A multi-site stochastic weather generator for high-frequency precipitation using censored skew-symmetric distribution
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Environmental Statistics Group
KAUST Grant NumberOSR-2019-CRG7-3800
Preprint Posting Date2019-12-26
Online Publication Date2020-10-08
Print Publication Date2021-03
Embargo End Date2022-11-01
Permanent link to this recordhttp://hdl.handle.net/10754/661040
MetadataShow full item record
AbstractStochastic 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.
CitationLi, 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.2020.100474
SponsorsThis 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.