Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-535-2022
https://doi.org/10.5194/gmd-15-535-2022
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

Related authors

Oceanographic preconditions for planning seawater heat pumps in the Baltic Sea – an example from the Tallinn Bay, Gulf of Finland
Jüri Elken, Ilja Maljutenko, Priidik Lagemaa, Rivo Uiboupin, and Urmas Raudsepp
State Planet, 4-osr8, 9, https://doi.org/10.5194/sp-4-osr8-9-2024,https://doi.org/10.5194/sp-4-osr8-9-2024, 2024
Short summary
Baltic Sea surface temperature analysis 2022: a study of marine heatwaves and overall high seasonal temperatures
Anja Lindenthal, Claudia Hinrichs, Simon Jandt-Scheelke, Tim Kruschke, Priidik Lagemaa, Eefke M. van der Lee, Ilja Maljutenko, Helen E. Morrison, Tabea R. Panteleit, and Urmas Raudsepp
State Planet, 4-osr8, 16, https://doi.org/10.5194/sp-4-osr8-16-2024,https://doi.org/10.5194/sp-4-osr8-16-2024, 2024
Short summary
The state of the ocean in the northeastern Atlantic and adjacent seas
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024,https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
A new conceptual framework for assessing the state of the Baltic Sea
Urmas Raudsepp, Ilja Maljutenko, Priidik Lagemaa, and Karina von Schuckmann
State Planet Discuss., https://doi.org/10.5194/sp-2024-19,https://doi.org/10.5194/sp-2024-19, 2024
Preprint under review for SP
Short summary
Baltic Sea freshwater content
Urmas Raudsepp, Ilja Maljutenko, Amirhossein Barzandeh, Rivo Uiboupin, and Priidik Lagemaa
State Planet, 1-osr7, 7, https://doi.org/10.5194/sp-1-osr7-7-2023,https://doi.org/10.5194/sp-1-osr7-7-2023, 2023
Short summary

Related subject area

Numerical methods
The Measurement Error Proxy System Model: MEPSM v0.2
Matt J. Fischer
Geosci. Model Dev., 17, 6745–6760, https://doi.org/10.5194/gmd-17-6745-2024,https://doi.org/10.5194/gmd-17-6745-2024, 2024
Short summary
Numerical stabilization methods for level-set-based ice front migration
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024,https://doi.org/10.5194/gmd-17-6227-2024, 2024
Short summary
Modelling chemical advection during magma ascent
Hugo Dominguez, Nicolas Riel, and Pierre Lanari
Geosci. Model Dev., 17, 6105–6122, https://doi.org/10.5194/gmd-17-6105-2024,https://doi.org/10.5194/gmd-17-6105-2024, 2024
Short summary
Consistent point data assimilation in Firedrake and Icepack
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
Geosci. Model Dev., 17, 5369–5386, https://doi.org/10.5194/gmd-17-5369-2024,https://doi.org/10.5194/gmd-17-5369-2024, 2024
Short summary
A Joint Reconstruction and Model Selection Approach for Large Scale Inverse Modeling
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot Miller, and Arvind Saibaba
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-90,https://doi.org/10.5194/gmd-2024-90, 2024
Revised manuscript accepted for GMD
Short summary

Cited articles

Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) – Snapshot of Argo GDAC of August 10st 2020, SEANOE [data set], https://doi.org/10.17882/42182#76230, 2020. 
Bholowalia, P. and Kumar, A.: EBK-means: A clustering technique based on elbow method and K-means in WSN, International Journal of Computer Applications, 105, 17–24, 2014. 
Burchard, H. and Bolding, K.: GETM – a general estuarine transport model, scientific documentation, Tech. Rep. EUR 20253 EN, European Commission (220), 2002. 
Celebi, M. E., Kingravi, H. A., and Vela, P. A.: A comparative study of efficient initialization methods for the K-means clustering algorithm, Expert Syst. Appl., 40, 200–210, https://doi.org/10.1016/j.eswa.2012.07.021, 2013. 
CMEMS: CMEMS-PQ-StrategicPlan, available at: https://marine.copernicus.eu/sites/default/files/wp-content/uploads/2017/03/CMEMS-PQ-StrategicPlan-v1.6-1.pdf (last acess: 18 February 2021), 2016. 
Download
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.