Profile directory About Mobile apps
Log in Sign up
arXiv Statistics @arxiv_stats@qoto.org
Follow

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays. (arXiv:2311.12909v1 [stat.ML]) http://arxiv.org/abs/2311.12909

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays

This work introduces a new, distributed implementation of the Ensemble Kalman Filter (EnKF) that allows for non-sequential assimilation of large datasets in high-dimensional problems. The traditional EnKF algorithm is computationally intensive and exhibits difficulties in applications requiring interaction with the background covariance matrix, prompting the use of methods like sequential assimilation which can introduce unwanted consequences, such as dependency on observation ordering. Our implementation leverages recent advancements in distributed computing to enable the construction and use of the full model error covariance matrix in distributed memory, allowing for single-batch assimilation of all observations and eliminating order dependencies. Comparative performance assessments, involving both synthetic and real-world paleoclimatic reconstruction applications, indicate that the new, non-sequential implementation outperforms the traditional, sequential one.

arxiv.org
November 23, 2023 at 3:20 AM · · feed2toot · 0 · 0 · 0
Sign in to participate in the conversation
Qoto Mastodon

QOTO: Question Others to Teach Ourselves
An inclusive, Academic Freedom, instance
All cultures welcome.
Hate speech and harassment strictly forbidden.

Resources

  • Terms of service
  • Privacy policy

Developers

  • Documentation
  • API

What is Mastodon?

qoto.org

  • About
  • v3.5.19-qoto

More…

  • Source code
  • Mobile apps
v3.5.19-qoto · Privacy policy