A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery
Abstract
:1. Introduction
2. Data and Model Design
2.1. Data
2.2. Convolutional Neural Network (CNN)
2.3. Model Optimization
2.4. Gradient-Weighted Class Activation Mapping
3. Results and Discussion
3.1. Performance of the Three CNN Schemes
3.2. Class Activation Mapping
3.3. Sensitivity Test of Dropout and Pooling Layers
4. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Set | Sample Size |
---|---|
Training | 29,730 |
Validation | 2505 |
Testing | 11,624 |
Total | 43,859 |
Model | Layer | Size of the Selected Filter (Range) | Size of the Selected Dropout Rate (Range) | Size of the Selected Learning Rate (Range) |
---|---|---|---|---|
Scheme 1 (RMW) | Conv 1 | 7 (3, 5, 7, 9) | – | – |
Conv 2 | 9 (3, 5, 7, 9) | – | – | |
Conv 3 | 7 (3, 5, 7, 9) | – | – | |
Conv 4 | 9 (3, 5, 7, 9) | – | – | |
Conv 5 | 7 (3, 5, 7, 9) | – | – | |
Dropout | – | 0.25 (0.25, 0.50, 0.75) | – | |
Adam optimizer | – | – | 10−6 (10−3, 10−4, 10−5, 10−6) | |
Scheme 1 (R34) | Conv 1 | 3 (3, 5, 7, 9) | – | – |
Conv 2 | 9 (3, 5, 7, 9) | – | – | |
Conv 3 | 3 (3, 5, 7, 9) | – | – | |
Conv 4 | 9 (3, 5, 7, 9) | – | – | |
Conv 5 | 9 (3, 5, 7, 9) | – | – | |
Dropout | – | 0.25 (0.25, 0.50, 0.75) | – | |
Adam optimizer | – | – | 10−3 (10−3, 10−4, 10−5, 10−6) | |
Scheme 2 | Conv 1 | 5 (3, 5, 7, 9) | – | – |
Conv 2 | 3 (3, 5, 7, 9) | – | – | |
Conv 3 | 5 (3, 5, 7, 9) | – | – | |
Conv 4 | 9 (3, 5, 7, 9) | – | – | |
Conv 5 | 3 (3, 5, 7, 9) | – | – | |
Dropout (RMW) | – | 0.75 (0.25, 0.50, 0.75) | – | |
Dropout (R34) | – | 0.5 (0.25, 0.50, 0.75) | – | |
Adam optimizer | – | – | 10−4 (10−3, 10−4, 10−5, 10−6) | |
Scheme 3 | Conv 1 | 5 (3, 5, 7, 9) | – | – |
Conv 2 | 9 (3, 5, 7, 9) | – | – | |
Conv 3 | 5 (3, 5, 7, 9) | – | – | |
Conv 4 | 7 (3, 5, 7, 9) | – | – | |
Conv 5 | 9 (3, 5, 7, 9) | – | – | |
Dropout (RMW) | – | 0.5 (0.25, 0.50, 0.75) | – | |
Dropout (R34) | – | 0.75 (0.25, 0.50, 0.75) | – | |
Adam optimizer | – | – | 10−5 (10−3, 10−4, 10−5, 10−6) |
TC Size | Method | Region | Period Covered | Correlation | MAE (nmi) |
---|---|---|---|---|---|
RMW | Kossin et al. [23] | AO | 1995–2004 | 0.58 | 13.11 |
Scheme 3 (this study) | WNP | 2011–2016 | 0.95 | 2.05 | |
R34 | Demuth et al. [14] | AO, ENP | 1999–2004 | 0.89 | 16.90 |
Knaff et al. [20] | AO, ENP | 2011–2013 | – | 37.00 | |
Scheme 3 (this study) | WNP | 2011–2016 | 0.93 | 9.77 |
Model | RWW | R34 | |||||||
---|---|---|---|---|---|---|---|---|---|
Corr. | RMSE (nmi) | MAE (nmi) | Bias (nmi) | Corr. | RMSE (nmi) | MAE (nmi) | Bias (nmi) | ||
With dropout layers | Scheme 1 | 0.89 * (0.86 *) | 5.88 (4.98) | 4.25 (3.55) | −1.07 (−0.35) | 0.91 * (0.88 *) | 18.26 (17.98) | 13.60 (12.60) | 0.66 (−0.25) |
Scheme 2 | 0.92 * (0.91 *) | 4.88 (4.00) | 3.36 (2.66) | −0.56 (−0.10) | 0.94 * (0.91 *) | 15.51 (15.62) | 11.43 (10.82) | 1.30 (−0.08) | |
Scheme 3 | 0.97 * (0.95 *) | 3.40 (3.09) | 2.27 (2.05) | −0.17 (−0.05) | 0.95 * (0.93 *) | 13.57 (14.34) | 9.65 (9.77) | 0.29 (0.54) | |
Without dropout layers | Scheme 1 | 0.93 * (0.92 *) | 4.83 (4.04) | 3.29 (2.66) | −1.06 (−0.41) | 0.93 * (0.90 *) | 15.77 (16.75) | 11.46 (11.54) | 1.42 (0.12) |
Scheme 2 | 0.94 * (0.92 *) | 4.45 (3.80) | 2.97 (2.48) | −0.92 (−0.31) | 0.94 * (0.91 *) | 15.72 (16.23) | 11.42 (11.19) | 0.88 (−0.45) | |
Scheme 3 | 0.95 * (0.93 *) | 4.16 (3.64) | 2.90 (2.50) | −0.13 (−0.12) | 0.94 * (0.92 *) | 15.11 (15.26) | 11.11 (10.82) | −0.12 (−0.18) |
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Baek, Y.-H.; Moon, I.-J.; Im, J.; Lee, J. A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery. Remote Sens. 2022, 14, 426. https://doi.org/10.3390/rs14020426
Baek Y-H, Moon I-J, Im J, Lee J. A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery. Remote Sensing. 2022; 14(2):426. https://doi.org/10.3390/rs14020426
Chicago/Turabian StyleBaek, You-Hyun, Il-Ju Moon, Jungho Im, and Juhyun Lee. 2022. "A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery" Remote Sensing 14, no. 2: 426. https://doi.org/10.3390/rs14020426
APA StyleBaek, Y.-H., Moon, I.-J., Im, J., & Lee, J. (2022). A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery. Remote Sensing, 14(2), 426. https://doi.org/10.3390/rs14020426