• Aberson, S. D., M. L. Black, R. A. Black, R. W. Burpee, J. J. Cione, C. W. Landsea, and F. D. Marks, 2006: Thirty years of tropical cyclone research with the NOAA P-3 aircraft. Bull. Amer. Meteor. Soc., 87, 10391056, https://doi.org/10.1175/BAMS-87-8-1039.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, M. M., and M. T. Montgomery, 2008: Observed structure, evolution, and potential intensity of category 5 Hurricane Isabel (2003) from 12 to 14 September. Mon. Wea. Rev., 136, 20232046, https://doi.org/10.1175/2007MWR1858.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bender, M. A., I. Ginis, R. Tuleya, B. Thomas, and T. Marchok, 2007: The operational GFDL coupled hurricane ocean prediction system and a summary of its performance. Mon. Wea. Rev., 135, 39653989, https://doi.org/10.1175/2007MWR2032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chatfield, K., K. Simonyan, A. Vedaldi, and A. Zisserman, 2014: Return of the devil in the details: Delving deep into convolutional nets. Proceedings of the British Machine Vision Conference 2014, M. Valstar, A. French, and T. Pridmore, Eds., BMVA Press, 6.16.12, https://doi.org/10.5244/C.28.6.

    • Search Google Scholar
    • Export Citation
  • Chen, B., B. Chen, H. Lin, and R. L. Elsberry, 2019: Estimating tropical cyclone intensity by satellite imagery utilizing convolutional neural networks. Wea. Forecasting, 34, 447465, https://doi.org/10.1175/WAF-D-18-0136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., L. I. Yan, K. F. Mao, and S. M. Fei, 2013: Automatic location of typhoon center based on nonlinear fitness function from IR satellite cloud images. J. Trop. Meteor., 29, 155160.

    • Search Google Scholar
    • Export Citation
  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420430, https://doi.org/10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., Z. Sun, and L. Jin, 2016: Learning deep neural network using max-margin minimum classification error. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Shanghai, China, Institute of Electrical and Electronics Engineers, 26772681, https://doi.org/10.1109/ICASSP.2016.7472163.

    • Search Google Scholar
    • Export Citation
  • Frank, N. L., 1977: Atlantic tropical systems of 1971. Mon. Wea. Rev., 122, 307314, https://doi.org/10.1175/1520-0493(1972)100%3C0268:ATSO%3E2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Giammanco, I. M., J. L. Schroeder, and M. D. Powell, 2012: GPS dropwindsonde and WSR-88D observations of tropical cyclone vertical wind profiles and their characteristics. Wea. Forecasting, 28, 7799, https://doi.org/10.1175/WAF-D-11-00155.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glorot, X., A. Bordes, and Y. Bengio, 2012: Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, Vol. 15, G. Gordon, D. Dunson, and M. Dudík, Eds., MLR Press, 315–323, https://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf.

    • Search Google Scholar
    • Export Citation
  • Guo, D., Y. Wu, S. S. Shitz, and S. Verdu, 2011: Estimation in Gaussian noise: Properties of the minimum mean-square error. IEEE Trans. Inf. Theory, 57, 23712385, https://doi.org/10.1109/TIT.2011.2111010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Handcock, R. N., A. R. Gillespie, K. A. Cherkauer, J. E. Kay, S. J. Burges, and S. K. Kampf, 2006: Accuracy and uncertainty of thermal-infrared remote sensing of stream temperatures at multiple spatial scales. Remote Sens. Environ., 100, 427440, https://doi.org/10.1016/j.rse.2005.07.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hasler, A. F., R. Mack, and A. Negri, 1983: Stereoscopic observations from meteorological satellites. Adv. Space Res., 2, 105113, https://doi.org/10.1016/0273-1177(82)90130-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, K., X. Zhang, S. Ren, and J. Sun, 2015: Deep residual learning for image recognition. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, NV, Institute of Electrical and Electronics Engineers, 770778.

    • Search Google Scholar
    • Export Citation
  • Hinton, G. E., N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, 2012: Improving neural networks by preventing co-adaptation of feature detectors. arXiv, 18 pp., https://arxiv.org/abs/1207.0580.

    • Search Google Scholar
    • Export Citation
  • Hsiao, L. F., M. S. Peng, D. S. Chen, K. N. Huang, and T. C. Yeh, 2009: Sensitivity of typhoon track predictions in a regional prediction system to initial and lateral boundary conditions. J. Appl. Meteor. Climatol., 48, 19131928, https://doi.org/10.1175/2009JAMC2038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiménez, P., R. Parra, and J. M. Baldasano, 2007: Influence of initial and boundary conditions for ozone modeling in very complex terrains: A case study in the northeastern Iberian Peninsula. Environ. Modell. Software, 22, 12941306, https://doi.org/10.1016/j.envsoft.2006.08.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juang, B. H., and S. Katagiri, 1992: Discriminative learning for minimum error classification. IEEE Trans. Sig. Proc., 40, 30433054, https://doi.org/10.1109/78.175747.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krizhevsky, A., I. Sutskever, and G. E. Hinton, 2012: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (NIPS 2012), F. Pereira et al., Eds., NeurIPS, 1097–1105, https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf.

    • Search Google Scholar
    • Export Citation
  • Kuo, H. C., C. P. Chang, Y. T. Yang, and H. J. Jiang, 2008: Western north Pacific typhoons with concentric eyewalls. Mon. Wea. Rev., 137, 37583770, https://doi.org/10.1175/2009MWR2850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LeCun, Y., Y. Bengio, and G. Hinton, 2015: Deep learning. Nature, 521, 436444, https://doi.org/10.1038/nature14539.

  • Lee, J., J. Im, D.-H. Cha, H. Park, and S. Sim, 2020: Tropical cyclone intensity estimation using multi-dimensional convolutional neural networks from geostationary satellite data. Remote Sens., 12, 108, https://doi.org/10.3390/rs12010108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, L., Y. Chen, T. Xu, R. Liu, K. Shi, and C. Huang, 2015: Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm. Remote Sens. Environ., 164, 142154, https://doi.org/10.1016/j.rse.2015.04.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, P., L. Dong, H. Xiao, and M. Xu, 2015: A cloud image detection method based on SVM vector machine. Neurocomputing, 169, 3442, https://doi.org/10.1016/j.neucom.2014.09.102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C. C., G. R. Liu, T. H. Lin, and C. C. Chao, 2010: Accumulated rainfall forecast of Typhoon Morakot (2009) in Taiwan using satellite data. J. Meteor. Soc. Japan, 88, 785798, https://doi.org/10.2151/jmsj.2010-501.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McDermott, E., T. J. Hazen, J. L. Roux, A. Nakamura, and S. Katagiri, 2006: Discriminative training for large-vocabulary speech recognition using minimum classification error. IEEE Trans. Audio Speech Lang. Process., 15, 203223, https://doi.org/10.1109/TASL.2006.876778.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., and R. J. Kallenbach, 2010: A theory for vortex Rossby‐waves and its application to spiral bands and intensity changes in hurricanes. Quart. J. Roy. Meteor. Soc., 123, 435465, https://doi.org/10.1002/qj.49712353810.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nair, V., and G. E. Hinton, 2010: Rectified linear units improve restricted Boltzmann machines. ICML’10: Proc. of the 27th Int. Conf. on Machine Learning, Haifa, Israel, Association for Computing Machinery, 807814, https://dl.acm.org/doi/10.5555/3104322.3104425.

    • Search Google Scholar
    • Export Citation
  • Park, D. R., and S. W. Bang, 2010: Method and apparatus for processing line pattern using convolution kernel. U.S. Patent US20060182365A1, filed 10 February 2006, issued 2 February 2010.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D, 1980: An evaluation of diagnostic marine boundary layer models applied to tropical cyclones. Wind Eng., 108, 133143.

  • Rehn, M., and F. T. Sommer, 2007: A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. J. Comput. Neurosci., 22, 135146, https://doi.org/10.1007/s10827-006-0003-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, S., K. He, R. Girshick, and J. Sun, 2015: Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39, 11371149, https://doi.org/10.1109/TPAMI.2016.2577031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodés-Guirao, L., 2019: Deep learning for digital typhoon: Exploring a typhoon satellite image dataset using deep learning. Dissertation, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 70 pp., http://www.diva-portal.org/smash/get/diva2:1304600/FULLTEXT01.pdf.

    • Search Google Scholar
    • Export Citation
  • Sabour, S., N. Forsst, and G. E. Hinton, 2017: Dynamic routing between capsules. Proc. of the 31st Int. Conf. on Neural Information Processing Systems, Long Beach, CA, Association for Computing Machinery, 38563866, https://dl.acm.org/doi/10.5555/3294996.3295142.

    • Search Google Scholar
    • Export Citation
  • Sharma, A., X. Liu, X. Yang, and D. Shi, 2017: A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 1928, https://doi.org/10.1016/j.neunet.2017.07.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simonyan, K., and A. Zisserman, 2014: Very deep convolutional networks for large-scale image recognition. arXiv, 14 pp., https://arxiv.org/abs/1409.1556.

    • Search Google Scholar
    • Export Citation
  • Szegedy, C., and Coauthors, 2015: Going deeper with convolutions. 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, Institute of Electrical and Electronics Engineers, 19, https://doi.org/10.1109/CVPR.2015.7298594.

    • Search Google Scholar
    • Export Citation
  • Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, 2016: Rethinking the inception architecture for computer vision. 2016 IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, NV, Institute of Electrical and Electronics Engineers, 28182826, https://doi.org/10.1109/CVPR.2016.308.

    • Search Google Scholar
    • Export Citation
  • Taigman, Y., M. Yang, M. A. Ranzato, and L. Wolf, 2014: DeepFace: Closing the gap to human-level performance in face verification. 2014 IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, OH, Institute of Electrical and Electronics Engineers, 17011708, https://doi.org/10.1109/CVPR.2014.220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tralli, D. M., R. G. Blom, V. Zlotnicki, A. Donnellan, and D. L. Evans, 2005: Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS J. Photogramm. Remote Sens., 59, 185198, https://doi.org/10.1016/j.isprsjprs.2005.02.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, S6S9, https://doi.org/10.1175/BAMS-87-9-Velden.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weatherford, C. L., and W. M. Gray, 1988: Typhoon structure as revealed by aircraft reconnaissance. Part II: Structural variability. Mon. Wea. Rev., 116, 10441056, https://doi.org/10.1175/1520-0493(1988)116<1044:TSARBA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wei, K., Z. L. Jing, Y. X. Li, and S. L. Liu, 2011: Spiral band model for locating tropical cyclone centers. Pattern Recognit. Lett., 32, 761770, https://doi.org/10.1016/j.patrec.2010.12.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wimmers, A., C. Velden, and J. H. Cossuth, 2019: Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery. Mon. Wea. Rev., 147, 22612282, https://doi.org/10.1175/MWR-D-18-0391.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. C., K. H. Chou, H. J. Cheng, and Y. Wang, 2003: eyewall contraction, breakdown and reformation in a landfalling typhoon. Geophys. Res. Lett., 30, L017653, https://doi.org/10.1029/2003GL017653.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, M., W. Lu, J. Yang, Y. Ma, W. Yao, and Z. Zheng, 2015: A hybrid method based on extreme learning machine and k -nearest neighbor for cloud classification of ground-based visible cloud image. Neurocomputing, 160, 238249, https://doi.org/10.1016/j.neucom.2015.02.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, J., J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, 2010: SUN database: Large-scale scene recognition from abbey to zoo. 2010 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, San Francisco, CA, IEEE, 34853492, https://doi.org/10.1109/CVPR.2010.5539970.

    • Search Google Scholar
    • Export Citation
  • Xie, F., Z. He, M. Q. Esguerra, F. Qiu and V. Ramanathan, 2013: Determination of heterotic groups for tropical Indica hybrid rice germplasm. Theor. Appl. Genet., 127, 407417, https://doi.org/10.1007/s00122-013-2227-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zeiler, M. D., and R. Fergus, 2013: Stochastic pooling for regularization of deep convolutional neural networks. arXiv, 9 pp., https://arxiv.org/abs/1301.3557.

    • Search Google Scholar
    • Export Citation
  • Zhang, L. J., H. Y. Zhu, and X. J. Sun, 2014: China’s tropical cyclone disaster risk source analysis based on the gray density clustering. Nat. Hazards, 71, 10531065, https://doi.org/10.1007/s11069-013-0700-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, Z. S., Z. R. Liu, D. M. Huang, W. Song, G. L. Zou, Q. Hou, and J. B. Hao, 2018: Deep learning model for typhon grade classification based on improved activation function. Comput. Sci., 45, 177181, https://doi.org/10.11896/j.issn.1002-137X.2018.12.028.

    • Search Google Scholar
    • Export Citation
  • Zheng, Z. S., Q. Hou, G. L. Zou, and L. Qi, 2019: Research on deep learning based on improved minimal classification error criterion algorithm: Take typhoon satellite image as example. Jisuanji Yingyong Yanjiu, 36, 31603163.

    • Search Google Scholar
    • Export Citation
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Deep Learning for Typhoon Intensity Classification Using Satellite Cloud Images

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  • 1 a College of Information and Science, Shanghai Ocean University, Shanghai, China
  • | 2 b International Centre for Marine Research, Shanghai Ocean University, Shanghai, China
  • | 3 c National Marine Information Center, Tianjin, China
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Abstract

A tropical cyclone, also known as a typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damage to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant preprocessing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on a convolutional neural network (CNN). The Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud bands (SCBs) more effectively and reduces the overfitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10 000 multiple sensor satellite cloud images from the National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3, and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas, and SCBs, which facilitate classifying and forecasting typhoon intensity.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chenyu Hu, 1105814265@qq.com

Abstract

A tropical cyclone, also known as a typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damage to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant preprocessing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on a convolutional neural network (CNN). The Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud bands (SCBs) more effectively and reduces the overfitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10 000 multiple sensor satellite cloud images from the National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3, and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas, and SCBs, which facilitate classifying and forecasting typhoon intensity.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chenyu Hu, 1105814265@qq.com
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