Articles | Volume 5, issue 2
21 Nov 2019
 | 21 Nov 2019

Automated detection of weather fronts using a deep learning neural network

James C. Biard and Kenneth E. Kunkel

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Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., and Ghemawat, S.: TensorFlow: Large-scale machine learning on heterogeneous systems, available at: (last access: April 2019), 2015. 
American Meteorological Society Glossary of Meteorology: Front, available at:, last access: 27 March 2019. 
Biard, J. C.: Coded Surface Bulletin (JSON format), Zenodo,, 2019a. 
Biard, J. C.: Coded Surface Bulletin (netCDF format), Zenodo,, 2019b. 
Biard, J. C. and Kunkel, K. E.: DL-FRONT MERRA-2 weather front probability maps over North America, Zenodo,, 2019a. 
Short summary
A deep learning convolutional neural network (DL-FRONT) was around 90 % successful in determining the locations of weather fronts over North America when compared against front locations determined manually by forecasters. DL-FRONT detects fronts using maps of air pressure, temperature, humidity, and wind from historical observations or climate models. DL-FRONT makes it possible to do science that was previously impractical because manual front identification would take too much time.