A multi-site stochastic weather generator for high-frequency precipitation using censored skew-symmetric distribution
Type
ArticleAuthors
Li, Yuxiao
Sun, Ying

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
KAUST Grant Number
OSR-2019-CRG7-3800Date
2020-10-08Embargo End Date
2022-11-01Submitted Date
2020-04-24Permanent link to this record
http://hdl.handle.net/10754/661040
Metadata
Show full item recordAbstract
Stochastic weather generators (SWGs) are digital twins of complex weather processes and widely used in agriculture and urban design. Due to improved measuring instruments, an accurate SWG for high-frequency precipitation is now possible. However, high-frequency precipitation data are more zero-inflated, skewed, and heavy-tailed than common (hourly or daily) precipitation data. Therefore, classical methods that either model precipitation occurrence independently of their intensity or assume that the precipitation follows a censored meta-Gaussian process may not be appropriate. In this work, we propose a novel multi-site precipitation generator that drives both occurrence and intensity by a censored non-Gaussian vector autoregression model with skew-symmetric dynamics. The proposed SWG is advantageous in modeling skewed and heavy-tailed data with direct physical and statistical interpretations. We apply the proposed model to 30-second precipitation based on the data obtained from a dense gauge network in Lausanne, Switzerland. In addition to reproducing the high-frequency precipitation, the model can provide accurate predictions as the long short-term memory (LSTM) network but with uncertainties and more interpretable results.Citation
Li, Y., & Sun, Y. (2021). A multi-site stochastic weather generator for high-frequency precipitation using censored skew-symmetric distribution. Spatial Statistics, 41, 100474. doi:10.1016/j.spasta.2020.100474Sponsors
This research was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Office of Sponsored Research (OSR) under Award No.: OSR-2019-CRG7-3800.This research was supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No.: OSR-2019-CRG7-3800. The dataset used in this study is provided by GAIA Lab, Institute of Earth Surface Dynamics (IDYST), the University of Lausanne. We acknowledge their efforts for collecting the data.Publisher
Elsevier BVJournal
Spatial StatisticsarXiv
1912.11833Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S2211675320300683ae974a485f413a2113503eed53cd6c53
10.1016/j.spasta.2020.100474