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Autoencoder-based flow-analogue probabilistic reconstruction of heat waves from pressure fields.

Jorge Pérez-Aracil1, Cosmin M Marina1, Eduardo Zorita2

  • 1Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain.

Annals of the New York Academy of Sciences
|October 30, 2024
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Summary

A new hybrid method combining deep autoencoders (AEs) and the analogue method (AM) improves meteorological field reconstruction. This AE-AM approach enhances the accuracy of predicting temperature during heat waves compared to traditional methods.

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analogue methodautoencodersfield reconstructionheat waves

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Area of Science:

  • Meteorology
  • Data Science
  • Machine Learning

Background:

  • Accurate reconstruction of meteorological fields is crucial for weather forecasting and climate studies.
  • Traditional methods like the analogue method (AM) can be limited by high-dimensional, noisy predictor data.

Purpose of the Study:

  • To introduce and evaluate a novel hybrid approach, AE-AM, for probabilistic reconstruction of meteorological fields.
  • To compare the performance of AE-AM against the classical AM in reconstructing temperature fields during heat waves.

Main Methods:

  • A deep autoencoder (AE) is trained on predictor fields to create a compressed latent space.
  • The analogue method (AM) is applied within this latent space to identify historical analogues for reconstruction.
  • The AE-AM approach is tested on reconstructing daily maximum temperature from sea-level pressure during European heat waves (1950-2010).

Main Results:

  • The AE-AM approach significantly outperforms the standard AM in reconstructing the magnitude and spatial patterns of heat wave temperature events.
  • Improvements in skill score ranged from 7% to 22%, varying by the specific heat wave analyzed.
  • The AE-AM method demonstrated enhanced reconstruction accuracy by filtering irrelevant information in the predictor fields.

Conclusions:

  • The hybrid AE-AM method offers a significant advancement over classical AM for meteorological field reconstruction.
  • Deep autoencoders effectively reduce dimensionality and extract relevant features, improving analogue-based predictions.
  • This approach shows considerable potential for improving the probabilistic reconstruction of extreme weather events.