A Quantile-Conserving Ensemble Filter Based on Kernel-Density Estimation Journal Article uri icon

Overview

abstract

  • Ensemble Kalman filters are an efficient class of algorithms for large-scale ensemble data assimilation, but their performance is limited by their underlying Gaussian approximation. A two-step framework for ensemble data assimilation allows this approximation to be relaxed: The first step updates the ensemble in observation space, while the second step regresses the observation state update back to the state variables. This paper develops a new quantile-conserving ensemble filter based on kernel-density estimation and quadrature for the scalar first step of the two-step framework. It is shown to perform well in idealized non-Gaussian problems, as well as in an idealized model of assimilating observations of sea-ice concentration.

publication date

  • June 28, 2024

has restriction

  • gold

Date in CU Experts

  • July 10, 2024 6:40 AM

Full Author List

  • Grooms I; Riedel C

author count

  • 2

Other Profiles

Electronic International Standard Serial Number (EISSN)

  • 2072-4292

Additional Document Info

start page

  • 2377

end page

  • 2377

volume

  • 16

issue

  • 13