Algorithm Theoretical Basis Document

GCOM-C/SGLI Level-3 Map (G7A)

31 March 2017
Masahiro Hori
Japan Aerospace Exploration Agency

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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 G7A algorithm is to make map-projected images of the SGLI
Level-3 (L3) spatially- or temporally- binned geophysical variable (GV) products. Mapprojection is the equirectangular (EQR) projection for all products and only forcryosphere products polar stereographic (PS) projection is also available. The spatialresolutions are kept the same as the input.
1.2 Development strategy
The G7A algorithm is designed to use a same program code for making map-projected
images of all GVs in order to make the maintenance of the code simple. Basically theoutput of the G7A processing contains map projected spatial and temporal average(AVE) of GV except for the case of snow and ice cover extent (SICE) and cloud type(CLTYPE) in cloud properties (CLPR) product (see next sub-section). The SICE andCLTYPE are flag products and the outputs of these two GVs are customized so that theratio of the number of snow/ice cover observation or cloud type to the number of total
observations within each bin is calculated. Finally, to make the addition or deletion ofprocessing target GVs quite easy, the GV names of the processing targets are defined in
an external text file so that the change could be made without compiling the code.
1.3 Processing targets and outputs
Processing targets of the L3 Map algorithm (G7A) 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 G7A processing
Input

Long Name

Geophysical

OutputOutputVariables (GVs)

Product ID

Variables*1

Rs_VN01

RV01

AVE, 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

Product ID
RV01

Land surface
reflectance3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_PI01RP01

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

Stat (= Nused),

CLTYPECFRX*2surfacetemperature
SICE

Snow and ice
cover extent

CFRX*2Cloud properties

QA_flag
Stat (= Nused),
QA_flag

CLTT

CLTT

CLTT

AVE, QA_flag

CLTH

CLTH

CLTHSame 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_available
radiation
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
5L530nLw @ 530nm

NWLR_530

L530

Same as above

L565

nLw @ 565nm

NWLR_565

L565Same 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” denotes the cloud fraction of type “x” which is the ID number of the ISCCP cloudclassification. For example, in case of “cirrus cloud” the ISCCP ID is “1” and thus the input variablenamed as “Ncfr1” is used to take temporal statistics and store them in the output file with product IDof “CFR1”. In the ascending orbit cloud fraction is estimated for three layer of High, Middle, and Low,
the results of which are stored as “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 IDof “CFRA” and “cfra” in the G5A and G6A processing.
*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 stored in the input files are the eight values or flag of Ave, RMS, Nused, Ninput,Min, Max, Date, QA_flag (see ATBD of G5A and G6A). When processing the flag
products, the statistics of Ave, RMS, Min, and Max are not taken. Instead, only snow/iceor cloud counts are stored in the input file.
2. Theoretical Description
2.1 Processing flow
Figure 1 indicate the flow of the G7A L3 Map projection processing. Spatial
resolutions are kept the same as the input without projection. The output of G7A
processing contains global EQR projected map image of AVE (PS projected images ofNorthern Hemisphere and Southern Hemisphere are also available only for cryosphereproducts).
.
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1/24deg Global Bin [EQA 1-Dim.](Terrestrial, Cryosphere, Ocean L3
Spatial binning: 1D) [G4A]

1/24deg Global Bin [EQA 1-Dim.](Terrestrial, Cryosphere, Ocean L3
Temporal binning: 8D, 1M) [G6A]

Input:
L3Bin (1D)

1/12deg Global Bin [EQA 1-Dim.](Atmosphere L3 Spatial binning:1D) [G4A]

1/12deg Global Bin [EQA 1-Dim.](Atmosphere L3 Temporalbinning: 8D, 1M) [G6A]

Input:
L3Bin (8D, 1M)

G7A: Map projection process (EQR (and PS for Cryos.))
1/24deg Global Map [EQR](Terrestrial, Cryosphere,Ocean L3 Map: 1D, 8D, 1M)
01D

08D

01M

EQR

1/24deg Northern and Southern
1/12deg Global Map [EQR]
Output:Hemisphere Map [PS](Atmosphere L3 Map : 1D,L3Map (1D, 8D, 1M)(Cryosphere L3 Map : 1D, 8D,8D, 1M)
1M)
08D
01M
01D
01D
08D
01M
01
08
01
EQR
NPS&SPS

Fig. 1 Flow of the G7A L3 Map projection processing
2.2 Definition of the statistics employed in the G5A and G6A processing
Equations for calculating “AVE” and “RMS” are the followings;
AVE =1𝑛𝑛∑𝑖𝑖 𝑥𝑥𝑥𝑥
1RMS = � (∑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“QA_flag” currently stores flags for discriminating land and water. In future update,
QA information for GVs will be included.

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2.4. Sample images
Figure 2 show sample images of L3 Map projection.

Fig. 2a L3 map image of water cloud effective radius (CERW)

Fig. 2b L3 map image of cloud fraction of all type (CFRA)

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Fig. 2c L3 map image of cloud top height (CLTH). Fig. 2d (left) &2e (right) L3 map image of snow and ice cover extent (left: SICE) andsnow and ice surface temperature (right: SIST) in the Northern Hemisphere
3. Current status and remaining issues of the G7A code implementation
The G7A 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.

9Appendix 1: Equations for polar stereographic projection
Geographic coordinate (latitude: ϕ, longitude: λ) are calculated from map coordinates (horizontal:u, vertical: v) with the following equations,√𝑣𝑣 2 +𝑢𝑢2𝜋𝜋

ϕ = NS∙ � − 2 tan−1 �2𝑢𝑢

λ = λ0 - NS∙ tan−1 � �𝑅𝑅��

Eq. (1)
Eq. (2)𝑣𝑣

where NS is a north-south identification factor (1 for northern and -1 for southern), λ0 is the standard
longitude (the longitude line which is parallel to the grid's y-axis (v-direction), along which latitude
increases), and R is a scale factor (1000).
Fig. A1 indicates the map coordinates of the four corners (not the center of the pixel but the cornerof the pixel) of a Northern PS projected image. Spatial resolution is 1/24 deg. The array size is3500pixels × 3500 lines.

-135, +6.032568+135, +6.032568

PS
3500 × 3500
-45, +6.032568+45, +6.032568
Fig. A1 Map coordinates of the four corners of the polar stereographic projection area.
For comparison purpose, Fig. A2 indicates the map coordinates of an EQR image.

+180, +90-180, +90

EQR8640 x 4320
+180, -90-180, -90
Fig. A2 Map coordinates of the four corners of the equirectangular projection area.
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