Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 08 02 2022
accepted: 11 07 2023
medline: 7 9 2023
pubmed: 5 9 2023
entrez: 5 9 2023
Statut: epublish

Résumé

Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.

Identifiants

pubmed: 37669263
doi: 10.1371/journal.pone.0291039
pii: PONE-D-22-03604
pmc: PMC10479889
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0291039

Informations de copyright

Copyright: © 2023 Mattern, Edwards. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

Ann Rev Mar Sci. 2015;7:21-42
pubmed: 25103331
PLoS One. 2019 Oct 16;14(10):e0223131
pubmed: 31618274

Auteurs

Jann Paul Mattern (JP)

Ocean Sciences Department, UC Santa Cruz, Santa Cruz, CA, United States of America.

Christopher A Edwards (CA)

Ocean Sciences Department, UC Santa Cruz, Santa Cruz, CA, United States of America.

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Classifications MeSH