Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods

TL;DR

This paper introduces two novel autoencoder-based methods for reducing the dimensionality of ensemble forecast fields, effectively capturing their probabilistic nature and key spatial features to facilitate downstream processing.

Contribution

The study develops and compares two new frameworks tailored for ensemble forecast data, addressing limitations of existing methods for probabilistic and high-dimensional data.

Findings

Both methods preserve key spatial and statistical features of the ensemble.

The autoencoder-based approach enables probabilistic reconstruction of forecast fields.

The methods effectively reduce data volume for large ensemble datasets.

Abstract

Large-scale numerical simulations often produce high-dimensional gridded data that is challenging to process for downstream applications. A prime example is numerical weather prediction, where atmospheric processes are modeled using discrete gridded representations of the physical variables and dynamics. Uncertainties are assessed by running the simulations multiple times, yielding ensembles of simulated fields as a high-dimensional stochastic representation of the forecast distribution. The high-dimensionality and large volume of ensemble datasets poses major computing challenges for subsequent forecasting stages. Data-driven dimensionality reduction techniques could help to reduce the data volume before further processing by learning meaningful and compact representations. However, existing dimensionality reduction methods are typically designed for deterministic and single-valued inputs, and thus cannot handle ensemble data from multiple randomized simulations. In this study, we propose novel dimensionality reduction approaches specifically tailored to the format of ensemble forecast fields. We present two alternative frameworks, which yield low-dimensional representations of ensemble forecasts while respecting their probabilistic character. The first approach derives a distribution-based representation of an input ensemble by applying standard dimensionality reduction techniques in a member-by-member fashion and merging the member representations into a joint parametric distribution model. The second approach achieves a similar representation by encoding all members jointly using a tailored variational autoencoder. We evaluate and compare both approaches in a case study using 10 years of temperature and wind speed forecasts over Europe. The approaches preserve key spatial and statistical characteristics of the ensemble and enable probabilistic reconstructions of the forecast fields.