Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-535-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-535-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
Methods for assessment of models
 | 
25 Jan 2022
Methods for assessment of models |  | 25 Jan 2022

A method for assessment of the general circulation model quality using the K-means clustering algorithm: a case study with GETM v2.5

Urmas Raudsepp and Ilja Maljutenko

Data sets

Data for A method for assessment of the general circulation model quality using K-means clustering algorithm Ilja Maljutenko https://doi.org/10.5281/zenodo.4588510

Model code and software

Source code for the GETM and GOTM software Ilja Maljutenko https://doi.org/10.5281/zenodo.5267002

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Short summary
A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
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