Ver.3 Atmosphere Products1Evaluation SummaryProductReleasethresholdStandardaccuracy

Targetaccuracy

Status*1

10% (withCloud
whole-skyflag/Classification camera)

Incl. below cloud Incl. below cloudamountamount☆

Classified cloud
fraction

20% (on solarirradiance)

15% (on solarirradiance)

10% (on solarirradiance)☆

Cloud top
temp/height

1K

3 K/2 km (toptemp/height)

1.5 K/1 km(temp/height)◎Water cloudOT/effectiveradius
Ice cloud optical
thickness
Aerosol overthe
oceanLand aerosol bynear ultra violet

Comparison with in-situ observation (sky-camera images)
for release threshold. Evaluations for standard and target
accuracies were performed as the Classified cloud fraction
products.
Comparison of SGLI-derived solar irradiance using cloud
products including cloud flag, cloud fraction etc. withground-measured solar irradiance.
Evaluation was made as vi-cal. of SGLI TIR bands for therelease threshold. In addition, comparison with othersatellite data for evaluating the achievement of thestandard accuracy.

10%/30%(CloudOT/radius 100% (as cloudliquid water))

50% / 20%○ → ◎ Comparison with in-situ observation.

30%

70%

20%

○ → ◎ Comparison with in-situ observation.

0.1 (Monthlya_670,865)

0.1 (scenea_670,865)

0.05 (scenea_670,865)○ → ◎ Comparison with in-situ observation.

0.15 (Monthly
a_380)0.15 (scenea_380)0.1 (scenea_380 )◎→Aerosol overthe landAerosol byPolarization→Merged to
ARNP

Evaluation Methods

0.15
0.15 (scene(Monthlya_670,a_670,865)
865)

0.1 (scenea_670,865)◎Comparison with in-situ observation.

Comparison with in-situ observation and other satellite(MODIS) data.

*1 Symbols denote as follows; 〇: the release threshold achieved, ◎: the standard accuracy achieved, ☆: the target accuracy achieved.

2CLFG (CLoud FlaG)■Algorithm PI : Takashi Nakajima■Implement + V3 update : Kazuhisa Tanada■Problems of V2 CLFG
Misclassification

Impact①day： snow（〇）→land clear（×）

Overestimation in ARNP/AOT②day： heavy aerosol（〇）→cloud（×）

Decreasing effective pixels in ARNP

③night： snow、clear（〇）→cloud（×）

Decreasing effective pixels in LST■Method of V3 CLFG
Method：DNN（Deep Neural Network）
Ground truth：made by checking the RGB & SIPR images
Input data：
day…lat,VN01,03,04,06,09,11,SW03,TI01,02,elavation,date (12 data)night…lat,TI01,02,elavation,date (5 data)
Output labels：
day…land, ocean, cloud, aerosol, snow (5 labels)night…land&ocean, cloud, aerosol, snow (4 labels)

LTOA

Input

CLAUDIACurrent
algorithm

DNN

Added
algorithm

V3 CLFG

Output3CLFG (CLoud FlaG)■Training dataset■Input parameter selection
Spectrum

We used 500,000 pixels, various locations and periods.

■Training results
Regression
Coefficient

AccuracynightdayWe chose the SGLI channel which has large coefficient.

Corrected pixels / Total pixels---------------------------------------------------------------------------------------------------------------

Total Acc = 99.78% 5226469/5238201
land Acc = 99.67% 1230057/1234083
ocean Acc = 99.93% 811784/812343
cloud Acc = 99.84% 1362311/1364443
aerosol Acc = 99.70% 958309/961198
snow Acc = 99.75% 864008/866134

4

CLFG (CLoud FlaG)■Results
We partially solved these issues of V2 CLFG and improved it.
The CLFG product is expected to remain on the target accuracy, because V2 CLAUDIA algorithm iscontinuously used for the area where there are no misclassifications.
RGBFire smoke(heavy aerosol)snow

V2CLFG
clear

cloudsnow

aerosol

V3CLFG

Validation result

Release accuracy[Ver.1/Ver.2] 10.2 %10%
(with whole-sky camera)

Standard accuracy

Target accuracy

15%10%(Evaluate with cloud fraction on (Evaluate with cloud fraction
solar irradiance)on solar irradiance)

The standard accuracy is expected to be achieved.

5(Appendix) CLFG at nightClear pixels increase by ~ 20% at night (however, still few at high latitudes)

V2 V3
20200101A

20200401A

20200701A

20201001AV2V36CLPR (CLoud PRopertis)
・Major changes- Algorithm has not been changed- Screening the validation data with strictly quality check・Validation method- Comparing with the in-situ observation of sky radiometers- Using the average values of SGLI’s 5x5 pixels around the in-situ site
- Only using the in-situ data observed within +/-30 minutes of SGLI observation time.
- Data period is July 2019 – December 2020

Water Clouds

Ice Clouds

Validation result

Release accuracyStandard accuracy

Target accuracy

Water cloud
Optical thickness: 8-9%(vs. MODIS)
Effective radius: 2-5%(vs. MODIS)
Cloud liquid water: 82.07%(in-situ)（with other satellite)
10%30%-(with in-situ)
100%（with in-situ）
20%
50%

Ice cloudOptical thickness: 55.99%(in-situ)（with other satellite）
30%（with in-situ）
70%（with in-situ）
20%

The standard accuracy is expected to be achieved.

7ARNP(AeRosol properties using Numerical Prediction)
Atmosphere Product-Aerosol over Land and Ocean
 Algorithm (RESTEC Yoshida)• Use the forecast from aerosol assimilation system of Himawari8/AHI as a priori estimate of retrieval.
 Major improvement from Ver.2• Utilize both polarization and non-polarization observation• Consider the error covariance of surface reflectance• Apply V3 cloud flag (retrieval of heavy aerosol)• Add the estimated uncertainties of aerosol optical thickness,
angstrom exponent, and single scattering albedo• Integrate output over ocean and land
 Channels used
• Land：VN01, VN02, VN03, VN05, VN08, P02• Ocean：VN10, SW01, SW03, SW04, P02
8

ARNP: Validation methods• Method:• Standard accuracy: RMS errors are evaluated comparing SGLI derived AOT
with those from In-situ data (scene by scene)• In-situ data（１）Land：Sky-radiometer• Source: Irie PI@Chiba univ. (SKYNET), Aoki PI@Toyama univ., JMA, AERONET*1• Period：March 27, 2018 ～ November 30, 2020
（V2: March 2018, June 2018, Feb. 2018, Jan. 2020, Feb. 2020）（２）Ocean: Microtops on ships• Source：AERONET - Maritime Aerosol Network*1• Period： March 27, 2018 ～ July 1, 2020
QA flags in V3（V2: March 2018, June 2018, Feb. 2019）*1 Aerosol Robotic Network(Giles et al., 2019; O’Neill et al., 2003),
https://aeronet.gsfc.nasa.gov/• Matchup conditions• Observed within 30 minutes• SGI AOT is averaged within 10km fromin-situ site, where the valid pixel islarger than 50%, and the standard
deviation is less than 0.1• QA_flag is considered for bit
0,2,3,5,9,10,12,13,15

9

ARNP: Validation results over land
AOT over land@380nm（Ver. 2）

AOT over land@380nm（Ver. 3）

Averaged SGLI within 10km vs. Sky
radiometer at the same time

Averaged SGLI within 10km vs. Sky
radiometer at the same time

R=0.862
Bias =0.092
RMSE =0.137
Data num=1058

Product

Estimated RMSE

Standard accuracy(L+5)

Target accuracy(L+5)

AOT over land

0.137（Scene）

0.15 （Scene）

0.10 （Scene）• More validation sample number than V2• Standard accuracy will be archived
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ARNP: Validation results over ocean
AOT over ocean@670nm（Ver. 2）

AOT over ocean@670nm and 865nm（Ver. 3）

Averaged SGLI within 10km vs. shipborne
microtops at the same time

Averaged SGLI within 10km vs. shipborne
microtops at the same time

865nm

670nm

670nm

R=0.942
Bias =0.048
RMSE =0.072
Data num=30

R=0.956
Bias =0.027
RMSE =0.051
Data num=30

Product

Estimated RMSE

Standard accuracy(L+5)

Target accuracy(L+5)

AOT overocean

0.072@670nm
0.051@865nm（Scene）

0.1 （Scene）

0.05 （Scene）• More validation sample number than V2• Standard accuracy will be archived
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ARNP: Summary

Evaluation
MethodProductPeriod

AOT_ocean@670,865

2018/3/27～
2020/11/30

In-situ(Scene)AOT_land＠380

2018/3/27～
2020/11/30

In-situ(Scene)Estimated
RMSE

ReleaseStandard

0.072@670nm
0.051@865nm 0.1 （Month） 0.1（Scene）（Scene）
0.137（Scene）

0.15 (Month）

0.15（Scene）

Target0.05（Scene）

0.10（Scene）

The standard accuracy is expected to be achieved.

12

(Appendix) Site maps of ARNP validation data

Blue: Ocean
Red: Land

13

(Appendix) ARNP Image (global)
Sep. 7 2020 (California Forest fire)
V2（ARNPF）
oceanland
AOT

AOTAEAESSA

V3（ARNPF）

P2 polarization reflectanceSSA

14

(Appendix) ARNP Image (Tile)
Sep. 7 2020 (California Forest fire)
AOTAE

SSA

V2（Land）

V3（ARNPK）

15