%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 }