Multi-task Learning Based Tropical Cyclone Intensity Monitoring and Forecasting through Fusion of Geostationary Satellite Data and Numerical Forecasting Model Output |
Lee, Juhyun
(Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Yoo, Cheolhee (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Im, Jungho (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Shin, Yeji (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Cho, Dongjin (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) |
1 | Chaudhuri, S., D. Dutta, S. Goswami, and A. Middey, 2013. Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification, Natural Hazards, 65(1): 97-113. DOI |
2 | Chung, C. Y., H. K. Lee, H. J. Ahn, M. H. Ahn, and S. N. Oh, 2006. Developing the cloud detection algorithm for COMS Meteorolgical Data Processing System, Korean Journal of Remote Sensing, 22(5): 367-372 (in Korean with English abstract). DOI |
3 | Combinido, J. S., J. R. Mendoza, and J. Aborot, 2018. A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images, Proc. of 2018 24th International Conference on Pattern Recognition (ICPR), IEEE, Beijing, China, Aug. 20-24, pp.1474-1480. |
4 | Dvorak, V. F., 1975. Tropical cyclone intensity analysis and forecasting from satellite imagery, Monthly Weather Review, 103(5): 420-430. DOI |
5 | Feng, B., 2005. A neural network regression model for tropical cyclone forecast, Proc. of 2005 International Conference on Machine Learning and Cybernetics, IEEE, Guangzhou, China, Aug. 18-21, pp.4122-4128. |
6 | Grossman, M., and M. Zaiki, 2009. Reconstructing typhoons in Japan in the 1880s from documentary records, Weather, 64(12): 315-322. DOI |
7 | Guo, Y., J. Cai, B. Jiang, and J. Zheng, 2018. Cnnbased real-time dense face reconstruction with inverse-rendered photo-realistic face images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(6): 1294-1307. DOI |
8 | Ham, Y.-G., J.-H. Kim, and J.-J. Luo, 2019. Deep learning for multi-year ENSO forecasts, Nature, 573(7775): 568-572. DOI |
9 | Han, H., S. Lee, J. Im, M. Kim, M.-I. Lee, M. H. Ahn, and S.-R. Chung, 2015. Detection of convective initiation using Meteorological Imager onboard Communication, Ocean, and Meteorological Satellite based on machine learning approaches, Remote Sensing, 7(7): 9184-9204. DOI |
10 | Hoegh-Guldberg, O., D. Jacob, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K. Ebi, F. Engelbrecht, and J. Guiot, 2018. Impacts of 1.5 C global warming on natural and human systems, Global warming of 1.5. An IPCC Special Report, http://hdl.handle.net/10138/311749, Accessed on Sep. 25, 2020. |
11 | Huang, X., Z. Guan, L. He, Y. Huang, and H. Zhao, 2016. A PNN prediction scheme for local tropical cyclone intensity over the South China Sea, Natural Hazards, 81(2): 1249-1267. DOI |
12 | Liu, X., Y. Lu, G. Zhu, Y. Lei, L. Zheng, H. Qin, C. Tang, G. Ellison, R. McCormack, and Q. Ji, 2013. The diagnostic accuracy of pleural effusion and plasma samples versus tumour tissue for detection of EGFR mutation in patients with advanced non-small cell lung cancer: comparison of methodologies, Journal of Clinical Pathology, 66(12): 1065-1069. DOI |
13 | Kayalibay, B., G. Jensen, and P. van der Smagt, 2017. CNN-based segmentation of medical imaging data, arXiv preprint arXiv:1701.03056, Accessed on Sep. 25, 2020. |
14 | Kim, Y., G.-H. Kwak, K.-D. Lee, S.-I. Na, C.-W. Park, and N.-W. Park, 2018. Performance evaluation of machine learning and deep learning algorithms in crop classification: Impact of hyper-parameters and training sample size, Korean Journal of Remote Sensing, 34(5): 811-827 (in Korean with English abstract). DOI |
15 | Lee, J., J. Im, D.-H. Cha, H. Park, and S. Sim, 2020. Tropical cyclone intensity estimation using multidimensional convolutional neural networks from geostationary satellite data, Remote Sensing, 12(1): 108. |
16 | Mendelsohn, R., K. Emanuel, S. Chonabayashi, and L. Bakkensen, 2012. The impact of climate change on global tropical cyclone damage, Nature Climate Change, 2(3): 205-209. DOI |
17 | Menzel, W. P., and J. F. Purdom, 1994. Introducing GOES-I: The first of a new generation of geostationary operational environmental satellites, Bulletin of the American Meteorological Society, 75(5): 757-782. DOI |
18 | Pradhan, R., R. S. Aygun, M. Maskey, R. Ramachandran, and D. J. Cecil, 2017. Tropical cyclone intensity estimation using a deep convolutional neural network, IEEE Transactions on Image Processing, 27(2): 692-702. DOI |
19 | Nwe, T. L., T. H. Dat, and B. Ma, 2017. Convolutional neural network with multi-task learning scheme for acoustic scene classification, Proc. of 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, Kuala Lumpur, Malaysia, Dec.12-15, pp.1347-1350. |
20 | Olander, T. L., and C. S. Velden, 2007. The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery, Weather and Forecasting, 22(2): 287-298. DOI |
21 | Qiu, Z., T. Yao, and T. Mei, 2017. Learning spatiotemporal representation with pseudo-3d residual networks, Proc. of the IEEE International Conference on Computer Vision, Venice, Italy, Oct. 22-29, pp. 5533-5541. |
22 | Ritchie, E. A., K. M. Wood, O. G. Rodreiguez-Herrera, M. F. Pineros, and J. S. Tyo, 2014. Satellitederived tropical cyclone intensity in the North Pacific Ocean using the deviation-angle variance technique, Weather and Forecasting, 29(3): 505-516. DOI |
23 | Ruder, S., 2017. An overview of multi-task learning in deep neural networks, arXiv preprint arXiv:1706.05098, Accessed on Sep. 25, 2020. |
24 | Schmetz, J., S. Tjemkes, M. Gube, and L. Van de Berg, 1997. Monitoring deep convection and convective overshooting with METEOSAT, Advances in Space Research, 19(3): 433-441. DOI |
25 | Sim, S., J. Im, S. Park, H. Park, M. H. Ahn, and P.-w. Chan, 2018. Icing detection over East Asia from geostationary satellite data using machine learning approaches, Remote Sensing, 10(4): 631. DOI |
26 | Yoo, C., D. Han, J. Im, and B. Bechtel, 2019. Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images, ISPRS Journal of Photogrammetry and Remote Sensing, 157: 155-170. DOI |
27 | Song, A., and Y. Kim, 2017. Deep learning-based hyperspectral image classification with application to environmental geographic information systems, Korean Journal of Remote Sensing, 33(6-2): 1061-1073 (in Korean with English abstract). DOI |
28 | Velden, C. S., C. M. Hayden, S. J. W. Nieman, W. Paul Menzel, S. Wanzong, and J. S. Goerss, 1997. Upper-tropospheric winds derived from geostationary satellite water vapor observations, Bulletin of the American Meteorological Society, 78(2): 173-196. DOI |