Tornado Storm Data Synthesization using Deep Convolutional Generative Adversarial Network: Related Works and Implementation Details

Thumbnail Image

Files

Barajas_ICDATA2020_Appendices.pdf (2.24 MB)

Links to Files

http://hpcf-files.umbc.edu/research/papers/Barajas_ICDATA2020_Appendices.pdf

Permanent Link

http://hdl.handle.net/11603/19168

Collections

UMBC Mathematics and Statistics Department
UMBC Faculty Collection
UMBC Information Systems Department
UMBC Student Collection

Author/Creator

Author/Creator ORCID

Date

Type of Work

technical reports
Text

Department

Program

Citation of Original Publication

Carlos A. Barajas, Matthias K. Gobbert and Jianwu Wang, Tornado Storm Data Synthesization using Deep Convolutional Generative Adversarial Network: Related Works and Implementation Details, http://hpcf-files.umbc.edu/research/papers/Barajas_ICDATA2020_Appendices.pdf

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

UMBC High Performance Computing Facility (HPCF)

Abstract

Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic weather patterns much more rapidly, thus allowing for more timely alerts to the public. A challenge in applying machine learning in tornado prediction is the imbalance between tornadic data and non-tornadic data. To have more balanced data, we created in this work a new data synthesization system to augment tornado storm data by implementing a deep convolutional generative adversarial network (DCGAN) and qualitatively compare its output to natural data.