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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
17 Aug 2023
Methods for assessment of models |
| 17 Aug 2023
| 17 Aug 2023
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)
Christoph Neuhauser
CORRESPONDING AUTHOR
School of Computation, Information and Technology, Technical University of Munich, Garching bei München, Germany
Maicon Hieronymus
Institute of Computer Science, Johannes Gutenberg University, Mainz, Germany
Michael Kern
Advanced Micro Devices GmbH, Dornach bei München, Germany
Marc Rautenhaus
Visual Data Analysis Group, Regional Computing Centre, University of Hamburg, Hamburg, Germany
Annika Oertel
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Rüdiger Westermann
School of Computation, Information and Technology, Technical University of Munich, Garching bei München, Germany
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Tim Radke, Johanna Baehr, and Marc Rautenhaus
EGUsphere, https://doi.org/10.5194/egusphere-2026-3877, https://doi.org/10.5194/egusphere-2026-3877, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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As primary objective, we investigate the patterns learned by artificial intelligence (AI) for the detection of atmospheric surface fronts (SFs) and whether they are physically plausible. Both plausible and implausible patterns are found. While the AI considers the rise in for example temperature, it also uses less explainable patterns further away from the detected SFs. These patterns then lead to the AI detecting much more SFs than expected leaving room for further improvements of the method.
Julia Thomas, Hendrik Reich, Thorsten Steinert, Gernot Geppert, Klaus Stephan, Anselm Erdmann, Philipp Gasch, Maxime Hervo, Jan Keller, Alberto Carrassi, Peter Knippertz, and Annika Oertel
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-352, https://doi.org/10.5194/essd-2026-352, 2026
Preprint under review for ESSD
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We present a convective-scale reanalysis of the 'Swabian MOSES 2023' campaign that assimilates 3-months of additional field campaign observations in southwest Germany. It is computed with a 1-hourly assimilation timestep on a 2.2 km grid centred over Germany in moderately complex terrain. In addition to all operationally available observations, it incorporates 6 additional observation types. Among these is an unprecedented number of vertical wind profiles retrieved from 12 Doppler wind lidars.
Ken S. Carslaw, Leighton A. Regayre, Ulrike Proske, Andrew Gettelman, David M. H. Sexton, Yun Qian, Lauren R. Marshall, Oliver Wild, Marcus van Lier-Walqui, Annika Oertel, Saloua Peatier, Ben Yang, Jill S. Johnson, Sihan Li, Daniel T. McCoy, Benjamin M. Sanderson, Christina J. Williamson, Gregory S. Elsaesser, Kuniko Yamazaki, and Ben B. B. Booth
Atmos. Chem. Phys., 26, 4651–4667, https://doi.org/10.5194/acp-26-4651-2026, https://doi.org/10.5194/acp-26-4651-2026, 2026
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A major challenge in climate science is reducing projection uncertainty despite advances in models and observational constraints. Perturbed parameter ensembles (PPEs) offer a powerful tool to explore and reduce uncertainty by revealing model weaknesses and guiding development. PPEs are now widely applied across climate systems and scales. We argue they should be prioritized alongside complexity and resolution in model resource planning.
Cornelis Schwenk, Annette Miltenberger, and Annika Oertel
Atmos. Chem. Phys., 25, 11333–11361, https://doi.org/10.5194/acp-25-11333-2025, https://doi.org/10.5194/acp-25-11333-2025, 2025
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We studied how different parameter choices concerning cloud processes affect the simulated transport of water and ice into the upper atmosphere (which affects the greenhouse effect) during a weather system called a warm conveyor belt. Using a set of model experiments, we found that some parameters have a strong effect on humidity and ice, especially during fast ascents. These findings could help improve weather and climate models and may also be relevant for future climate engineering studies.
Gabriella Wallentin, Annika Oertel, Luisa Ickes, Peggy Achtert, Matthias Tesche, and Corinna Hoose
Atmos. Chem. Phys., 25, 6607–6631, https://doi.org/10.5194/acp-25-6607-2025, https://doi.org/10.5194/acp-25-6607-2025, 2025
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Multilayer clouds are common in the Arctic but remain underrepresented. We use an atmospheric model to simulate multilayer cloud cases from the Arctic expedition MOSAiC 2019/2020. We find that it is complex to accurately model these cloud layers due to the lack of correct temperature profiles. The model also struggles to capture the observed cloud phase and the relative concentration of cloud droplets and cloud ice. We constrain our model to measured aerosols to mitigate this issue.
Alexander Lojko, Andrew C. Winters, Annika Oertel, Christiane Jablonowski, and Ashley E. Payne
Weather Clim. Dynam., 6, 387–411, https://doi.org/10.5194/wcd-6-387-2025, https://doi.org/10.5194/wcd-6-387-2025, 2025
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Convective storms can produce intense anticyclonically rotating vortices (~10 km) defined by negative potential vorticity (NPV), which can elongate to larger scales (~1000 km). Our composite analysis shows that elongated NPV frequently occurs along the western North Atlantic tropopause, where we observed it enhancing jet stream kinematics. Elongated NPV may impinge on aviation turbulence and weather forecasting despite its small-scale origin.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Svenja Christ, Marta Wenta, Christian M. Grams, and Annika Oertel
Weather Clim. Dynam., 6, 17–42, https://doi.org/10.5194/wcd-6-17-2025, https://doi.org/10.5194/wcd-6-17-2025, 2025
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The detailed representation of sea surface temperature (SST) in numerical models is important for the prediction of atmospheric blocking in the North Atlantic. Yet the underlying physical processes are not fully understood. Using SST sensitivity experiments for a case study, we identify a physical pathway through which SST in the Gulf Stream region is linked to the downstream upper-level flow evolution in the North Atlantic.
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
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Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
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We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Annika Oertel, Annette K. Miltenberger, Christian M. Grams, and Corinna Hoose
Atmos. Chem. Phys., 23, 8553–8581, https://doi.org/10.5194/acp-23-8553-2023, https://doi.org/10.5194/acp-23-8553-2023, 2023
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Warm conveyor belts (WCBs) are cloud- and precipitation-producing airstreams in extratropical cyclones that are important for the large-scale flow and cloud radiative forcing. We analyze cloud formation processes during WCB ascent in a two-moment microphysics scheme. Quantification of individual diabatic heating rates shows the importance of condensation, vapor deposition, rain evaporation, melting, and cloud-top radiative cooling for total heating and WCB-related potential vorticity structure.
Andreas Alexander Beckert, Lea Eisenstein, Annika Oertel, Timothy Hewson, George C. Craig, and Marc Rautenhaus
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2022-36, https://doi.org/10.5194/wcd-2022-36, 2022
Preprint withdrawn
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This study revises and extends a previously presented 3-D objective front detection method and demonstrates its benefits to analyse weather dynamics in numerical simulation data. Based on two case studies of extratropical cyclones, we demonstrate the evaluation of conceptual models from dynamic meteorology, illustrate the benefits of our interactive analysis approach by comparing fronts in data with different model resolutions, and study the impact of convection on fronts.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Roderick van der Linden, Michael Maier-Gerber, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 15, 4447–4468, https://doi.org/10.5194/gmd-15-4447-2022, https://doi.org/10.5194/gmd-15-4447-2022, 2022
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Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics. For example, anomalies in the PV field near the tropopause are linked to extreme weather events. In this study, an objective strategy to identify these anomalies is presented and evaluated. As a novel concept, it can be applied to three-dimensional (3-D) data sets. Supported by 3-D visualizations, we illustrate advantages of this new analysis over existing studies along a case study.
Julian F. Quinting, Christian M. Grams, Annika Oertel, and Moritz Pickl
Geosci. Model Dev., 15, 731–744, https://doi.org/10.5194/gmd-15-731-2022, https://doi.org/10.5194/gmd-15-731-2022, 2022
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This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.
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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.
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes,...
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