Articles | Volume 14, issue 11
Geosci. Model Dev., 14, 7133–7153, 2021
https://doi.org/10.5194/gmd-14-7133-2021
Geosci. Model Dev., 14, 7133–7153, 2021
https://doi.org/10.5194/gmd-14-7133-2021

Development and technical paper 24 Nov 2021

Development and technical paper | 24 Nov 2021

How biased are our models? – a case study of the alpine region

Denise Degen et al.

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Cited articles

Aretz-Nellesen, N., Grepl, M. A., and Veroy, K.: 3D-VAR for parameterized partial differential equations: a certified reduced basis approach, Adv. Comput. Math., 45, 2369–2400, 2019. a
Baroni, G. and Tarantola, S.: A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study, Environ. Modell. Softw., 51, 26–34, 2014. a
Baş, D. and Boyacı, I. H.: Modeling and optimization I: Usability of response surface methodology, J. Food Eng., 78, 836–845, 2007. a
Benner, P., Gugercin, S., and Willcox, K.: A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems, SIAM Rev., 57, 483–531, 2015. a, b, c
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., and Escaleira, L. A.: Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta, 76, 965–977, 2008. a
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In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.