On the ensemble-based linearization of numerical models

Yaremchuk, Max; Nechaev, Dmitri; Frolov, Sergey

doi:10.1002/qj.3723

i

On the ensemble-based linearization of numerical models

2020
By Yaremchuk, Max ; Nechaev, Dmitri ; Frolov, Sergey

Details

Journal Title:

Quarterly Journal fo the Royal Meteorological Society
Personal Author:

Yaremchuk, Max ; Nechaev, Dmitri ; Frolov, Sergey
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)
Description:

Current parallelization trends in computer technology facilitates development of the algorithms that retrieve linear approximations of the model operators and their adjoints from ensembles of model simulations. In this study we address the problem of obtaining exact linearizations in the presence of semi-implicit numerics of the parent model under realistic constraints on the ensemble size. The method is based on factorization of the model into a sequence of local and non-local linear operators and employs prior information on the structure of the respective sparse matrices. The performance of the method is tested using 28 perturbed solutions of the shallow-water equations with a moderate size (urn:x-wiley:qj:media:qj3723:qj3723-math-0001) state vector. Numerical experiments have shown feasibility of the approach under relatively general constraints on the structure of the parent model. Because of the substantial expense of the ensemble-based linearization, special focus is made on the assessment of the optimal frequency of such computations within the time intervals between data injections in typical operational systems.
Keywords:

Ensemble Methods Variational Data Assimiliation Ensemble Methods Variational Data Assimiliation
Source:

QJR Meteorol Soc. 2020; 146: 1026– 1039.
DOI:

https://doi.org/10.1002/qj.3723
Document Type:

Journal Article
Rights Information:

Public Domain
Compliance:

Submitted
Main Document Checksum:

urn:sha256:843084062af3908463a70fec57cff9d881e9a65a46854259c35a9a33759cead5
Download URL:

https://repository.library.noaa.gov/view/noaa/31004/noaa_31004_DS1.pdf
File Type:

[PDF - 2.96 MB ]

ON THIS PAGE

Details

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.