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

The model's ability to reproduce the state of the simulated object or particular feature or phenomenon is always a subject of discussion. Multidimensional model quality assessment is usually customized for the specific focus of the study and often for a limited number of locations. In this pape...

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Veröffentlicht in:Geoscientific Model Development Jg. 15; H. 2; S. 535 - 551
Hauptverfasser: Raudsepp, Urmas, Maljutenko, Ilja
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Katlenburg-Lindau Copernicus GmbH 25.01.2022
Copernicus Publications
Schlagworte:
Accuracy
Algorithms
Analysis
Case studies
Centroids
Cluster analysis
Clustering
Dimensional analysis
Elbow
Errors
General circulation models
Learning algorithms
Machine learning
Methods
Model accuracy
Quality assessment
Quality control
Rivers
Saline water
Salinity
Salinity effects
Simulation
Temperature
Variables
Vector quantization
Gulf of Riga
North Sea
Gulf of Bothnia
Gulf of Finland
Baltic Sea
Gotland (island)
ISSN:1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X
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Zusammenfassung:The model's ability to reproduce the state of the simulated object or particular feature or phenomenon is always a subject of discussion. Multidimensional model quality assessment is usually customized for the specific focus of the study and often for a limited number of locations. In this paper, we propose a method that provides information on the accuracy of the model in general, while all dimensional information for posterior analysis of the specific tasks is retained. The main goal of the method is to perform clustering of the multivariate model errors. The clustering is done using the K-means algorithm of unsupervised machine learning. In addition, the potential application of the K-means clustering of model errors for learning and predicting is shown. The method is tested on the 40-year simulation results of the general circulation model of the Baltic Sea. The model results are evaluated with the measurement data of temperature and salinity from more than 1 million casts by forming a two-dimensional error space and performing a clustering procedure in it. The optimal number of clusters that consist of four clusters was determined using the Elbow cluster selection criteria and based on the analysis of the different number of error clusters. In this particular model, the error cluster with good quality of the model with a bias of 0.4 ∘C (SD = 0.8 ∘C) for temperature and 0.6 g kg−1 (SD = 0.7 g kg−1) for salinity made up 57 % of all comparison data pairs. The prediction of centroids from a limited number of randomly selected data showed that the obtained centroids gained a stability of at least 100 000 error pairs in the learning dataset.
Bibliographie:ObjectType-Case Study-2
SourceType-Scholarly Journals-1
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ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-15-535-2022