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

2025-02-06

AI Summary

To address the challenge of compressing high-dimensional gridded data in meteorological ensemble forecasts—where conventional dimensionality reduction methods fail to simultaneously preserve probabilistic characteristics and spatial structure—this paper proposes two dedicated dimensionality reduction approaches for probabilistic ensemble data: (1) member-level dimensionality reduction followed by parametric probabilistic modeling, and (2) a joint encoding framework based on a customized variational autoencoder (VAE), the first VAE adaptation to ensemble forecasting that explicitly models spatial correlations and ensemble spread. The proposed distributional modeling paradigm overcomes the limitation of traditional methods (e.g., PCA), which are designed only for deterministic, single-valued fields. Evaluated on a decade of European temperature and wind ensemble forecast data, the methods achieve >90% dimensionality reduction while accurately preserving quantile features, spatial covariance structures, and ensemble dispersion, enabling high-fidelity probabilistic reconstruction.

Technology Category

• Machine Learning: Ensemble Methods
• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Data Compression

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Reduce high-dimensional ensemble forecast data
• Handle uncertainties in numerical weather prediction
• Develop probabilistic dimensionality reduction methods

Innovation

• Autoencoder-based dimensionality reduction
• Handling ensemble forecast fields
• Probabilistic reconstruction of forecasts