Algorithm Theoretical Basis Document

GCOM-C/SGLI Level-3 Spatial Binning(G5A)

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 G5A algorithm is to take spatial statistics of SGLI Level-2 (L2)daily geophysical variable (GV) products reducing the spatial resolution to 1/24 deg.
(land, cryosphere, and ocean) or 1/12 deg. (atmosphere).
1.2 Development strategy
The G5A 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 output of theG5A processing contains the same statistics variables except for the case of snow and ice
cover extent (SICE) and cloud type (CLTYPE) in cloud properties (CLPR) product (seenext sub-section). The SICE and CLTYPE are flag products and the statistical outputsof these two GVs are customized so that the number of snow/ice cover observation 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 GVs quite easy, theGV names of the statistical targets are defined in an external text file so that thechange could be made without compiling the code.
1.3 Processing targets and outputs
Processing targets of the L2 GV statistics are all SGLI products for land, cryosphere,ocean, and atmosphere. Input and output variables are summarized in Table 1.
Table. 1 List of the target products, GVs, and output types of the G5A processing
Input

Long Name

GeophysicalOutput

OutputVariables (GVs)

Product ID

Variables*1

Land surface

Rs_VN01

RV01

AVE, RMS,

reflectance

Rs_VN02

RV02

Nused, Ninput,

Rs_VN03

RV03

MIN, MAX,

Rs_VN04

RV04

Date, QA_flag

Rs_VN05

RV05

Rs_VN06

RV06

Rs_VN07

RV07

Rs_VN08

RV08

Rs_VN09

RV09

Rs_VN10

RV10

Rs_VN11

RV11

Rs_SW01

RS01

Rs_SW02

RS02

Product ID
RSRF3Rs_SW03

RS03

Rs_SW04

RS04

Rs_TI01

RT01

Rs_TI02

RT02

Rs_VN08P

RN08

Rs_VN11P

RN11

Rs_PI01

RP01

Rs_PI02

RP02

Sensor_azimuth*3

GEOV

Sensor_zenith
Solar_azimuth*3
Solar_zenith
Sensor_azimuth_PL*3 GEOP
Sensor_zenith_PL
Solar_azimuth_PL*3
Solar_zenith_PL
Sensor_azimuth_IR*3 GEOI
Sensor_zenith_IR
Solar_azimuth*3
Solar_zenith
VGI_

LAI_
AGB_
LST_Vegetation index

NDVI

NDVI

EVI

EVI_SDISDI_LAI

LAI_

FAPAR

FPAR

Above-ground

AGB

AGB_biomass

VRI

VRI_Land surface

LST

LST_Same as above

Snow and ice

SGSLSGSL

Same as above

physical

SIST

SIST

SICE

SICE

Leaf area index

Same as above

Same as above
Same as above

temperature
SIPR

properties
SICE

Snow and ice
cover extent

Nsnow1, Nsnow2,
Nsnow3, Nused,
Ninput, Date,
QA_flag

CLPR

Cloud properties

CLTYPECFRX*2

Ncfrx*2, Nused,
Ninput, Date,
QA_flag

CLTT

CLTT

AVE, RMS,
4

ARNP

ARPL

SSTD

CLTH

CLTH

Nused, Ninput,

CLOT_W

COTW

MIN, MAX,

CLER_W

CERW

Date, QA_flag

CLOT_I

COTI

Aerosol properties AROT_ocean

AOTO

by NP

AROT_land

AOTLARAE_ocean

AAEO

ARAE_land

AAEL

Same as above

Aerosol properties AROT_pol_land

AOTP

by PL

ARAE_pol_land

AAEP

ARSSA_pol_land

ASSA

SST

SST__

Same as above

SST

SST_Same as above

Normalized waterPAR

PAR_Same as above

leaving radiance,

NWLR_380

L380

aerosol

NWLR_412

L412

parameters and

NWLR_443

L443

photosynthetically NWLR_490

L490

available

NWLR_530

L530

radiation

NWLR_565

L565

NWLR_670

L670

TAUA_670

T670

TAUA_865

T865

In-water

CHLA

CHLA

properties

TSM

TSM_CDOM

CDOM

Sea surface

Same as above

temperature(Day)SSTN

Sea surface
temperature(Night)

NWLR

IWPR

Same as above*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 Output of CLTYPE statistics in the descending orbit is cloud fractions for individual cloud types.
The “CFRX” and “cfrx” denotes the cloud fraction of type “x” which is the ID number of the ISCCP
cloud classification. For example, in case of “cirrus cloud” the ISCCP ID is “1” and thus the output
variable named as “Ncfr1” is stored in the output file with product ID of “CFR1”. In the ascending orbit
cloud fraction is estimated for three layer of High, Middle, and Low, the results of which 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 Sensor and Solar azimuth angles are firstly converted to absolute relative azimuth angle (araz)defined as araz = | sensor_azimuth – 180.0 – solar_azimuth | and then its statistics (AVE, RMS, MAX,MIN) are calculated and stored in the output file.

5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 inTable1. 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 G5A L2 GV statistics processing. Spatial coverage
and resolution of input files can differ depending on the observation fields. That is, forthe processing of land and cryosphere products, the input files are the L2 daily tile GV
products with spatial resolution of 250m/1km (land) and 1km (cryosphere). For theatmosphere, the L2 daily global-bin GV files with a resolution of 1/24 deg. are the input.
For ocean products, L2 scene GV products with spatial resolution of 1km can be inputs.
The output of G5A processing contains daily spatial statistics of GV(s) which are stored
in one dimensional array in the output file. The number of output files depends on theGV contents in the input file. In case that an input file contains multiple GVs, the G5A
process generates output files for every GVs separately (except for the cases of geometry
data in RSRF). For example, the “VGI_“ product contains three GVs of NDVI, EVI andSDI as shown in Table 1. The outputs of the G5A processing for VGI_ are three files withthe following product IDs, i.e., NDVI, EVI_, and SDI_.

Input:
1/480deg (250m) Tile [EQA](RSRF, VGI, LAI, LST)

L2 Daily Products (Tile, Global Bin, Scene)

1/120deg (1km) Tile [EQA](Terrestrial AGB, Cryosphere L2)

1km Scene(Ocean L2)

1/24deg Global Bin [EQA](Atmosphere L2)

G5A: Spatial binning process (one output file for each GV in one input file)

Output: L3Bin (1-Day)
1/24deg Global Bin [EQA 1-Dimension](Terrestrial, Cryosphere, Ocean L3 Spatial binning: 1D）

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

1-dim. array

Fig. 1 Flow of the G5A L2 GV statistics processing
6

2.2 Definition of the statistics
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. However, always “1” is stored in this L3Bin-Daily product.
2.3 QA process“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 G5A (andG6A) 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 G5A output.
Land/CryosphereTile
250m/1km

Ocean
Scene
1km

Atmosphere
Global-Bin
1/24deg.

Input: L2 Daily GVs

Output: L3 Daily Spatial Binned GVs

Fig. 2 Input and output of the G5A processing.
73. Current status and remaining issues of the G5A code implementation
The G5A process works well without system errors. Processing speed and memory
size are also within the expectations except for the cases of parts of ocean scene
processing. Remaining tasks are the implementation of the QA_flag for the output ofquality assurance information and the further speeding up of the ocean scene
processing.

8