EconPapers    
Economics at your fingertips  
 

Stochastic tropical cyclone precipitation field generation

William Kleiber, Stephan Sain, Luke Madaus and Patrick Harr

Environmetrics, 2023, vol. 34, issue 1

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.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1002/env.2766

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:envmet:v:34:y:2023:i:1:n:e2766

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1180-4009
subscrip@blackwellpub.com

Access Statistics for this article

More articles in Environmetrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery (contentdelivery@wiley.com).

 
Page updated 2024-12-29
Handle: RePEc:wly:envmet:v:34:y:2023:i:1:n:e2766