Tuned Fusion Deep Learning Approach for Unsupervised Cyclone Passive Microwave Rainfall Imagery Prediction
Keywords:
Deep Learning, Principal Component Analysis (PCA), Tropical Cyclone Detection, Unsupervised LearningAbstract
In many cases, tropical cyclones (TCs) can bring about widespread, intense rainfall because of the volume of water vapour they contain. High-resolution, passive microwave (PMW) rainfall assessment of tropical cyclones is essential for disaster forecasting tropical cyclones. Still, it remains a tricky issue because of the low temporal resolution of sensing devices. This research makes an effort to address this issue by directly forecasting PMW rainfall pictures of tropical cyclones. We developed a tuned fusion learning approach to forecast unannotated PMW image classes. The study outcomes demonstrate the system’s ability to retrieve essential features from PMW images successfully. The global applicability of this deep learning technique is promising. It offers a fresh angle on satellite-based typhoon rainfall forecasting, which can yield valuable insights for real-time visualization of typhoon precipitation around the world in operational activities.
Downloads
References
L. Chen, Y. Li, and Z. Cheng, “An overview of research and forecasting on rainfall associated with landfalling tropical cyclones,” Advances in Atmospheric Sciences, vol. 27, pp. 967-976, 2010.
J. Shi et al., “Implementation of an aerosol–cloud‐microphysics–radiation coupling into the NASA unified WRF: Simulation results for the 6–7 August 2006 AMMA special observing period,” Quarterly Journal of the Royal Meteorological Society, vol. 140, no. 684, pp. 2158-2175, 2014.
S. Vahedizade, “Machine Learning for Advancing Spaceborne Passive Microwave Remote Sensing of Snowfall,” University of Minnesota, 2022.
P. Minnett et al., “Half a century of satellite remote sensing of sea-surface temperature,” Remote Sensing of Environment, vol. 233, p. 111366, 2019.
V. Levizzani, “Satellite clouds and precipitation observations for meteorology and climate,” Hydrological modelling the water cycle. Springer, Berlin, pp. 49-68, 2008.
K.-l. Hsu, X. Gao, S. Sorooshian, and H. V. Gupta, “Precipitation estimation from remotely sensed information using artificial neural networks,” Journal of Applied Meteorology Climatology, vol. 36, no. 9, pp. 1176-1190, 1997.
S. Yang and J. Cossuth, “Satellite remote sensing of tropical cyclones,” in Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection: IntechOpen, 2016, p. 137.
S. Yang, R. Bankert, and J. Cossuth, “Tropical cyclone climatology from satellite passive microwave measurements,” Remote Sensing, vol. 12, no. 21, p. 3610, 2020.
Q. P. Remund and D. G. Long, “Sea ice extent mapping using Ku band scatterometer data,” Journal of Geophysical Research: Oceans, vol. 104, no. C5, pp. 11515-11527, 1999.
Q. P. Remund and D. G. Long, “A decade of QuikSCAT scatterometer sea ice extent data,” IEEE Transactions on Geoscience Remote Sensing, vol. 52, no. 7, pp. 4281-4290, 2013.
J. C. Comiso, “Large-scale characteristics and variability of the global sea ice cover,” Sea ice: an introduction to its physics, chemistry, biology geology, pp. 112-142, 2003.
S. Yang, J. Hawkins, and K. Richardson, “The improved NRL tropical cyclone monitoring system with a unified microwave brightness temperature calibration scheme,” Remote Sensing, vol. 6, no. 5, pp. 4563-4581, 2014.
A. Wimmers, C. Velden, and J. H. Cossuth, “Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery,” Monthly Weather Review, vol. 147, no. 6, pp. 2261-2282, 2019.
E. Rodgers and R. Adler, “Tropical cyclone rainfall characteristics as determined from a satellite passive microwave radiometer,” Monthly Weather Review, vol. 109, no. 3, pp. 506-521, 1981.
H. Jiang, C. Tao, and Y. Pei, “Estimation of tropical cyclone intensity in the North Atlantic and northeastern Pacific basins using TRMM satellite passive microwave observations,” Journal of applied meteorology climatology, vol. 58, no. 2, pp. 185-197, 2019.
R. V. Deo, R. Chandra, and A. Sharma, “Stacked transfer learning for tropical cyclone intensity prediction,” 2017.
S. Banerjee, A. Ghosh, K. Sorkhel, and T. Roy, “Post Cyclone Damage Assessment Using CNN Based Transfer Learning and Grad-CAM,” in 2021 IEEE Pune Section International Conference (PuneCon), 2021, pp. 1-7: IEEE.
R. K. Singh, K. N. Singh, M. Maisnam, and S. Maity, “Antarctic sea ice extent from ISRO’s SCATSAT-1 using PCA and an unsupervised classification,” in Proceedings, 2018, vol. 2, no. 7, p. 340: MDPI.
B. Chen, B.-F. Chen, and H.-T. Lin, “Rotation-blended CNNs on a new open dataset for tropical cyclone image-to-intensity regression,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 90-99.
NCEI, “The Geostationary IR Channel Brightness Temperature (BT)- GridSat-B1 Climate Data Record (CDR),” National Centers for Environmental Information, 2017.
NOAA, “NOAA CPC Morphing Technique (“CMORPH”),” National Weather Service: Climate Prediction Center, 2023.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning, 2019, pp. 6105-6114: PMLR.
B. Koonce and B. Koonce, “EfficientNet,” Convolutional Neural Networks with Swift for Tensorflow: Image Recognition Dataset Categorization, pp. 109-123, 2021.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.