Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915022027882496 |
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| author | Pérez-Aracil, J. Peláez-Rodríguez, C. McAdam, Ronan Squintu, Antonello Marina, Cosmin M. Lorente-Ramos, Eugenio Luther, Niklas Torralba, Veronica Scoccimarro, Enrico Cavicchia, Leone Giuliani, Matteo Zorita, Eduardo Hansen, Felicitas Barriopedro, David Garcia-Herrera, Ricardo Gutiérrez, Pedro A. Luterbacher, Jürg Xoplaki, Elena Castelletti, Andrea Salcedo-Sanz, S. |
| author_facet | Pérez-Aracil, J. Peláez-Rodríguez, C. McAdam, Ronan Squintu, Antonello Marina, Cosmin M. Lorente-Ramos, Eugenio Luther, Niklas Torralba, Veronica Scoccimarro, Enrico Cavicchia, Leone Giuliani, Matteo Zorita, Eduardo Hansen, Felicitas Barriopedro, David Garcia-Herrera, Ricardo Gutiérrez, Pedro A. Luterbacher, Jürg Xoplaki, Elena Castelletti, Andrea Salcedo-Sanz, S. |
| contents | Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection Pérez-Aracil, J. Peláez-Rodríguez, C. McAdam, Ronan Squintu, Antonello Marina, Cosmin M. Lorente-Ramos, Eugenio Luther, Niklas Torralba, Veronica Scoccimarro, Enrico Cavicchia, Leone Giuliani, Matteo Zorita, Eduardo Hansen, Felicitas Barriopedro, David Garcia-Herrera, Ricardo Gutiérrez, Pedro A. Luterbacher, Jürg Xoplaki, Elena Castelletti, Andrea Salcedo-Sanz, S. Atmospheric and Oceanic Physics Artificial Intelligence Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability. |
| title | Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2411.10108 |