Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models.
%0 Journal Article
%1 ng2022causalityguided
%A Ng, Kelvin S.
%A Leckebusch, Gregor C.
%A Hodges, Kevin I.
%D 2022
%J Advances in Atmospheric Sciences
%K China MyYangtzeWork eastasianmonsoon monsoon statistics
%R 10.1007/s00376-022-1348-3
%T A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
%U https://doi.org/10.1007/s00376-022-1348-3
%X Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models.
@article{ng2022causalityguided,
abstract = {Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models.},
added-at = {2022-05-15T14:13:43.000+0200},
author = {Ng, Kelvin S. and Leckebusch, Gregor C. and Hodges, Kevin I.},
biburl = {https://www.bibsonomy.org/bibtex/237f030819e73602bdf3429e913d943c6/pbett},
day = 14,
doi = {10.1007/s00376-022-1348-3},
interhash = {fd288b8332ee5cb5f7e999bfe2e7ded6},
intrahash = {37f030819e73602bdf3429e913d943c6},
issn = {1861-9533},
journal = {Advances in Atmospheric Sciences},
keywords = {China MyYangtzeWork eastasianmonsoon monsoon statistics},
month = may,
timestamp = {2022-05-19T10:35:13.000+0200},
title = {A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study},
url = {https://doi.org/10.1007/s00376-022-1348-3},
year = 2022
}