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PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning

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Abstract

Automated prediction of hurricane intensity from satellite infrared imagery is a challenging problem with implications in weather forecasting and disaster planning. In this work, a novel machine learning-based method for estimation of intensity or maximum sustained wind speed of tropical cyclones over their life cycle is presented. The approach is based on a support vector regression model over novel statistical features of infrared images of a hurricane. Specifically, the features characterize the degree of uniformity in various temperature bands of a hurricane. Performance of several machine learning methods such as ordinary least squares regression, backpropagation neural networks and XGBoost regression has been compared using these features under different experimental setups for the task. Kernelized support vector regression resulted in the lowest prediction error between true and predicted hurricane intensities (approximately 10 knots or 18.5 km/h), which is better than previously proposed techniques and comparable to SATCON consensus. The performance of the proposed scheme has also been analyzed with respect to errors in annotation of center of the hurricane and aircraft reconnaissance data. The source code and webserver implementation of the proposed method called PHURIE (PIEAS HURricane Intensity Estimator) is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#PHURIE.

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Acknowledgements

We are grateful to Dr. Charles Anderson, Colorado State University, USA, and Dr. John Knaff, National Oceanic and Atmospheric Administration, for discussion and suggestions. We will also like to acknowledge the internal reviewers of National Hurricane Center for their comments that helped us improve the quality of the manuscript.

Funding

AA, MD and BJ are funded via Information Technology and Telecommunication Endowment Fund at Pakistan Institute of Engineering and Applied Sciences.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), PO Nilore, Islamabad, Pakistan

    Amina Asif, Muhammad Dawood, Bismillah Jan, Javaid Khurshid & Fayyaz ul Amir Afsar Minhas

  2. National Hurricane Center, National Oceanic and Atmospheric Administration (NOAA), Miami, FL, USA

    Mark DeMaria

Authors
  1. Amina Asif
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  2. Muhammad Dawood
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  3. Bismillah Jan
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  4. Javaid Khurshid
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  5. Mark DeMaria
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  6. Fayyaz ul Amir Afsar Minhas
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Corresponding author

Correspondence to Fayyaz ul Amir Afsar Minhas.

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Asif, A., Dawood, M., Jan, B. et al. PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning. Neural Comput & Applic 32, 4821–4834 (2020). https://doi.org/10.1007/s00521-018-3874-6

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  • DOI: https://doi.org/10.1007/s00521-018-3874-6

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