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  • Jorge Pérez-Aracil

We introduce a novel deep learning (DL) method that significantly enhances the reconstruction of heatwaves, outperforming the traditional analogue approach. This method leverages historical models and ERA5 reanalysis data for improved accuracy and reliability.

Jorge Pérez-Aracil

Jorge Pérez-Aracil

Jorge Pérez Aracil is an Assistant Professor in the Department of Signal Theory and Communications at the University of Alcalá. His academic background includes a strong focus on electronic and communications engineering, and he is involved in teaching and research in the areas of acoustics, vibrations, and hybrid energy systems. Jorge has contributed to several scientific publications and actively participates in projects related to signal processing and communications engineering.

SUMMARY OF ACTIVITIES/INTERESTS

  • Climate Extremes
  • Natural Hazards
  • Machine Learning
  • Impact
  • Attribution