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Stochastic tropical cyclone precipitation field generation

Author

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  • William Kleiber
  • Stephan Sain
  • Luke Madaus
  • Patrick Harr

Abstract

Tropical cyclones are important drivers of coastal flooding which have severe negative public safety and economic consequences. Due to the rare occurrence of such events, high spatial and temporal resolution historical storm precipitation data are limited in availability. This article introduces a statistical tropical cyclone space‐time precipitation generator given limited information from storm track datasets. Given a handful of predictor variables that are common in either historical or simulated storm track ensembles such as pressure deficit at the storm's center, radius of maximal winds, storm center and direction, and distance to coast, the proposed stochastic model generates space‐time fields of quantitative precipitation over the study domain. Statistically novel aspects include that the model is developed in Lagrangian coordinates with respect to the dynamic storm center that uses ideas from low‐rank representations along with circular process models. The model is trained on a set of tropical cyclone data from an advanced weather forecasting model over the Gulf of Mexico and southern United States, and is validated by cross‐validation. Results show the model appropriately captures spatial asymmetry of cyclone precipitation patterns, total precipitation as well as the local distribution of precipitation at a set of case study locations along the coast. We additionally compare our model against a widely‐used statistical forecast, and illustrate that our approach better captures uncertainty, as well as storm characteristics such as asymmetry.

Suggested Citation

  • William Kleiber & Stephan Sain & Luke Madaus & Patrick Harr, 2023. "Stochastic tropical cyclone precipitation field generation," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
  • Handle: RePEc:wly:envmet:v:34:y:2023:i:1:n:e2766
    DOI: 10.1002/env.2766
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    References listed on IDEAS

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    4. Thomas Wahl & Shaleen Jain & Jens Bender & Steven D. Meyers & Mark E. Luther, 2015. "Increasing risk of compound flooding from storm surge and rainfall for major US cities," Nature Climate Change, Nature, vol. 5(12), pages 1093-1097, December.
    5. Daniel R. Chavas & Kevin A. Reed & John A. Knaff, 2017. "Physical understanding of the tropical cyclone wind-pressure relationship," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    6. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
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    Cited by:

    1. Andrew Zammit‐Mangion & Nathaniel K. Newlands & Wesley S. Burr, 2023. "Environmental data science: Part 1," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    2. Caitlin M. Berry & William Kleiber & Bri‐Mathias Hodge, 2023. "Subordinated Gaussian processes for solar irradiance," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    3. S. R. Johnson & S. E. Heaps & K. J. Wilson & D. J. Wilkinson, 2023. "A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.

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