Articles | Volume 5, issue 2
https://doi.org/10.5194/ascmo-5-147-2019
https://doi.org/10.5194/ascmo-5-147-2019
21 Nov 2019
 | 21 Nov 2019

Automated detection of weather fronts using a deep learning neural network

James C. Biard and Kenneth E. Kunkel

Data sets

• National Weather Surface Coded Surface Bulletins 2003- National Weather Service https://doi.org/10.5281/zenodo.2642801

National Weather Surface Coded Surface Bulletins 2003- (JSON format) J. C. Biard https://doi.org/10.5281/zenodo.2646544

National Weather Surface Coded Surface Bulletins 2003- (netCDF format) J. C. Biard https://doi.org/10.5281/zenodo.2641072

DL-FRONT MERRA-2 weather front probability maps over North America 1980- J. C. Biard and K. E. Kunkel https://doi.org/10.5281/zenodo.2641072

DL-FRONT MERRA-2 vectorized weather fronts over North America 1980-2018 (JSON format) J. C. Biard and K. E. Kunkel https://doi.org/10.5281/zenodo.2669180

DL-FRONT MERRA-2 vectorized weather fronts over North America 1980-2018 (netCDF format) J. C. Biard and K. E. Kunkel https://doi.org/10.5281/zenodo.2669505

DL-FRONT MERRA-2 weather front processing and analysis artifacts J. C. Biard and K. E. Kunkel https://doi.org/10.5281/zenodo.2712481

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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.