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
Embargo End Date2022-11-01
Permanent link to this recordhttp://hdl.handle.net/10754/661040
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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.