter) absorption, Land Aerosol
Baseline of DOM
Chlorophyll absorption, DOM absorption, Land Aerosol
Chlorophyll absorption
Carotinoid absorption, Snow impurity
Plankton (Carotinoid, Phycobiline)
Pigment
Phycobiline absorption, Vegetation
Fluorescence Minimum Absorption
Phycobiline absorption
Baseline of Fluorescence, Atmospheric Correction
Natural Fluorescence
Chlorophyll abs., Aerosol Optical Thickness, Vegetation
Baseline of Fluorescence
Sea Ice Monitoring, Vegetation
Atmospheric Correction
Cloud Geometrical Thickness
Atmospheric Correction
Cloud and Aerosol Optical Thickness, Snow Grain Size
Vegetation Classification etc.
Vegetation Classification etc.
Vegetation Classification etc.
Vegetation Classification etc.
Moisture, Snow Cover, Cloud Optical Thickness
Waver Vapor Amount
Moisture, Snow Grain Size
Water Vapor Amount, Upper Cloud Detection
Cloud Effective Radius, Cloud Phase, Snow Grain Size
Cloud Effective Radius

CH

30

31

32

33

34

35

MTIR channels
NASDA Earth Observation Research Center

36

Wavelength
µm
3.715

6.7

7.3

7.5

8.6

10.8

12.0

Bandwidth
µm
0.33

0.5

0.5

0.5

0.5

1.0

1.0

Dynamic
Range
K
345

307

322

324

350

354

358

VNIR and SWIR channels

TargetTemp.
K
H: 300NEdT(Cold/Hot TV)K
0.07/0.07

L: 2500.71/0.78

285

0.02/0.03

200

0.27/0.32

300

0.02/0.03

200

0.24/0.27

300

0.02/0.02

200

0.21/0.24

300

0.03/0.05

180

0.47/0.49

300

0.04/0.05

180

0.24/0.30

300

0.04/0.06

180

0.23/0.27

IFOV

Primary Targets

m /rad.
Cloud Effective Radius
1000/
1.25

Water Vapor Index

Water Vapor Index

Water Vapor Index

Water Vapor Amount, Temperature

Temperature

Temperature

ATSK1,2<Acronyms>
P.I. :
Principal Investigator
A.D. :
Algorithm Developer
A.I. :
Algorithm Integrator

Cloud Mask (Scene)

Table CLOUD MASK BIT CONFIGURATION

Abstract
The GLI cloud mask will indicate whether ag i v e n v ie w o f t he e a r t h s u r f a c e i sunobstructed by clouds or optically thickaerosol, and whether that clear scene iscontaminated by a shadow. The cloud mask
will be generated at 1-km resolution. Input tothe cloud mask algorithm is assumed to becalibrated and navigated Level-1B radiancedata.
The strategy for this cloud mask algorithm is
to start with single pixel (1-km field of view)
tests. Many of the single pixel tests rely onradiance (or temperature) thresholds in theinfrared and reflectance thresholds in thesolar. These thresholds vary with surface
emissivity, with atmospheric moisture andaerosol content, and with GLI viewing scanan gle . The 3 2 bi ts of C l o u d f l a gs w er eobtained from ATSK1,2 together with CTSK1
in cryosphere analyses.

Input :
Output :P.I. :
A.D. :A.I:

Level-1B data
CLFLG_p
Steven A. Ackerman (SSEC,University of Wisconsin)
Richard Frey (SSEC, University of Wisconsin)
Masaru Tairadate (Fujitsu)

NASDA Earth Observation Research Center

bit
field
0-1
2
3
4
5-6
7
8
9
10
11
12
13
14
15
16
17
18

Algorithm

Key
00 = cloud
01 = 66% prob. clear
ATSK1,2 Unobstructed FOV Quality Flag
10 = 95% prob. clear
11 = 99% prob. clear
Processing Path Flags
Day / Night Flag
0 = Night / 1 = Day
Sunglint Flag
0 = Yes / 1 = No
Snow / Ice Background Flag 0 = Yes/ 1 = No
00 = Water/ 01 = Coastal
Land / Water Flag
10 = Desert / 11 = Land
Additional Information
Non-cloud obstruction Flag 0 = Yes / 1 = NoThin Cirrus Detected (solar) 0 = Yes / 1 = No
Shadow Found
0 = Yes / 1 = No
1-km Cloud Flags
Result from Group I Tests
0 = Yes / 1 = No
Result from Group II Tests
0 = Yes / 1 = No
Result from Group III Tests 0 = Yes / 1 = No
Result from Group IV Tests 0 = Yes / 1 = No
Result from Group V Tests
0 = Yes / 1 = No
reserved for ATSK4
Description

19

Dummy Flag

20

Using Channel Flag

Used channel 28 and 29

Snow/Cloud descrimination

Execution flag

21

CTSK1

22-23

Cloud confidence level flag

24-26

27-31

Surface classification flagSpare

0 = not dummy / 1 = dummy(valid for bit 1-20)
0 = not used / 1 = used(valid for bit 1-20)
0 = not executed / 1 =executed(valid for bit 21-26)
00 = clear sky
01 = high-confidence cloudy
confidence = 100%
10 = middle-confidence cloudy
50% < confidence < 100%
11 = low-confidence cloudy
0% < confidence < 50%000 = snow over ice
001 = sea ice
010 = cloud shadow
011 = land
100 = open water
101 = snow over land
111 = spareATSK3_p

Cloud Properties (Scene)

Abstract
A method for satellite remote sensing of cloud microphysics has beendeveloped to apply to GLI/ADEOS-II multispectral radiance data. Thisalgorithm is an enhanced algorithm of AVHRR/NOAA data analysis (Nakajima
and Nakajima1995; Kawamoto 2000), which has an active thermal collection inabsorption channel.
Undesirable radiation components such as ground-reflected solar radiationand thermal radiation are guessed from satellite-received radiances inchannels 13 or 19 (678 or 865 nm), 30 (3.715 µm) and 35 (10.8 µm) of GLI
and subtracted from radiances in channels 13 and 30 to derive the reflectedsolar radiation of a cloud layer which includes information about cloud
microphysical properties. This method can be applied to a broad range ofwater clouds from semi-transparent to thick clouds.
The ATSK3_p is designed to analyze every scene of Level-1B data.

Input :
Output :Principal Investigator :Algorithm Developer :Algorithm Integrator :Level-1B data, CLFLG_p (from ATSK1,2/CTSK1)
CLOP_pTeruyuki Nakajima (CCSR, University of Tokyo)
Takashi Y. Nakajima (EORC, NASDA)
Masaru Tairadate (Fujitsu)

Sample View:
Cloud optical thickness retrieved from MODIS/Terra
data (off the coas t of California). This result was
obtained using GL I /ADEOS-II an aly sis sy st e md evelope d i n EOR C /N ASD A. M OD IS d a ta were
provided by NASA.

NASDA Earth Observation Research Center

ATSK3_r<Acronyms>
P.I. :
Principal Investigator
A.D. :
Algorithm Developer
A.I. :
Algorithm Integrator

Cloud Properties (Global)

Abstract
A method for satellite remote sensing of cloud microphysics has beendeveloped to apply to GLI/ADEOS-II multispectral radiance data. Thisalgorithm is an enhanced algorithm of AVHRR/NOAA data analysis (Nakajima
and Nakajima1995; Kawamoto 2000), which has an active thermal collection inabsorption channel.
Undesirable radiation components such as ground-reflected solar radiationand thermal radiation are guessed from satellite-received radiances inchannels 13 or 19 (678 or 865 nm), 30 (3.715 µm) and 35 (10.8 µm) of GLI
and subtracted from radiances in channels 13 and 30 to derive the reflectedsolar radiation of a cloud layer which includes information about cloud
microphysical properties. This method can be applied to a broad range ofwater clouds from semi-transparent to thick clouds.
The ATSK3_r is designed to analyze the atmospheric segment data.for
global scale analyses.

NASDA Earth Observation Research Center

Input :
Output :P.I.:
A.D. :
A.I. :

Segment data
work_r_w, work_r_i
Teruyuki Nakajima (Univ. Tokyo)
Takashi Y. Nakajima (NASDA)
Masaru Tairadate (Fujitsu)

Sample View:
Global distribution of cloud optical thickness
retrieved from MODIS/ Terra data. Monthly
mean values from September13 to October 12
in 2000. These results were obtained using
GLI/ADEOS-II analysis system developed in
EORC/NASDA. MODIS data were provided byNASA.

ATSK3_p & ATSK3_r flow chart
I n t he A TSK3_p process, radiance of n on absorbing channel (Lch13, or Lch19), ground albedo(Ag), and scan geometries are used to calculate
cloud optical thickness (CLOP_p).
On the other hand, the A TSK 3_r proc es sneeds non-absorbing channel (Lch13, or L ch19),
absorbing channel (Lch30), and thermal channel(Lch35), ground albedo (Ag), and some objective
an al ysis data such as v e r t i cal p ro f iles o ftemperature, pressure, and relative humidity, toobtained optical thickness (CLOP), effective radius(CLER), cloud liquid water path (CLWP), cloud top
temperature (CLTT) and cloud top height (CLHT).
P re- proc es s and pos t- proc es s pro gram ar erequired to select target pixels from radiancedataset and select suitable results from all outputs.

NASDA Earth Observation Research Center

ATSK3_e

Cloud Properties (Global, thin ice cloud)Abstract
Th is al gori t hm w i l l re tri ev e t hin c i r r us c l o u dmicrophysical parameters, effective particle radius,
optical t hickness and cloud top temperature fromchannels (3.715 µm), 35 (10.8 µm), and 36 (12.0 µm).
Figure 1 (next page) illustrates the flow of the algorithm.
And Figure 2 illustrates the finding process (Processing
A in Fig.1) of cloud top temperature and the surface
temperature (or the cloud top temperature of the cloudlying under the high cloud). Fig.3 shows examples for
the Look-Up Table in the algorithm and the row data intest-run (using AVHRR/NOAA data).

NASDA Earth Observation Research Center

Input :
Output :
P.I :
A.D.:
A.I. :<Acronyms>
P.I. :
Principal Investigator
A.D. :
Algorithm Developer
A.I. :
Algorithm Integrator

Segment data
work_eTeruyuki Nakajima (CCSR, University of Tokyo)
Shuichiro Katagiri (EORC, NASDA)
Shuichiro Katagiri (EORC, NASDA)

Sample View:
Global distribution of cirrus cloud optical thick-ness
retrieved from MODIS/Terra data. Result of one day
analysis in December, 2000. This result was obtainedusing GLI/AEOS-II analysis system developed in
EORC/NASDA. MODIS data were provided by NASA.

ATSK3_e flow chart
Segment Data
(100 data per pixel)

Surface Temperature,
Effective Water Vapor
from ancillary data

Satellite Data

Temperature of Cloud Top Initial
Condition Tc[K]Temperature of Ground or
Low Cloud Initial Condition
Tg [k]
Effective Water Vapor
Wef

10.8µm
10.8-11.8 µm
3.7-10.8 µm

Processing A:
Fix Coud Temperature
Tc = Tc - 0.5

Table A
10.8 µm vs. 3.72-10.8 µm

No Match with Table A

Fix Ground or Low Cloud
Temperature
Tg = Tg - 0.5
Fix Effective Water Vapor
Wef → Wef (new)No Retrieved
Processing B:

Table B
10.8 µm vs. 10.8-11.8 µm
Average Smallest 10 Effective Radii and their Optical Thickness

Effective Radius,
Optical Thickness

Fig.1 The flow chart of the retrieval algorithm.

NASDA Earth Observation Research Center

Fitting Tg and Tc

Fitting re and τ

Fig.2 The concept of the table fitting
The example of processing A in Fig. 1, here,
AVHRR’s ch3=3.7µm, ch4=10.8µm, ch5=11.8µm

NASDA Earth Observation Research Center

Fig.3 Examples for the table in Algorithm and the raw
data in Testrun (AVHRR)
here, AVHRR’s ch3=3.7µm, ch4=10.8µm, ch5=11.8µm

ATSK5<Acronyms>
P.I. :
Principal Investigator
A.D. :
Algorithm Developer
A.I. :
Algorithm Integrator

Aerosol Properties (Global)

Abstract
This sa te ll it e r emot e se nsi ng al gori th m r etr iev es
aer os ol opt ic al t hi ck nes s at 50 0nm and Angstrom
exponent from two channel radiant data, that is, visible(channel 13, 678nm) and near-IR (channel 19, 865nm)
satellite data. Satellite-received radiance is synthesizedwith four Look-Up Tables (LUTs). For retrievals, ancillarydata are needed, which include wind velocity at 10 meterhei ght, oz one and w ater vapor amount to correct
radiance for surface reflectance, ozone and water vapor
absorption.

NASDA Earth Observation Research Center

Input :
Output :P.I. :
A.D. :
A.I. :

Segment data
AROP, ARAE
Teruyuki Nakajima (CCSR, University of Tokyo)
Akiko Higurashi (NIES, Ministry of Environment)
Yi Liu (EORC, NASDA)

Sample View:
Global distribution of aerosol optical thickness retrieved
from MODIS/Terra data. Monthly mean values from
September 13 to October 12 in 2000. These results
were obtained using GLI/ADEOS-II analysis systemdeveloped in EO R C/ NAS DA. M O DI S da ta we reprovided by NASA.

ATSK5 flow chart

Synthesizing
Theoretical
ReflectanceLook-Up Table--Single scattering
components ( P, ω )--Multiple scattering
components(m )(n , Rmol ,m )--Correction of water vapor

χabsorption ( TW )Segment
DataRλ1, Rλ2

Water Vaporand ozone
amount andsurface wind
speedCompareTheoretical
Reflectanceτ ,αNASDA Earth Observation Research Center

Rλ=0.678 Rλ=0.865(25 data per 1
pixel)

Select 1 suitableradiance data from25 data

Observed
ReflectanceATSK16

Cloud Fraction

Abstract
The algorithm will classify clouds into several types using
output data from ATSK3_r and ATSK3_e (The work files. See
the corresponding pages for details), based on the ISCCP
categories. The characteristics of this algorithm have an index
of cloud shape and an additional classification of cirrus. The
cloud shape can be determined by sum of spatial differences
between each pixel in an area of 0.25 degrees square inlatitude and longitude, so a high difference means cumulus-type
and a low one stratus-type. The split window technique willseparate a cirrus cloud from other clouds.
The cloud information by the ATSK16 algorithm will be used
for estimation of surface radiation budget as a research product.
The ATSK16 also generates averaged cloud parameters suchas CLOP, CLER and so on from work files of ATSK3_r and
ATSK3_e.
Rossow et al. 1996, ISCCP Documentation of New Cloud Dataset

Input :
Output :work_r_w(by ATSK3_r), work_r_i(by ATSK3_r), work_e(by ATSK3_e)
CLFR_1-19,
CLOP_w_r, CLER_w_r, CLTT_w_r, CLHT_w_r, CLWP_w_r, CLOP_i_r,
CLOP_i_e, CLER_i_e, CLTT_i_e
Principal Investigator :Tamio Takamura (CEReS, Chiba University)
Algorithm Developer :Itaru Okada (CEReS, Chiba University)
Algorithm Integrator :Yi Liu (EORC, NASDA)

NASDA Earth Observation Research Center

ATSK16 flow chart

Ice cloudYes

No

Average CLOP_i_r
CLFR_18

Index of imhomogeneityof CLTT: CLFR_19

CLTP classification

CLTP=<440
Average CLTT :CLFR_12
Average CLOP:
CLFR_15

440<CLTP=<680
Average CLTT:
CLFR_13
Average CLOP:
CLFR_16680<CLTP
Average CLTT:
CLFR_14
Average CLOP:
CLFR_17

CLOP
classification

CLOP
classification

CLOP
classification

3.6>CLOP
count pixel number:
CLFR_1

3.6>CLOP
count pixel number:
CLFR_4

3.6>CLOP
count pixel number:
CLFR_723>CLOP>=3.6
count pixel number:
CLFR_223>CLOP>=3.6
count pixel number:
CLFR_523>CLOP>=3.6
count pixel number:
CLFR_8

CLOP>=23
count pixel number:
CLFR_3

CLOP>=23
count pixel number:
CLFR_6

CLOP>=23
count pixel number:
CLFR_9

CLTP: Cloud Top Pressure

Count pixel number:
CLFR_10

Sum of
CLFR_1 to CLFR_10
CLFR_11

NASDA Earth Observation Research CenterReferences
ATSK1,2
Ackerman, S. A., W. L. Smith and H. E. Revercomb, 1990: The 27-28 October 1986 FIRE IFO cirrus case study: Spectral
properties of cirrus clouds in the 8-12 micron window, Mon. Wea. Rev., 118, 2377-2388.
Ackerman, S. A., 1997 : Discriminating clear-sky from cloud with GLI. Development status report to NASDA.
Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller and L. E. Gumley Discriminating Clear-sky fromClouds with MODIS, 1998: J. Geo. Res., 103 , D24, p. 32,141
ATSK3_p, ATSK3_r
Nakajima, T. and M. D. King, 1990: Determination of the optical thickness and effective radius of clouds from reflected solar
radiation measurements. Part I: Theory. J. Atmos. Sci., 47, 1878-1893.
Nakajima T. Y. and T. Nakajima, 1995: Wide-area determination of cloud microphysical properties from NOAA AVHRR
measurement for FIRE and ASTEX regions, J. Atmos. Sci., 52, 4043-4059.
Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 2000: A global determination of cloud microphysics with AVHRR remotesensing, J. Climate, 14, 2054-2068.
ATSK3_e
Katagiri, S., 2001:, thesis for doctor degree., University ofTokyo.
ATSK5
Nakajima, T., and A. Higurashi, 1998: A use of two-channel radiances for an aerosol characterization from space. Geophys.
Res. Lett., 25(20), 3815-3818.
Higurashi, A., and T. Nakajima, 1999: Development of a two Channel aerosol retrieval algorithm on global scale using
NOAA/AVHRR. J. Atmos. Sci., 56, 924-941.
Higurashi, A., and T. Nakajima, 2000: A study of global aerosol optical climatology with two channel AVHRR remote sensing.
J. Climate, 13, 2011-2027.
ATSK16
Rossow, W.B., A. W. Walker, D. E. Beuschet. M. D. Roiter, 1996: International Satellite Cloud Climatology Project
documentation of new cloud dataset. pp39.

NASDA Earth Observation Research Center