mlsanityData to 7 Oct 2026

Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders

arXiv 2006.01101 ↗First posted 1 Jun 2020physics.comp-ph cs.LG stat.COcited by 11

Not on the map. Why?

Abstract (arXiv)

It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs; a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz-`96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly-sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 4; 2022: 0; 2023: 3; 2024: 1; 2025: 3.

11 papers.

  1. 2021Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformerscited by 15
  2. 2021Bridging observation, theory and numerical simulation of the ocean using Machine Learningcited by 15
  3. 2023A VAE Approach to Sample Multivariate Extremescited by 9
  4. 2021Machine Learning Techniques to Construct Patched Analog Ensembles for Data Assimilationcited by 7
  5. 2021Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantificationcited by 7
  6. 2025Ensemble Kalman filter in latent space using a variational autoencoder paircited by 5
  7. 2025Latent Twinscited by 3
  8. 2023Online machine-learning forecast uncertainty estimation for sequential data assimilationcited by 2
  9. 2025Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methodscited by 1
  10. 2023Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic modelcited by 0
  11. 2024U-Net Kalman Filter (UNetKF): An Example of Machine Learning-assisted Ensemble Data Assimilationcited by 0