Application of Recurrent Neural Networks for Drought Projections in California

Authors

J. A. Le, Chapman University
Hesham el-Askary, Chapman UniversityFollow
Mohamed Allali, Chapman UniversityFollow
Daniele C. Struppa, Chapman UniversityFollow

Document Type

Article

Publication Date

1-6-2017

Abstract

We use recurrent neural networks (RNNs) to investigate the complex interactions between the long-term trend in dryness and a projected, short but intense, period of wetness due to the 2015-2016 El Niño. Although it was forecasted that this El Niño season would bring significant rainfall to the region, our long-term projections of the Palmer Z Index (PZI) showed a continuing drought trend, contrasting with the 1998-1999 El Niño event. RNN training considered PZI data during 1896-2006 that was validated against the 2006-2015 period to evaluate the potential of extreme precipitation forecast. We achieved a statistically significant correlation of 0.610 between forecasted and observed PZI on the validation set for a lead time of 1 month. This gives strong confidence to the forecasted precipitation indicator. The 2015-2016 El Niño season proved to be relatively weak as compared with the 1997-1998, with a peak PZI anomaly of 0.242 standard deviations below historical averages, continuing drought conditions.

Comments

NOTICE: this is the author’s version of a work that was accepted for publication in Atmospheric Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Atmospheric Research, volume 188, in 2017. DOI: 10.1016/j.atmosres.2017.01.002

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Recommended Citation

Le, J.A., El-Askary, H.M., Allali, M., Struppa, D.C., 2017. Application of recurrent neural networks for drought projections in California. Atmospheric Research 188, 100–106. doi:10.1016/j.atmosres.2017.01.002

Peer Reviewed

1

Copyright

Elsevier

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.