Algorithm Theoretical Basis Document

GCOM-C/SGLI Level-3 Temporal Binning(G6A)

31 March 2017
Masahiro Hori
Japan Aerospace Exploration Agency

1Contents
1. Introduction
1.1 Objectives
1.2 Development strategy
1.3 Processing targets
2. Theoretical Description
2.1 Processing flow
2.2 Statistics
2.3 QC process
2.4. Sample images
3. Current status and remaining issues

21. Introduction
1.1 Objectives
The objective of the G6A algorithm is to take temporal statistics of SGLI Level-3 (L3)spatially-binned geophysical variable (GV) products. The temporal interval of thestatistics is 8-day or 1-month. The spatial resolutions are kept the same.
1.2 Development strategy
The G6A algorithm is designed to use a same program code for taking statistics of allGVs in order to make the maintenance of the code simple. Basically the input andoutput of the G6A processing contains the same statistics variables except for the caseof snow and ice cover extent (SICE) and cloud type (CLTYPE) in cloud properties(CLPR) product (see next sub-section). The SICE and CLTYPE are flag products and theinput and outputs of these two GVs are customized so that the number of snow/ice coverobservation or cloud type within each bin with a spatial resolution of 1/24 deg. for SICE
and 1/12 deg. for CLTYPE is counted. Finally, to make the addition or deletion of GVsquite easy, the GV names of the statistical targets are defined in an external text file sothat the change could be made without compiling the code.
1.3 Processing targets and outputs
Processing targets of the L3 GV temporal statistics are all SGLI products for land,cryosphere, ocean, and atmosphere. Input and output variables are summarized inTable 1.
Table. 1 List of the target products, GVs, and output types of the G6A processing
Input

Long Name

Product ID
RV01

Land surface

Geophysical

OutputOutputVariables (GVs)

Product ID

Variables*1

Rs_VN01

RV01

AVE, RMS,

reflectanceNused, Ninput,MIN, MAX,Date, QA_flag

RV02

Rs_VN02

RV02

Same as above

RV03

Rs_VN03

RV03

Same as above

RV04

Rs_VN04

RV04

Same as above

RV05

Rs_VN05

RV05

Same as above

RV06

Rs_VN06

RV06

Same as above

RV07

Rs_VN07

RV07

Same as above

RV08

Rs_VN08

RV08

Same as above
3RV09Rs_VN09

RV09

Same as above

RV10

Rs_VN10RV10Same as above

RV11

Rs_VN11RV11Same as above

RS01

Rs_SW01

RS01

Same as above

RS02

Rs_SW02

RS02

Same as above

RS03

Rs_SW03

RS03

Same as above

RS04

Rs_SW04

RS04

Same as above

RT01

Rs_TI01

RT01

Same as above

RT02

Rs_TI02

RT02

Same as above

RN08

Rs_VN08P

RN08

Same as above

RN11

Rs_VN11P

RN11Same as above

RP01

Rs_PI01

RP01

Same as above

RP02

Rs_PI02

RP02

Same as above

GEOV

Absolute_relative_

GEOVSame as above

GEOP

Same as above

GEOI

Same as above

NDVI

NDVI

Same as above

EVI

EVI_

Same as above

azimuth*3
Sensor_zenith
Solar_zenith
GEOP

Absolute_relative_
azimuth_PL*3
Sensor_zenith_PL
Solar_zenith_PL

GEOI

Absolute_relative_
azimuth_IR*3
Sensor_zenith_IR
Solar_zenith

NDVI

Normalized
difference
vegetation index

EVI_

Enhancedvegetation index

SDI_Shadow index

SDI

SDI_

Same as above

LAI_

Leaf area index

LAI

LAI_

Same as above

FPAR

Leaf area index

FAPAR

FPAR

Same as above

AGB_Above-ground

AGB

AGB_

Same as above

VRI

VRI_

Same as above

LST

LST_Same as above

Snow grain size of SIST

SGSL

Same as abovebiomass
VRI_Vegetation
roughness index

LST_Land surface
temperature

SGSL

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shallow layer
SIST

Snow and ice

SIST

SIST

Same as above

SICE

SICE

Nsnow1, Nsnow2,

surfacetemperature
SICE

Snow and ice
cover extent

Nsnow3, Nused,
Ninput, Date,
QA_flag

CFRX*2Cloud properties

CLTYPECFRX*2

Ncfrx*2, Nused,
Ninput, Date,
QA_flag

CLTT

CLTT

CLTT

AVE, RMS,
Nused, Ninput,MIN, MAX,Date, QA_flag

CLTH

CLTH

CLTH

Same as above

COTW

CLOT_W

COTW

Same as above

CERW

CLER_W

CERW

Same as above

COTI

CLOT_I

COTI

Same as above

AROT_ocean

AOTO

Same as above

AROT_land

AOTL

Same as above

ARAE_ocean

AAEO

Same as above

ARAE_land

AAEL

Same as above

AOTP

Same as above

AAEP

Same as above

ASSA

Same as above

SST

SST__

Same as above

photosynthetically PAR

PAR_Same as above

AOTO

Aerosol optical
thickness overocean by NP

AOTL

Aerosol optical
thickness overland by NP

AAEO

Aerosol Angstrom
Exponent overocean by NP

AAEL

Aerosol Angstrom
Exponent overland by NP

AOTP

Aerosol properties AROT_pol_land
by PL

AAEP

Aerosol properties ARAE_pol_land
by PL

ASSA

Aerosol properties ARSSA_pol_land
by PL

SST_Sea surface
temperature

PAR_5availableradiation
L380

nLw @ 380nm

NWLR_380

L380

Same as above

L412

nLw @ 412nm

NWLR_412

L412

Same as above

L443

nLw @ 443nm

NWLR_443

L443

Same as above

L490

nLw @ 490nm

NWLR_490

L490

Same as above

L530

nLw @ 530nm

NWLR_530

L530

Same as aboveL565

nLw @ 565nm

NWLR_565

L565

Same as above

L670

nLw @ 670nm

NWLR_670

L670

Same as above

T670

Taua @ 670nm

TAUA_670

T670

Same as above

T865

Taua @ 865nm

TAUA_865

T865

Same as above

CHLA

Chlorophyll-a

CHLA

CHLA

Same as above

TSM

TSM_Same as above

CDOM

CDOM

Same as above

concentration
TSM_Total suspended
matterCDOM

Colored dissolved
organic matter*1 AVE: Average of valid GV data, RMS: Root Mean Square of valid GV data, Nused: Number of valid
GV data actually used in the statistics, Ninput: Number of input GV data, Min: Minimum of valid GVdata, Max: Maximum of valid GV data, Date: Dates of the SGLI observations during the 8-day or
1-month interval period, QA_flag: Flag for quality assurance information, Nsnow1: Number of snow orice cover, Nsnow2: Number of snow with vegetation or bare ice, Nsnow3: Number of melting snow
*2 Input and output of CLTYPE statistics in the descending orbit is cloud fractions for individual cloudtypes. The “CFRX” and “cfrx” denotes the cloud fraction of type “x” which is the ID number of theISCCP cloud classification. For example, in case of “cirrus cloud” the ISCCP ID is “1” and thus theoutput variable named as “Ncfr1” is stored in the output file with product ID of “CFR1”. In theascending orbit cloud fraction is estimated for three layer of High, Middle, and Low, the results ofwhich are stored in “CFRH or cfrh”, “CFRM or cfrm”, and “CFRL or cfrlh”, respectively. In addition,cloud fraction considering all the cloud types are also calculated and generated with the ID of “CFRA”
and “cfra”.
*3 Absolute relative azimuth angle (araz) is converted from sensor and solar azimuth angles by araz =| sensor_azimuth – 180.0 – solar_azimuth | and then its statistics (AVE, RMS, MAX, MIN) arecalculated and stored in the output file of the G5A processing.

Basically (except for the case of flag products (SICE and CLTYPE)) the statistics
variables taken and stored in the output files are the eight values or flag shown in
Table1. When processing the flag products, the statistics of Ave, RMS, Min, and Max are
not taken. Instead, only snow/ice or cloud counts are stored in the output file.
2. Theoretical Description
2.1 Processing flow
Figure 1 indicate the flow of the G6A L3 global GV temporal statistics processing.
Spatial resolution and array definition of output files are kept the same as the input.
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That is, for the processing of land, cryosphere, and ocean products, the resolution is 1/24
deg. and for the atmosphere the resolution is 1/12 deg. The output of G6A processing
contains 8-day or 1-month temporal statistics of GV(s) which are stored in one
dimensional array. An input file (L3 daily spatially-binned products) contains only one
GV and thus the G6A also generates only one output file for the GV. Product ID of theinput and output are therefore the same as shown in Table 1.

1/24deg Global Bin [EQA 1-Dim.](Terrestrial, Cryosphere, Ocean L3
Spatial binning: 1D）

Input: daily × 8-D or1-month
1/12deg Global Bin [EQA 1-Dim.](Atmosphere L3 Spatial binning: 1D)

G6A: Temporal binning process
1/24deg Global Bin [EQA 1-Dim.](Terrestrial, Cryosphere, Ocean L3Temporal binning: 8D, 1M）

1/12deg Global Bin [EQA 1-Dim.](Atmosphere L3 Temporal binning: 8D,1M)

Output: 8-D or
1-M stats.

1-dim. array
Fig. 1 Flow of the G6A L3 GV temporal statistics processing
2.2 Definition of the statistics
Equations for calculating “AVE” and “RMS” are the followings;
AVE =1𝑛𝑛∑𝑖𝑖 𝑥𝑥𝑥𝑥
1

RMS = � (∑𝑖𝑖 𝑥𝑥𝑥𝑥 2 )𝑛𝑛Where n is the total number of observation days with valid GVs, xi is the daily valueof a GV to be processed.
“Nused” is the number of valid GV data actually used in the statistics, whereas “Ninput”
is the number of all the input GV data.
“MIN” and “MAX” are the minimum and maximum of valid GVs data during thetemporal interval.
“Date” is an 8-bit value storing the dates of SGLI observations used in the statistics
calculation. For example, when the Date value is 40 (i.e., 101000), then SGLI
observations of 4th and 6th days in the temporal interval are used in the statistics.
2.3 QA process
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“QA_flag” currently stores flags for discriminating land and water. In future update,
QA information for GVs will be included.
2.4. Sample images
Because the output statistics variables are stored in one-dimensional array, the output
results cannot be visualized as images. The output one-dimensional array of G6A (andG5A) is projected to the equirectangular (EQR) map projection (and polar stereographic
projection only for cryosphere products) in the G7A processing. Figure 2 illustrate thedefinition of the one-dimensional array of the G6A output.

Fig. 2 Definition of the output array of the G6A processing.
3. Current status and remaining issues of the G6A code implementation
The G6A process works well without system errors. Processing speed and memory
size are also within the expectations. Remaining tasks are the implementation of theQA_flag for the output of quality assurance information.

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