Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 16
Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4617-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4617-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 16
Methods for assessment of models
 | 
17 Aug 2023
Methods for assessment of models |  | 17 Aug 2023

Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)

Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann

Data sets

Trajectory data with sensitivities to cloud microphysical parameters Maicon Hieronymus and Annika Oertel https://doi.org/10.5281/zenodo.8043592

Model code and software

Met.3D (1.6.0-multivar1) Christoph Neuhauser, Maicon Hieronymus, Michael Kern, & Met.3D Contributors https://doi.org/10.5281/zenodo.8082371

wavestoweather/AD_Sensitivity_Analysis: Source for Reference Paper Maicon Hieronymus https://doi.org/10.5281/zenodo.6645540

Video supplement

Supplementary videos for the paper "Visual analysis of model parameter sensitivities along warm conveyor belt trajectories" Christoph Neuhauser and Maicon Hieronymus https://doi.org/10.5281/zenodo.8085134

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Short summary
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
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