Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 30, issue 2
Articles | Volume 30, issue 2
https://doi.org/10.5194/npg-30-217-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/npg-30-217-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 2
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

Viewed

Total article views: 2,395 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,896 452 47 2,395 35 39
  • HTML: 1,896
  • PDF: 452
  • XML: 47
  • Total: 2,395
  • BibTeX: 35
  • EndNote: 39
Views and downloads (calculated since 11 Jan 2023)
Cumulative views and downloads (calculated since 11 Jan 2023)

Viewed (geographical distribution)

Total article views: 2,395 (including HTML, PDF, and XML) Thereof 2,279 with geography defined and 116 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
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.
Read more
Special issue
Interdisciplinary perspectives on climate sciences – highlighting...