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Data science and climate risk analytics

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  • Stephan R. Sain

Abstract

With influences from different communities, data science has evolved to provide insights in many different data‐driven environments, including climate science. In this article, a brief review of data science and its connection to climate science will be presented. Additionally, two data science pipelines for quantifying risks from climate change are discussed. These pipelines focus on flooding due to tropical cyclone storm surge and changes in the distribution of temperature or precipitation or wind due to climate change via downscaling climate models. Finally, some key data science research areas in climate risk analytics are discussed.

Suggested Citation

  • Stephan R. Sain, 2023. "Data science and climate risk analytics," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    • Handle: RePEc:wly:envmet:v:34:y:2023:i:2:n:e2749
    DOI: 10.1002/env.2749
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    References listed on IDEAS

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    1. William S. Cleveland, 2001. "Data Science: an Action Plan for Expanding the Technical Areas of the Field of Statistics," International Statistical Review, International Statistical Institute, vol. 69(1), pages 21-26, April.
    2. Tanya Fiedler & Andy J. Pitman & Kate Mackenzie & Nick Wood & Christian Jakob & Sarah E. Perkins-Kirkpatrick, 2021. "Business risk and the emergence of climate analytics," Nature Climate Change, Nature, vol. 11(2), pages 87-94, February.
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    Cited by:

    1. Wesley S. Burr & Nathaniel K. Newlands & Andrew Zammit‐Mangion, 2023. "Environmental data science: Part 2," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.

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