Data Assimilation Stability

Data Assimilation Stability is a theoretical construct in numerical analysis that characterizes the robustness of a data assimilation algorithm in maintaining consistent and bounded state estimates under perturbations in observational or model inputs, distinguishing it from general numerical stability by its specific focus on the interaction between observational data and dynamical model fidelity.

35

Publications

1.4K

Citations

92

Authors

64

Institutions

Publications per year

2017–2026

11

Authors

92

  1. AC
    Alberto Carrassi

    Nansen Environmental and Remote Sensing Center

    3 publications · 94 citations

    3

    h-index

  2. CG
    Colin Grudzien

    Nansen Environmental and Remote Sensing Center

    2 publications · 50 citations

    2

    h-index

  3. YL
    Yibao Li

    Xi'an Jiaotong University, China

    2 publications · 70 citations

    2

    h-index

  4. GA
    Georg A. Gottwald

    The University of Sydney, Australia

    2 publications · 75 citations

    2

    h-index

  5. AS
    Amos S. Lawless

    University of Reading, United Kingdom

    2 publications · 77 citations

    2

    h-index

1–5 of 92

Venues

  1. Monthly Weather Review

    7 publications · 181 citations

    7

    h-index

  2. Inverse Problems

    2 publications · 36 citations

    2

    h-index

  3. Tellus A Dynamic Meteorology and Oceanography

    2 publications · 64 citations

    2

    h-index

  4. Nonlinear processes in geophysics

    2 publications · 202 citations

    2

    h-index

  5. Ocean Modelling

    1 publications · 44 citations

    1

    h-index

1–5