Cyclone track forecasting based on satellite images using artificial neural networks

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 64, Issue 6, 2009, Pages 513-521

  Kovordányi, Rita ^a   Roy, Chandan ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

Artificial neural networks; Cyclones; Hazards; Meteorology; Tracking

Indexed keywords

AIR PRESSURES; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; CYCLONE TRACKS; CYCLONES; EFFECTIVE TOOL; FORECASTING TECHNIQUES; GENERALIZATION CAPABILITY; HUMAN VISUAL SYSTEM; NOAA AVHRR; REMOTE SENSING DATA; SATELLITE IMAGES; STORM SURGES; SUPPLEMENTARY INFORMATION; TEST IMAGES; TRACKING; TROPICAL CYCLONE; WATER TEMPERATURES; WIND SPEED;

ATMOSPHERIC PRESSURE; BACKPROPAGATION; REMOTE SENSING; SATELLITES; STORMS; TECHNOLOGICAL FORECASTING; WEATHER FORECASTING;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC PRESSURE; AVHRR; FORECASTING METHOD; HAZARD ASSESSMENT; INFORMATION SYSTEM; NOAA SATELLITE; SATELLITE IMAGERY; TRACKING; TROPICAL CYCLONE; WATER TEMPERATURE; WEATHER FORECASTING; WIND VELOCITY;

EID: 70350618080     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2009.03.002     Document Type: Article

References (32)

1. Barsi A., and Heipke C. Artificial neural networks for the detection of road junctions in aerial images. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 34 (2003) 113-118 (Part 3/W8)
2. Barsi, A., Heipke, C., 2003b. Detecting road junctions by artificial neural networks. In: Remote Sensing and Data Fusion over Urban Areas (Proc. 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (URBAN 2003)), Berlin, 22-23 May 2003, pp. 129-132
3. Brad, R., Letia, I.A., 2002a. Extracting cloud motion from satellite image sequences. In: Proc. Seventh International Conference on Control, Automation, Robotics and Vision (ICARCV'2002), Singapore, 2-5 December 2002, pp. 1303-1307
4. Brad, R., Letia, I.A., 2002b. Cloud motion detection from infrared satellite images. In: Proc. Second International Conference on Image and Graphics (ICIG 2002), vol. 4875, China, 16-18 August 2002, pp. 408-412
5. Bureau of Meteorology Research Centre. 2000. Global guide to Tropical Cyclone forecasting, chapter 3. http://www.bom.gov.au/bmrc/pubs/tcguide/ch3/ch3_tableofcontents.htm (accessed 12.01.07)
6. Cerveny R.S., and Newman L.E. Climatological relationships between tropical cyclones and rainfall. Monthly Weather Review 128 9 (2000) 3329-3336
7. Creem S.H., and Proffitt D.R. Defining the cortical visual systems: What, Where, and How. Acta Psychologica 107 1-3 (2001) 43-68
8. Dvorak V.F. Tropical cyclone intensity analysis and forecasting from satellite imagery. Monthly Weather Review 103 5 (1975) 420-430
9. Felleman D.J., and Van Essen D.C. Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1 (1991) 1-47
10. Fett R.W., and Brand S. Tropical cyclone movement forecasts based on observations from satellites. Journal of Applied Meteorology 14 4 (1975) 452-465
11. Fukushima K., and Miyake S. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition 15 6 (1982) 455-469
12. Goodale M.A., and Milner A.D. Separate visual pathways for perception and action. Trends in Neuroscience 15 1 (1992) 20-25
13. Hong Y., Hsu K.-L., Sorooshian S., and Gao X. Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology 43 12 (2004) 1834-1852
14. Jang J.-D., Viau A.A., Anctil F., and Bartholomé E. Neural network application for cloud detection in SPOT vegetation images. International Journal of Remote Sensing 27 3-4 (2006) 719-736
15. Kishtawal C.M., Patadia F., Singh R., Basu S., Narayanan M.S., and Joshi P.C. Automatic estimation of tropical cyclone intensity using multi-channel TMI data: A genetic algorithm approach. Geophysical Research Letters 32 11 (2005) L11804.1-L11804.5
16. Knaff J.A., Zehr R.M., and Goldberg M.D. An example of temperature structure differences in two cyclone systems derived from the advanced microwave sounder unit. Weather and Forecasting 15 4 (2000) 476-483
17. Kossin, J., 2003. A user's guide to the UW-CIMSS Tropical Cyclone Intensity Estimation (TIE) Model. Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin, Madison, WI
18. Kosslyn S.M. Image and Brain: The Resolution of the Imagery Debate (1994), MIT Press, Cambridge, MA pp. 53-79
19. Lau N.C., and Crane M.W. Comparing satellite and surface observations of cloud patterns in synoptic-scale circulation systems. Monthly Weather Review 125 12 (1997) 3172-3189
20. Lajoie F.A. On the direction of movement of tropical cyclones. Australian Meteorological Magazine 24 (1976) 95-104
21. Marshall J.F.L., Leslie L.M., Abbey Jr. R.F., and Qi L. Tropical cyclone track and intensity prediction: The generation and assimilation of high-density, satellite-derived data. Meteorology and Atmospheric Physics 80 1-4 (2002) 43-57
22. McBride J.L., and Holland G.J. Tropical-cyclone forecasting: A worldwide summery of techniques and verification statistics. Bulletin of the American Meteorological Society 68 10 (1987) 1230-1238
23. McClelland J.L. The GRAIN model: A framework for modeling the dynamics of information processing. In: Meyer D.E., and Kornblum S. (Eds). Attention and Performance XIV: Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience (1993), MIT Press, Cambridge, MA 655-688
24. Mutch J., and Lowe D.G. Object class recognition and localization using sparse features with limited receptive fields. International Journal of Computer Vision 80 1 (2008) 45-57
25. Naval Research Laboratory. 1999. Tropical Cyclone Forecasters Reference Guide. http://www.nrlmry.navy.mil/~chu/ (accessed 13.01.07)
26. O'Reilly R.C., and Munakata Y. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain (2000), MIT Press, Cambridge, MA pp. 46-177
27. O'Reilly R.C. Six principles for biologically-based computational models of cortical processing. Trends in Cognitive Sciences 2 11 (1998) 455-462
28. Rivolta G., Marzano F.S., Coppola E., and Verdecchia M. Artificial neural-network technique for precipitation nowcasting from satellite imagery. Advances in Geosciences 7 (2006) 97-103
29. Spratling M.W. Learning viewpoint invariant perceptual representations from cluttered images. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 5 (2005) 753-761
30. Mishkin M., Ungerleider L.G., and Macko K.A. Object vision and spatial vision: Two cortical pathways. Trends in Neurosciences 6 10 (1983) 414-417
31. Velden C.S., Olander T.L., and Zehr R.M. Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Weather and Forecasting 13 1 (1998) 172-186
32. Wells, F.H., 1987. Tropical cyclone intensity estimates using satellite data: The early years. In: Proc. 26th Conference on Hurricanes and Tropical Meteorology, Miami, FL, 2-7 May 2004, pp. 1-2