Articles | Volume 30, issue 2
https://doi.org/10.5194/npg-30-217-2023
https://doi.org/10.5194/npg-30-217-2023
Review article
 | 
28 Jun 2023
Review article |  | 28 Jun 2023

Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning

Eugenia Kalnay, Travis Sluka, Takuma Yoshida, Cheng Da, and Safa Mote

Data sets

travissluka/CFSv2-LETKF: v0.1 (v0.1) Travis Sluka, Cheng Da, Kriti Bhargava, and Steve Penny https://doi.org/10.5281/zenodo.8077444

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Short summary
Strongly coupled data assimilation (SCDA) generates coherent integrated Earth system analyses by assimilating the full Earth observation set into all Earth components. We describe SCDA based on the ensemble Kalman filter with a hierarchy of coupled models, from a coupled Lorenz to the Climate Forecast System v2. SCDA with a sufficiently large ensemble can provide more accurate coupled analyses compared to weakly coupled DA. The correlation-cutoff method can compensate for a small ensemble size.