GCOM-C/SGLI Sea Surface Temperature (SST)ATBD
Yukio Kurihara
JAXA/EORC
Ver. 2.0: Jun. 2020∗

1Introduction

Sea surface temperature (SST) is an important geophysical parameter associated with heat flux at theair-sea interface. JAXA provides an SST product generated from the data obtained with the Secondgeneration Global Imager (SGLI) onboard the Global Change Observation Mission -Climate (GCOM-C)
satellite1 (Fig. 1). This document presents the technical background of the SGLI SST product, whichis available at JAXA’s G-Portal2 . Data is outlined in Section 2, the algorithm is presented in Section
3, and its validation result is provided in Section 4. Lastly, currently recognized issues are presented inSection 5.

2Data

2.1

SGLI data

SST is determined with the split-window data obtained by SGLI onboard GCOM-C (Tables 1 and 2).
The spatial resolution of SGLI can be switched between 250 m and 1 km. SGLI is used to observe lands∗ Table A1 of change histories
1 https://suzaku.eorc.jaxa.jp/GCOM C/index.html
2 https://gportal.jaxa.jp/gpr/Figure 1. 8-day composite of SGLI SST at daytime from 1 to 8 Oct. 2018.

1

Table 1GCOM-C specifications
LaunchLaunch Vehicle
WeightPower
Design Life
Orbit
Altitude
Inclination
Equator crossing local time (descending)

23 December 2017 from Tanegashima Space Center
HII-A
2,000 kg
4 kw
5 years
Sun-synchronous
798 km
98.6 degrees
10:30 ± 15 min.

Table 2
SGLI data for SST retrieval
Ch.
VN1
VN2
VN3
VN4
VN5
VN6
VN7
VN8
VN9
VN10
VN11P1P2SW1SW2SW3SW4T1T2

λ[nm]380412443490
530565673.5
673.5
763868.5
868.5670
865
1050
1380
1640
2210
[µm]
10.8
12.0

IFOV
[m]
250 / 1000
250 / 1000
250 / 1000
250 / 1000
250 / 1000
250 / 1000
250 / 1000
250 / 1000
250 / 1000
250 / 1000
250 / 1000
1000
1000
1000
1000
250 / 1000
1000

Cloud
masking
nono
no
no
no
no
no
yes
no
no
no
no
no
no
yes
yes
no

SST
determination
nono
no
no
no
no
no
no
no
no
no
no
no
no
no
no
no

250 / 500 / 1000
250 / 500 / 1000

yes
yes

yes
yes

and near-shore ocean areas with 250-m resolution and to observe open ocean with 1-km resolution. The
swath width is 1,150 km for the VN and P channels and 1,400 km for the SW and T channels.

2.2

Ancillary data

The Merged Global Daily SST (MGDSST) is used in cloud detection at nighttime. MGDSST is objectively
analyzed daily SST data provided by the Japan Meteorological Agency (JMA).

3Algorithm

The algorithm is divided into two parts: the SST determination and the quality level (QL) decision. The
first component determines SSTs for all pixels except for land. The second component assignes a QL toeach determined SST.

3.1

SST determination

The SST algorithm is based on the quasi-physical method developed for Himawari-8 SST3 [Kuriharaet al., 2016, Yang et al., 2020]. The formulas are
Is = Is0 + a (I − I0 ) ,3 https://www.eorc.jaxa.jp/ptree/index.html

2(1)Table 3
Bin for initial data and coefficients
Coordinate
12Parameter
T1
T1 − T2Interval
1.0 K
0.1 K

Table 4
Relative frequency

Clear
CloudyUnknown

Tb =abs(SGLI SST − buoy)< 0.5 < 4.0
≥ 4.0
0.84
0.15
0.01
0.24
0.25
0.51
−−−hc
1
,·  2Kλ1 ln 2hc+15(2)λ1 Is

andTs =nX

ck Tb k .

(3)

k=0Here, Is is the sea surface blackbody radiance at T1 and the subscript 0 denotes the initial value. I isthe vector of the radiance observed at T1 and T2 and a is the coefficient vector. Eq. (2) is the inverseof the Planck function. Here, λ1 denotes the central wavelength of T1. Eq. (2) converts the radiance tothe blackbody temperature. In the conversion, the radiance is assumed to be the monochromatic. Eq.
(3) translates Tb to SST (denoted by Ts ), allowing for the Relative Sensor Response (RSR) of T1 intoaccount.
The initial values and coefficients were generated for each bin (Table 3) for each satellite zenith
angle, at 10◦ intervals using numerically simulated SGLI data. The simulation was made by performing
the Radiative Transfer for TOVS (RTTOV) 10.2 RTTOV [Saunders et al., 2012] on numerical weather
prediction (NWP) data. RTTOV is a radiative transfer model developed at the Numerical Weather
Prediction Satellite Application Facility (NWP SAF) of the European Organisation for the Exploitationof Meteorological Satellites (EUMETSAT). The NWP data was provided by JMA.

3.2

Quality level

The quality level (QL) is decided for each determined SST in the two steps shown in Fig. 2. In the firststep, threshold tests classify each SST into a clear, cloudy, or unknown class. Table 4 shows the relative
frequencies of the absolute difference (SGLI−buoy) for each clear, cloudy, and unknown class. Then, QL
is decided for each SST based on the first-step results and the cloud probability. QL is described in the10-14th bits of the QA flag (Table 5).
3.2.1

Smoothness test

The smoothness test divides the determined SST fields into coarse or smooth cells based on the spatial
uniformity of the SSTs.
3.2.2

SST front test

The front test examines all SSTs in coarse cells. Whether an SST is a part of a front or not is based on
the SST gradient determined using SSTs in the 3×3-pixel square centered at the target SST. An SST
recognized as part of a front is reorganized as part of a smooth cell.

3STARTnoInitial cloud/clear
classiﬁcation

SST analysis ?

yesno
Far from land ?
yesSmoothness testsmoothSmooth /coarse ?
coarseCoast test (v2)yesClear ?

Front ?

nonoUnknown

Initial cloud probability test

SST front test

yesno
Clear ?
yesCloudy

Clear
QL decision

Cloud probability test

Cloud probability test

Cloud probability test

Acceptable /Possibly cloudy /Unknown

Acceptable /Possibly cloudy /CloudyGood /
Acceptable /Possibly cloudy /CloudyFigure 2. QL decision flow

Table 5
QA flag
Bit
Description
Level-3 mask*
0
Invalid data
11Land1
2
Rejected by QC
1Retrieval error
134Invalid data (TIR1)
1
5Invalid data (TIR2)
1
reserved
67reserved
8
1: daytime, 0: nighttime or no visible data
9Near Land
110Cloudy
111Unknown clear/cloudy
112Possibly cloudy
113Acceptable
14Good15reserved*) The level-3 mask denotes the bits which the level-3 statistics processing
refers to. Data will be used for the level-3 statistics if all of the bits are
zero.

4

Daytime

Nighttime

Figure 3. Relative frequency of the differences between SGLI and buoy data (SGLI−buoy). Statistics arecalculated using the SGLI SSTs (QL=good) for 2019. The collocation window size is 1 hr × 3 km. rmsd:
the root mean square difference, std: the standard deviation, rstd: the robust standard deviation, n: thetotal number of match-ups, outlier: the total number of the match-ups, s.t. |SGLI − buoy| > 4 K, andclear% : the ratio of the match-ups (QL=good) to all match-ups. Note that statistics were calculated forall match-ups including outliers.

3.2.3

Coast test

The coast test was introduced in SGLI SST V.2. The test is performed on the data near land. The datais organized as a part of a smooth cell if a coastline is detected in the SGLI data (SW3 or T1) for the
3×3-pixel square centered on the data.
3.2.4

Initial cloud probability test

Each smooth cell is subjected to an initial test that decides whether the cell is clear or cloudy, basedon the cloud probability calculated for each SST in the cell. The cell is classified as clear if the cloud
probability is lower than 0.3 for over 10 % of SSTs in the cell, otherwise is is classified as cloudy. Cloudprobability is calculated by using VN8 and SW2 for daytime and SGLI- and MGD- SSTs for nighttime.
The cloud probability calculation is based on the Bayesian inference method. [e.g. Merchant et al., 2005].
The probability density functions (PDFs) are generated using the statistics derived from comparisons ofSGLI SST and buoy data. The condition of the data, whether clear, cloudy, or mixed, depends on thedifference between the collocated SGLI SST and buoy data.
3.2.5

QL decision

A QL is decided for each SST based on the cloud probability calculated with the prior probability given
depending on the initial classification result into account. The prior cloud probability is determined by
the relative frequencies shown in Table 4. Different thresholds are given to each initial class for thedetermination of QL.

4Validation

Figs. 3 and 4 show the statistics for SGLI SSTs for 2018 and 2019. The statistics were calculated bycomparing the drifting and moored buoys data with SGLI SSTs having good QLs. Buoy data were
downloaded from the in-situ SST quality monitor (iQuam)4 of the NOAA [Xu and Ignatov, 2014]. Eachbuoy data was compared with the nearest SGLI SST within a spatio-temporal window of 3 km×1 hr,
centered on the buoy.
4 https://www.star.nesdis.noaa.gov/socd/sst/iquam/index.html

5Daytime

Nighttime

Figure 4. Bias and RMSD (2018-2019)5

IssuesFollowing issues are recognized and waiting to be improved in the future.
1. Negative biases possibly dominate at SSTs above 300 K because of lower sensitivity to atmosphericwater vapor.
2. Unstable accuracy of SST front detection possibly generates false clouds at and around SST fronts.
3. Because of insufficient information, cloud contamination can be conspicuous at nighttime.
4. Because of contamination of land, unnatural SSTs may be calculated along land/water boundaries.

References
Kurihara, Y., Murakami, H., and Kachi, M. (2016). Sea surface temperature from the new japanesegeostationary meteorological himawari-8 satellite. Geophysical Research Letters, pages n/a–n/a.
2015GL067159.
Merchant, C. J., Harris, A. R., Maturi, E., and Maccallum, S. (2005). Probabilistic physically based
cloud screening of satellite infrared imagery for operational sea surface temperature retrieval. Quarterly
Journal of the Royal Meteorological Society, 131(611):2735–2755.
Saunders, R., Hocking, J., Rayer, P., Matricardi, M., Geer, A., Bormann, N., Brunel, P., Karbou, F.,and Aires, F. (2012). Rttov-10 science and validation report. EUMETSAT, NWPSAT-MO-TV-023,
page 31.
Xu, F. and Ignatov, A. (2014). In situ sst quality monitor (iQuam). J. Atmos. Oceanic Technol.,31(1):164–180.
Yang, M., Guan, L., Beggs, H., Morgan, N., Kurihara, Y., and Kachi, M. (2020). Comparison ofhimawari-8 ahi sst with shipboard skin sst measurements in the australian region. Remote Sensing,
12(8).

6Table A1
Change HistoryVersion1.0
2.0

DateOct. 2018
Jun. 2020

Description
Cloud masking was improved for coastal areas and inland water. Fig.
2 (cloud masking flow) was modified along with a change to the cloudmasking algorithm. Fig. 3 was updated. Fig. 4 was replaced with atime-series chart of bias and RMSD. Tables 3 (definition of LUT) and 4(relative frequency of clear/cloudy) were modified. Table 5 (thresholdsfor QL) was removed. Table 6 (QA flag) was modified and renamed
Table 5 (Table A2 for track changes). Substantial changes were made todescriptions including notations in Eq. (1).

Table A2
QA flag (track changes)
Bit0123456789101112131415

Description
No data Invalid data
Land
Rejected by QC
Retrieval error
No data (TIR1)
No data (TIR2)
no reserved
no reserved
1: daytime, 0: nighttime or no visible data
no Near Land
no Cloudy
Unknown clear/cloudy
Cloudy Possibly cloudy
Acceptable (possibly cloudy) Acceptable
Good0: Unreliable (inland/too close to land), 1: Reliable reserved

7