ABI Green band Generation using GOES-16 and

Himawari-8 observation data

Dec. 06 2019

Sejong University

Jeongeun Park and Sungwook Hong

Contents

1. Introduction

2. Data

3. Method

4. Result

5. Summary & Conclusion

1. Introduction

Bands GOES-R/ABI Himawari/AHI Resolution (km)

Central Wavelength (μm)

Blue 0.47 0.47 1

Green N/A 0.51 1

Red 0.64 0.64 0.5

Near IR 0.86 0.86 2

1.38 N/A 2

1.61 1.61 2

2.25 2.26 2

GOES-16/ABI vs. Himawari-8/AHI

Motivation

H-8 G-17 G-16

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• Synthetic green band generated from Red, Blue and Veggie bands

𝑹𝒈𝒓𝒆𝒆𝒏 = 𝟎. 𝟒𝟓 × 𝑹𝒓𝒆𝒅 + 𝟎. 𝟏 × 𝑹𝒗𝒆𝒈𝒈𝒊𝒆 + 𝟎. 𝟒𝟓 × 𝑹𝒃𝒍𝒖𝒆 (Bah et al. 2018)

Synthetic Green Band

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2. Data

2017.01.27

00:00 UTC

2017.02.27

00:00 UTC

2017.03.27

00:00 UTC

2017.04.27

00:00 UTC

2017.05.27

00:00 UTC2017.06.27

00:00 UTC

2017.07.27

00:00 UTC2017.08.27

00:00 UTC

2017.09.27

00:00 UTC

2017.10.27

00:00 UTC

2017.11.27

00:00 UTC

2017.12.27

00:00 UTC

Satellite Data

2018.08.27

00:00 UTC

00:10 UTC

2018.09 2018.10 2018.11 2018.12 2019.01 2019.02 2019.03 2019.04 2019.05

00:00 UTC

00:10 UTC

2018.09.27

18:30 UTC

20:30 UTC

2018.08.28

18:30 UTC

20:30 UTC

1. Training Dataset (242 days) – AHI (blue & green)

2. Validation Data (12 days) – AHI (blue & green)

3. Test Data (55 days) – ABI (blue(O), green(X))

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3. Method

Central

Wavelength

(µm)

2017

01.28

2017

02.28

2017

03.28

2017

04.28

2017

05.28

2017

06.28

2017

07.28

2017

08.28

2017

09.28

2017

10.28

2017

11.28

2017

12.28

0.47 0.9993 0.9992 0.9993 0.9993 0.9993 0.9992 0.9993 0.9992 0.9992 0.9991 0.9992 0.9992

0.51 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

0.64 0.9958 0.9959 0.9958 0.9961 0.9964 0.9960 0.9963 0.9962 0.9971 0.9969 0.9972 0.9971

0.86 0.9927 0.9927 0.9934 0.9929 0.9928 0.9934 0.9922 0.9925 0.9919 0.9915 0.9931 0.9933

1.61 0.9791 0.9722 0.9732 0.9797 0.9757 0.9768 0.9707 0.9789 0.9748 0.9780 0.9797 0.9823

2.26 0.9851 0.9840 0.9843 0.9823 0.9858 0.9877 0.9812 0.9853 0.9807 0.9807 0.9819 0.9870

3.89 0.9297 0.5893 0.8896 0.9355 0.9246 0.9332 0.9252 0.9383 0.9335 0.9348 0.9324 0.9215

6.24 0.8410 0.5486 0.8114 0.8347 0.8006 0.8155 0.8162 0.8014 0.8244 0.8185 0.8370 0.8330

6.94 0.7959 0.5456 0.7854 0.7899 0.7639 0.7892 0.7853 0.7765 0.8042 0.7712 0.8054 0.7956

7.35 0.7518 0.5328 0.7404 0.7277 0.7092 0.7161 0.7326 0.7147 0.7719 0.7371 0.7386 0.7484

8.59 0.7948 0.5467 0.7465 0.6427 0.6719 0.6907 0.6568 0.7452 0.7798 0.7576 0.7980 0.8317

9.64 0.7554 0.5440 0.7379 0.6532 0.6634 0.6540 0.6763 0.7021 0.7264 0.7293 0.7105 0.7877

10.4 0.7747 0.5521 0.7296 0.6127 0.6541 0.6880 0.6290 0.6750 0.7565 0.7420 0.7642 0.8154

11.24 0.7692 0.5557 0.7140 0.6083 0.6541 0.6868 0.6238 0.6761 0.7561 0.7268 0.7487 0.8129

12.38 0.7460 0.5552 0.6766 0.6091 0.6426 0.6582 0.6205 0.6637 0.7455 0.6939 0.7291 0.7939

13.28 0.6706 0.5386 0.6341 0.5858 0.5977 0.5641 0.5965 0.6190 0.6805 0.6362 0.6503 0.7172

<Correlation coefficients between AHI Green band & AHI 16 bands>

• To select the band, consider relevant between AHI green band and other bands.

• The highest CC with green band will be selected for input data.

Blue Band selected

for training pair

Band Selection

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Method

• Generator : produce AI images through

training input pair images. ( x & y => G(x) )

• Discriminator : try to distinguish the real pair

images from the AI-generated pair images.

(G(x) vs y)

• So Generator should create real-like image

that discriminator couldn’t realize the

difference.

CGAN (Conditional Generative Adversarial Network) Isola et al.(2017)

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4. Result

Observed AHI RGB AI AHI RGB

Observed AHI Green Band AI AHI Green Band

• Observed AHI Green vs. AI-generated AHI Green

Very high CC(=0.999) between observed AHI

green band and AI-generated AHI green band

(a)

(c) (d)

(b)

Our Model Validation : AHI Green

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Synthetic ABI RGB AI ABI RGB

Synthetic ABI Green Band AI ABI Green Band

• Synthetic ABI Green vs. AI-generated ABI Green

Very high CC(=0.993) between synthetic ABI green

band and AI-generated ABI green band

(a)

(c) (d)

(b)

Our Model Application : ABI Green

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Comparison with Blue and Red Band

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Temporal Variation: Synthetic ABI RGB

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Temporal Variation: Our ABI RGB

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Synthetic RGB

Result Animation

AI RGB

Application to night time RGB generation

Synthetic RGB AI Synthetic RGB

• Create night version of ABI RGB

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Application to nighttime Vis band generation

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AI-generated VISCOMS VIS (Observation)

• 2015.08.15 (05:00~07:30 KST (Daytime), 17:00~19:45 KST (Nighttime)

VIIRS DNB vs AI-generated COMS VIS

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AI-generated COMS VIS

October 22, 2018

17:00 UTC nighttime

VIIRS Day and Night Band (DNB)

October 22, 2018

1 day- nighttime

Real COMS IR1

October 22, 2018

17:00 UTC nighttime

5. Summary & Conclusion

• Using a Deep Learning technique, we created GOES-16’s green band that

doesn’t exist.

• Our deep learning model using CGAN to produce AHI green band images

showed a good statistical agreement with the observed AHI green band images.

• Our AI-generated ABI green band exhibited the similar results to real AHI green

band.

• Our AI model also showed the equivalent performance with Synthetic-ABI.

Summary & Conclusion

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