ATBD for
GCOM-C/SGLI Sea Surface Temperature (SST)
Yukio Kurihara
Triple-i co., ltd., Tokyo, Japan1Introduction

Sea surface temperature (SST) is an important geophysical parameter connected to heat flux at the airsea interface. SSTs are retrieved from the split window data gathered with the Second-generation GlobalImager (SGLI) aboard the Global Change Observation Mission-Climate (GCOM-C) Shikisai satellite(Fig. 1). The GCOM-C satellite, that aims the monitoring of geophysical parameters related to theglobal climate system, flies on a sun-synchronous orbit at the altitude of 798 km and obtains global data
every 2∼3 days (Table 1)[1]. This document presents technical information on SGLI SST, i.e., data inSection 2, algorithms in Section 3, preliminary validation results in Section 4, and limitations of thealgorithm in Section 5.

2

Data

SGLI has switchable resolution from 250 m to 1 km, and the swath widths are 1,150 km for VN- and Pchannels and 1,400 km for SW- and T- channels. Table 2 shows the SGLI data used for SST retrieval.
Coefficients for SST determination were calculated using simulated SGLI data that was generated with
the Radiative Transfer for TOVS (RTTOV) 10.2: an RTM developed by the Numerical Weather Prediction Satellite Application Facility (NWP SAF) of the European Organisation for the Exploitation ofMeteorological Satellites (EUMETSAT)[9]. The NWP data used for the input to RTTOV was provided
by JMA. Cloud masking uses SST analysis: the Merged Global Daily SST (MGDSST) provided by theJapan Meteorological Agency (JMA)[5, 8].

3Algorithm

The algorithm for SGLI SST can be divided into two components: SST determination and cloud masking.
The SST determination determines SSTs for all pixels, and the cloud masking examines the determined
SSTs for cloud contaminations and decides the quality level (QL) for each data.
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.

1

Figure 1. 8-day composite of daytime SGLI SST (2018.10.01-08)

Table 2
SGLI data for SST retrievalCh.
VN1
VN2
VN3
VN4
VN5
VN6
VN7
VN8
VN9
VN10
VN11P1P2
SW1SW2SW3SW4T1T2

λ[nm]
380
412
443
490
530
565
673.5
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
nono
no
no
no
no
no
yes
no
no
no
nono
no
yesno
no

SST
determination
nono
nononono
nono
nono
nonononononono250 / 500 / 1000
250 / 500 / 1000

yes
yes

yes
yes2

Table 3
LUT for SST coefficients
Dim.
123

3.1

Parameter
T1
T1 − T2
Satellite zenith angle

Interval
1.0 K
0.1 K
10.0 deg.

SST determination

SST algorithm is based on the method developed for Himawari-8 (H8) SST[3, 4]. The method for H8
SST was reviewed and simplified for split-window data.
SST is determined by solving the equations:
Is = Is0 + OIs0 · (I − I0 ) ,(1)1hc
,·  2Kλ1 ln 2hc+15(2)

Tb =

λ1 Is

andTs =nX

ck Tb k .

(3)

k=0

Here, Ts on the left side of Eq. (3) denotes the determined SST. Is on the left side of Eq. (1) isthe surface blackbody radiance at the T1 channel, and I on the right side denotes a vector defined by
I = (I1 , d(I1 , I2 )). I1 and I2 denote the radiances at T1 and T2. d(I1 , I2 ) shows the difference between I1
and I2 : I1 − I2 0 ; where, I2 0 denotes the radiance translated from I2 by the conversion of the unit for T2
to the unit for T1. O denotes the difference operator: O = ∂I∂1 , ∂d(I∂1 ,I2 ) , and the centered dot between
OIs0 and (I − I0 ) denotes the dot product. The subscript “0” indicates the initial data. Initial values andthe difference operators were calculated using simulated SGLI data, and compiled into a three-dimension
look-up-table (LUT) (Table 3). I0 and Is0 nearest to I, which is given by SGLI data, are used for SST. Eq.
(2) is the inverse of the Planck function, where λ1 denotes the central wavelength of the T1 channel. Eq.
(2) gives a monochromatic blackbody temperature. Eq. (3) translates the monochromatic temperature
to the temperature corresponding to I1 and I2 : integral values of weighted monochromatic radiances,where the weights are given by the relative spectral response (RSR).

3.2

Cloud masking

Fig. 2 shows the process flow for detection and masking of clouds. The cloud masking method consistsof two steps: the step for the initial clear/cloud classification and the step for the decision of the quality
level (QL) which gives information on clear/cloudy. The initial step classifies the determined SSTs intoclear, cloudy, or indeterminable with some threshold tests. The second step gives each SST a QL fromgood to unknown that depends on the initial classification and the cloud probability. Cloud probabilities
are calculated based on Bayesian.
3.2.1

Initial classification

The smoothness test divides SSTs into some continuous coarse/smooth cells depending on their uniformity. The SST front test redistribute the SSTs in and around the SST fronts, where SST uniformity
is very low, from coarse to smooth. After the smoothness and front tests, the data in coarse cells areconsidered as cloudy; however, clear/cloudy is still undeterminable about the data in smooth cells. Thethermal test determines clear/cloudy of smooth cells by the comparison with analyzed SSTs. If over 10% of SSTs in a cell are within 2 K from the SST analysis, the cell is classified as clear, and if not, thecell is classified as cloudy. SSTs, those no analyzed SSTs are available for or are very close to land, areclassified as indeterminable. Table 4 shows relative frequencies of the data in each class at each absolute3Figure 2. Cloud masking flow

4Table 4
Relative frequency

Class
Clear
Cloudyabs(SGLI SST − buoy)< 0.5 < 4.0
≥ 4.0
0.84
0.15
0.01
0.24
0.25
0.51

Table 5Thresholds for QL

QL
Good
Acceptable(possiblycloudy)
CloudyUnknown

Initialclassification
Clear
Clear
CloudyIndeterminable
Clear
CloudyIndeterminable
Indeterminable
Indeterminable

Daytime
< 0.2
< 0.8
< 0.1
< 0.1
≥ 0.8
≥ 0.1
≥ 0.1
≥ 0.8
< 0.8

Nighttimeall−< 0.2
< 0.2−≥ 0.2≥ 0.2−allDescriptions

no SST analysis
no SST analysis

differences from buoy data. Relative frequencies for indeterminable SSTs are not available by no buoy
data for the comparison.
3.2.2

QL decision

The final decision of clear/cloudy is made by using the initial classification result and the cloud probability
calculated based on Bayesian (for example, [7]). VN8, SW2, and analyzed SSTs are used for the cloud
probability calculation. VN8 and SW2 are available only for daytime. Analyzed SSTs are not used forall data in initial clear cells. This is because analyzed SSTs often include large errors, especially around
SST fronts, and cause detection of false clouds. Probability Density Functions (PDFs) are generatedusing statistics derived by the comparison of SGLI SST and buoy data. Relative frequencies in Table
4 are used for the prior probabilities. Table 5 showes thresholds for the decision of QL: good (clear),acceptable (possibly cloudy), cloudy, or unknown (unknown quality). These thresholds were arrived atempirically. QL is provided with retrieved SST by the QA flag (Table 6). Note that aerosol and sea ice
are currently not taken into account for QL.
Table 6
QA flag
Bit012345678910111213
14
15

Description
No dataLand
Rejected by QC
Retrieval error
No data (TIR1)
No data (TIR2)
reserved
reserved
1: daytime, 0: nighttime or no visible data
reserved
reserved
Indeterminable clear/cloudy
CloudyAcceptable (possibly cloudy)Good
1: Reliable, 0: Unreliable (inland/too close to land)

5

1818

14
1210

rmsd
bias
medianstd
rstd
noutlierclear%

16

Relative frequency (%)

Relative frequency (%)

0.3547−0.0828−0.1218
0.34500.25001419
019.6974

rmsd
bias
medianstd
rstd
noutlierclear%

1686141210

0.4020−0.1134−0.1547
0.38590.30291334
015.6279

86442200−4−3−2−10

123−44−3

Satellite SST − in−situ (K)−2−101234

Satellite SST − in−situ (K)DaytimeNighttime

Figure 3. Relative frequency of the differences between SGLI and buoy data. Figures show the statisticsfor SGLI SSTs for September 2018. SSTs flagged as good were chosen for the comparison. The matchup
window size is 1 hr and 3 km.

Satellite SST vs. buoy (daytime)
DaytimeSatellite−buoy

210−1Satellite SST vs. buoy (nighttime)−2010

Satellite−buoy

20Nighttime

3040504050

Satellite Zenith Angle (°)

210−1−20102030

Satellite
Zenith AngleAngle (°)
SatelliteZenith(◦ )Figure 4. Box plot for the differences between SGLI and buoy data. The horizontal line in the box
indicates the second quartile (the median), and the upper and lower boundary of the box denotes the firstand third quartile. The ends of the range-line show the highest and lowest data within 1.5×interquartile
range (IQR): the first quartile subtracted from the third quartile. Outliers out of 1.5 IQR are shown bycircles[6].

4Validation

SGLI SSTs retrieved for September 2018 were validated by the comparison with buoy data. The buoy
data were downloaded from the in-situ SST quality monitor (iQuam) of NOAA[2, 10]. Each buoy data
was matched with the nearest SGLI SST within a temporal and spatial window of 1 hr and 3 km centeredat the buoy location. Matchups for SGLI SSTs with QL of good were chosen for the comparison. Results
are shown in fig. 3 and 4.

5Limitation• Seasonal biases are possibly included in SGLI SSTs. This is, not limited to SGLI, likely to becaused by less sensitivity of the split window data to the water vapor variation. However, further
examinations will be required to conclude this.
• Daytime SSTs, SSTs retrieved from the data gathered on the descending orbit, are possibly affectedby the diurnal warming. As well as daytime SSTs, nighttime SSTs can also be affected by the diurnalwarming, because the affects of the diurnal worming can remain until around midnight.
• Cloud masking only based on thermal information is less sensitive to low uniform clouds. Cloudcontaminations are likely to be included for high latitudes in nighttime.

6• False cloud detections are possible around some kind of SST fronts those are difficult to detect withthe current front test.

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