EGU24-12600, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Wednesday, 17 Apr, 10:45–12:30 (CEST), Display time Wednesday, 17 Apr, 08:30–12:30
Hall X5, X5.152

Autoencoder-based model for improving  reconstruction of heat waves using the analogue method

Jorge Pérez-Aracil1, Cosmin M. Marina1, Pedro Gutiérrez2, David Barriopedro3, Ricardo García-Herrera7, Matteo Giuliani4, Ronan McAdam5, Enrico Scoccimarro5, Eduardo Zorita6, Andrea Castelletti4, and Sancho Salcedo-Sanz1
Jorge Pérez-Aracil et al.
  • 1Universidad de Alcalá, Signal Processing and Communications, Spain
  • 2Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain.
  • 3Instituto de Geociencias (IGEO), Consejo Superior de Investigaciones Científicas–Universidad Complutense de Madrid (CSIC–UCM), Madrid, Spain.
  • 4Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy.
  • 5Climate Variability and Prediction Division, Fondazione Euro-Mediterraneo sui Cambiamenti Climatici - CMCC, Bologna, Italy.
  • 6Helmholtz-Zentrum Geesthacht, Hamburg, Germany
  • 7Departamento de Física de la Tierra y Astrofísica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid, Spain

The Analogue Method (AM) is a classical statistical downscaling technique applied to field reconstruction. It is widely used for prediction and attribution tasks. The method is based on the principle that two similar atmospheric states cause similar local effects. The core of the AM method is a K-nearest neighbor methodology. Thus, two different states have similarities according to the analogy criterion. The method has remained unchanged since its definition, although some attempts have been made to improve its performance. Machine learning (ML) techniques have recently been used to improve AM performance, however, it remains very similar. An ML-based hybrid approach for heatwave (HW) analysis based on the AM is presented here. It is based on a two-step procedure: in the first step, a non-supervised task is developed, where an autoencoder (AE) model is trained to reconstruct the predictor variable, i.e. the pressure field. Second, an HW event is selected, and then the AM method is applied to the latent space of the trained AE. Thus, the analogy between the fields is searched in the encoded data of the input variable, instead of on the original field. Experiments show that the meaningful features extracted by the AE lead to a better reconstruction of the target field when pressure variables are used as input. In addition, the analysis of the latent space allows for interpreting the results, since HW occurrence can be easily distinguished. Further research can be done on including multiple input variables. 

How to cite: Pérez-Aracil, J., Marina, C. M., Gutiérrez, P., Barriopedro, D., García-Herrera, R., Giuliani, M., McAdam, R., Scoccimarro, E., Zorita, E., Castelletti, A., and Salcedo-Sanz, S.: Autoencoder-based model for improving  reconstruction of heat waves using the analogue method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12600,, 2024.