Speeding up the ensemble data assimilation system of the limited‐area model of Météo‐France using a block Krylov algorithm
Author:
Affiliation:
1. CNRM, Météo‐France / CNRS Toulouse France
2. IRIT, CNRS Toulousea France
3. Cerfacs Toulouse France
Funder
RTRA-STAE foundation
Publisher
Wiley
Subject
Atmospheric Science
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3428
Reference38 articles.
1. A parallel implementation of a 4DEnVar ensemble
2. Ensemble–Variational Integrated Localized Data Assimilation
3. A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics
4. Bonavita M. Trémolet Y. Holm E. Lang S. Chrust M. Janisková M. Lopez P. Laloyaux P. de Rosnay P. Fisher M. Hamrud M.andEnglish S.(2017).A strategy for data assimilation. European Centre for Medium‐Range Weather Forecasts Technical Memorandum 800.
5. Background-error covariances for a convective-scale data-assimilation system: AROME-France 3D-Var
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3. A Square-Root, Dual-Resolution 3DEnVar for the AROME Model: Formulation and Evaluation on a Summertime Convective Period;Monthly Weather Review;2021-09
4. Enhanced parallelization of the incremental 4D‐Var data assimilation algorithm using the Randomized Incremental Optimal Technique;Quarterly Journal of the Royal Meteorological Society;2020-02-05
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