GCOM-C/SGLI Sea Surface Temperature (SST)ATBD
Yukio Kurihara
JAXA/EORC
Ver. 3.0: Oct. 2021∗

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). The SGLI SST product version 3 is available at JAXA’s G-Portal2 since 2021. Inversion 3, cloud masking has been improved resulting increases in the total amount of available data indaytime. SST front issues found in previous versions have been improved for daytime SSTs.
This document presents the technical background of the SGLI SST product version 3. 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 in Section 5.
∗ 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 LifeOrbit
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
VN11
P1P2
SW1SW2SW3SW4T1T22Data

2.1

SGLI data

λ[nm]380412443490
530565673.5
673.5
763
868.5
868.5
670
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
no
no
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

SST is determined with the split-window data (T1 and T2) obtained by SGLI onboard GCOM-C (Tables
1 and 2). The short-wave-length infrared data (SW2 and SW3) are used for cloud masking in daytime.
The spatial resolution of SGLI can be switched between 250 m and 1 km. SGLI is used to observe lands
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. The
split-window data are preprocessed for destriping and denoising. This preprocess was introduced in theSGLI SST version 3. The destriping and denoising do not reduce the spatial resolution of the determinedSSTs.

2.2Ancillary 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).

3

Algorithm

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.
2Table 3
Bin for initial data and coefficients
Coordinate12

3.1

Parameter
T1
T1 − T2Interval
1.0 K
0.1 K

SST determination

The SST algorithm is based on the quasi-physical method developed for Himawari-8 SST3 [Kuriharaet al., 2016, Yang et al., 2020]. Followings describe an outline of the algorithm. The detail of the method
is discussed by Kurihara et al. [2021].
The formulas are
Is = Is0 + a (I − I0 ) ,(1)Tb =hc1,·  2
Kλ1 ln 2hc+15(2)λ1 IsandTs =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 zenithangle, 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 based on the results of cloud masking. Dueto the improved cloud masking, the QL decision flow has changed significantly from version 2. Figure 2
shows the cloud masking and QL decision flow. Each test and cloud probability are outlined below. QL
is described in the 10-14th bits of the QA flag (Table 4). Note that quantitative uses are assumed about
the SSTs with QL≥4 and that qualitative uses are assumed about those with QL≥3.
3.2.1

Smoothness test

The smoothness test divides the determined SST fields into coarse or smooth cells based on local uncertainty of the SSTs. Local uncertainty is calculated at each SST pixel from the variance of the SSTgradients.
3.2.2

SST front test

The front test examines all SSTs in coarse cells. An SST recognized as part of a front is reorganized aspart of a smooth cell. In version 3, a new front detection method was introduced for the front test.
3 https://www.eorc.jaxa.jp/ptree/index.html

3START
Cloud/clear
Smoothness(local uncertainty)
test

notestyes

Smooth ?

SST front test

Front ?

yes

Cloud probability

noQL decision
QCCloudy

Good /
Acceptable /Possible cloudy /CloudyFigure 2. QL decision flow

Table 4
QA flag and QL
BitDescription
Level-3 mask* QL
0Invalid data
1
1
Land
1
2
Rejected by QC
1
3
Retrieval error
14Invalid data (TIR1)
1
5Invalid data (TIR2)
1
6
reserved
7
reserved
8
1: daytime, 0: nighttime or no visible data
9
Near Land
10Cloudy
1
111
Unknown clear/cloudy
1212
Possibly cloudy
13
13
Acceptable
414Good (daytime only)
515reserved*) 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 arezero.

43.2.3

Coast test (discontinued)

The coast test introduced for version 2 was removed in version 3. This is because of the cloud probability
calculation improved by replacing VN8 with SW3.
3.2.4

Cloud probability

Cloud probability is calculated for each SST in each smooth cell. The cloud probability is the posterior
probability derived by using prior information: SW2 and SW3 during the day and SGLI- and MGD- SSTs
at night. The cloud probability calculation is based on the Bayesian inference method [e.g. Merchantet al., 2005]. The probability density functions (PDFs) are generated using the statistics derived fromcomparisons of SGLI SST and buoy data. The condition of the data, whether clear, cloudy, or mixed,depends on the difference between the collocated SGLI SST and buoy data.
3.2.5

QL decision

A QL is decided for each SST based on the determined cloud probability. QC is restricted to minimum.
The thresholds for determining QL are different between day and night. Note that the highest QL is“Acceptable” for nighttime SSTs in version 3. This is because the cloud probabilities derived by using
SST analysis are less reliable.

4Validation

Figure 3 shows the statistics for SGLI SST version 3 for 2020. The statistics were calculated by comparing
the drifting and moored buoys data with SGLI SSTs having good QLs. Buoy data were downloaded fromthe in-situ SST quality monitor (iQuam)4 of the NOAA [Xu and Ignatov, 2014]. Each buoy data wascompared with the nearest SGLI SST within a spatio-temporal window of 3 km×1 hr, centered on thebuoy.

5Issues

Following 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. In the nighttime, unstable accuracy of SST front detection possibly generates false clouds at andaround 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.

4 https://www.star.nesdis.noaa.gov/socd/sst/iquam/index.html

516RMSD
BiasMedianSD
RSDN
Outlier
Clear%

Relative frequency (%)

1412100.3815−0.1887−0.17510.3316
0.261576473
120.9769

QL=5864
20−4−3−2−101234

Satellite SST − in−situ (K)1616

Relative frequency (%)

12100.4361−0.2312−0.2001
0.36970.2786102498828.1157

QL≥4RMSD
BiasMedianSD
RSDN
Outlier
Clear%

14

Relative frequency (%)

RMSD
BiasMedianSD
RSDN
Outlier
Clear%

1486421210

0.4008−0.2272−0.22360.33010.265477201
919.1437

QL≥4864200−4−3−2−101234−4Satellite SST − in−situ (K)−3−2−101234

Satellite SST − in−situ (K)DaytimeNighttime

Figure 3. Relative frequency of the differences between SGLI and buoy data (SGLI−buoy). Statistics
are calculated using the SGLI SSTs (QL=good) for 2020. 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: the total number of match-ups, outlier: the total number of the match-ups, s.t. |SGLI − buoy| > 4K, and clear% : the ratio of the match-ups to all match-ups. Note that statistics were calculated for all
match-ups including outliers.

References
Kurihara, Y., Murakami, H., and Kachi, M. (2016). Sea surface temperature from the new Japanesegeostationary meteorological Himawari-8 satellite. Geophysical Research Letters, 43(3):1234–1240.
Kurihara, Y., Murakami, H., Ogata, K., and Kachi, M. (2021). A quasi-physical sea surface temperature
method for the split-window data from the Second-generation Global Imager (SGLI) onboard theGlobal Change Observation Mission-Climate (GCOM-C) satellite. Remote Sensing of Environment,
257:112347.
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. RemoteSensing, 12(8).

6Table A1Change History
Version
1.0
2.0

Date
Oct. 2018
Jun. 2020

3.0

Oct. 2021

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).
Preprocessing for destriping and denoising of TIR data was introduced(Section 2). Cloud masking was improved. Section 3.2, Fig.2, and Table
4 were updated along with the changes to the cloud masking algorithm.
Fig. 3 was updated. Fig. 4 was removed.

Table A2
QA flag (track changes)
Bit0123456789101112131415

Description
Invalid data
Land
Rejected by QC
Retrieval error
No data (TIR1)
No data (TIR2)
reservedreserved
1: daytime, 0: nighttime or no visible data
Near Land
CloudyUnknown clear/cloudy
Possibly cloudy
Acceptable
Good (daytime only)
reserved7