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Discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources

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  • Andrew Zammit‐Mangion

Abstract

Statistical spatiotemporal environmental data analysis is rarely straightforward, with one having to face challenges relating to big data, non‐Gaussianity, nonstationarity, multiple scales of behavior, deterministic (numerical) model output, and more. One often has to rely heavily on good statistical parallel computing skills and sound knowledge of the application domain. The work of Tagle et al. (2020) overcomes all of these challenges, and is an excellent example of the tangible contributions spatiotemporal modeling and distribution theory can make to the environmental sciences at the policy level. In this discussion piece I focus on a few high‐level concepts in the paper of Tagle et al. (2020) that are relevant to related application domains. I also provide some technical suggestions that could be used to facilitate inference.

Suggested Citation

  • Andrew Zammit‐Mangion, 2020. "Discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    • Handle: RePEc:wly:envmet:v:31:y:2020:i:7:n:e2649
    DOI: 10.1002/env.2649
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    References listed on IDEAS

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    1. Andrew Zammit‐Mangion & Jonathan Rougier & Nana Schön & Finn Lindgren & Jonathan Bamber, 2015. "Multivariate spatio‐temporal modelling for assessing Antarctica's present‐day contribution to sea‐level rise," Environmetrics, John Wiley & Sons, Ltd., vol. 26(3), pages 159-177, May.
    2. Botond Cseke & Andrew Zammit-Mangion & Tom Heskes & Guido Sanguinetti, 2016. "Sparse Approximate Inference for Spatio-Temporal Point Process Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1746-1763, October.
    3. Zhenzhong Zeng & Alan D. Ziegler & Timothy Searchinger & Long Yang & Anping Chen & Kunlu Ju & Shilong Piao & Laurent Z. X. Li & Philippe Ciais & Deliang Chen & Junguo Liu & Cesar Azorin-Molina & Adria, 2019. "A reversal in global terrestrial stilling and its implications for wind energy production," Nature Climate Change, Nature, vol. 9(12), pages 979-985, December.
    4. Mahbubul Majumder & Heike Hofmann & Dianne Cook, 2013. "Validation of Visual Statistical Inference, Applied to Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 942-956, September.
    5. Felipe Tagle & Marc G. Genton & Andrew Yip & Suleiman Mostamandi & Georgiy Stenchikov & Stefano Castruccio, 2020. "A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
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