Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection
This work addresses heatwave prediction for climate science and societal impact, but appears incremental as it builds on existing methods like clustering and evolutionary algorithms.
The paper tackles the problem of predicting heatwaves by identifying key drivers using a novel spatio-temporal framework, applied to the Adda river basin in Italy, but does not report concrete numerical results.
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.