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Deepti: Deep Learning-Based Tropical Cyclone Intensity EstimationWe present the development of a deep learning model for objective estimation of tropical cyclone intensity at a higher temporal frequency, deployment of the model in production, design and implementation of the tropical cyclone monitoring and intensity estimation system and development of an interactive portal for situational awareness and evaluation of intensity estimation.
Document ID
20180008812
Acquisition Source
Marshall Space Flight Center
Document Type
Presentation
Authors
Maskey, Manil (NASA Marshall Space Flight Center Huntsville, AL, United States)
Ramachandran, Rahul (NASA Marshall Space Flight Center Huntsville, AL, United States)
Miller, J. J. (Alabama Univ. Huntsville, AL, United States)
Gurung, Iksha (Alabama Univ. Huntsville, AL, United States)
Freitag, Brian M. (Alabama Univ. Huntsville, AL, United States)
Bollinger, Drew (Development Seed Washington, DC, United States)
da Silva, Daniel (Development Seed Washington, DC, United States)
Molthan, Andrew (NASA Marshall Space Flight Center Huntsville, AL, United States)
Hain, Christopher (NASA Marshall Space Flight Center Huntsville, AL, United States)
Cecil, Dan (NASA Marshall Space Flight Center Huntsville, AL, United States)
Date Acquired
December 27, 2018
Publication Date
December 10, 2018
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
MSFC-E-DAA-TN63448
Meeting Information
Meeting: American Geophysical Union (AGU) Fall Meeting
Location: Washington, DC
Country: United States
Start Date: December 10, 2018
End Date: December 14, 2018
Sponsors: American Geophysical Union (AGU)
Funding Number(s)
CONTRACT_GRANT: NNM11AA01A
Distribution Limits
Public
Copyright
Public Use Permitted.

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