Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-5959-2020
https://doi.org/10.5194/gmd-13-5959-2020
Methods for assessment of models
 | 
01 Dec 2020
Methods for assessment of models |  | 01 Dec 2020

Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation

Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski

Viewed

Total article views: 2,965 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,202 685 78 2,965 153 102 119
  • HTML: 2,202
  • PDF: 685
  • XML: 78
  • Total: 2,965
  • Supplement: 153
  • BibTeX: 102
  • EndNote: 119
Views and downloads (calculated since 02 Jul 2020)
Cumulative views and downloads (calculated since 02 Jul 2020)

Viewed (geographical distribution)

Total article views: 2,965 (including HTML, PDF, and XML) Thereof 2,794 with geography defined and 171 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 02 Apr 2025
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
Share