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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Ken S. Carslaw, Leighton A. Regayre, Ulrike Proske, Andrew Gettelman, David M. H. Sexton, Yun Qian, Lauren 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-4341, https://doi.org/10.5194/egusphere-2025-4341, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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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.
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.
Cornelis Schwenk, Annette Miltenberger, and Annika Oertel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1816, https://doi.org/10.5194/egusphere-2025-1816, 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.
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.
Marcel Meyer, Iuliia Polkova, Kameswar Rao Modali, Laura Schaffer, Johanna Baehr, Stephan Olbrich, and Marc Rautenhaus
Weather Clim. Dynam., 2, 867–891, https://doi.org/10.5194/wcd-2-867-2021, https://doi.org/10.5194/wcd-2-867-2021, 2021
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Novel techniques from computer science are used to study extreme weather events. Inspired by the interactive 3-D visual analysis of the recently released ERA5 reanalysis data, we improve commonly used metrics for measuring polar winter storms and outbreaks of cold air. The software (Met.3D) that we have extended and applied as part of this study is freely available and can be used generically for 3-D visualization of a broad variety of atmospheric processes in weather and climate data.
Annika Oertel, Michael Sprenger, Hanna Joos, Maxi Boettcher, Heike Konow, Martin Hagen, and Heini Wernli
Weather Clim. Dynam., 2, 89–110, https://doi.org/10.5194/wcd-2-89-2021, https://doi.org/10.5194/wcd-2-89-2021, 2021
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Convection embedded in the stratiform cloud band of strongly ascending airstreams in extratropical cyclones (so-called warm conveyor belts) can influence not only surface precipitation but also the
upper-tropospheric potential vorticity (PV) and waveguide. The comparison of intense vs. moderate embedded convection shows that its strength alone is not a reliable measure for upper-tropospheric PV modification. Instead, characteristics of the ambient flow co-determine its dynamical significance.
Cited articles
Afzal, S., Hittawe, M., Ghani, S., Jamil, T., Knio, O., Hadwiger, M., and
Hoteit, I.: The State of the Art in Visual Analysis Approaches for Ocean and
Atmospheric Datasets, Comput. Graph. Forum, 38, 881–907,
https://doi.org/10.1111/cgf.13731, 2019. a
Bader, R., Sprenger, M., Ban, N., Radisuhli, S., Schar, C., and Günther, T.:
Extraction and Visual Analysis of Potential Vorticity Banners around
the Alps, IEEE T. Vis. Comput. Gr., 26,
259–269, https://doi.org/10.1109/TVCG.2019.2934310, 2019. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and
Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with
the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139,
3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barklie, R. H. D. and Gokhale, N. R.: The freezing of supercooled water drops, Stormy Weather Group, McGill Univ., Sci. Rep. MW-30, Part 3, 43–64, 1959. a
Baumgartner, M., Sagebaum, M., Gauger, N. R., Spichtinger, P., and Brinkmann, A.: Algorithmic differentiation for cloud schemes (IFS Cy43r3) using CoDiPack (v1.8.1), Geosci. Model Dev., 12, 5197–5212, https://doi.org/10.5194/gmd-12-5197-2019, 2019. a
Beckert, A. A., Eisenstein, L., Oertel, A., Hewson, T., Craig, G. C., and Rautenhaus, M.: The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models, Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, 2023. a
Bösiger, L., Sprenger, M., Boettcher, M., Joos, H., and Günther, T.: Integration-based extraction and visualization of jet stream cores, Geosci. Model Dev., 15, 1079–1096, https://doi.org/10.5194/gmd-15-1079-2022, 2022. a
Chen, L., Özsu, M. T., and Oria, V.: Robust and Fast Similarity Search for
Moving Object Trajectories, in: Proceedings of the 2005 ACM SIGMOD
International Conference on Management of Data, SIGMOD '05, 491–502,
Association for Computing Machinery, New York, NY, USA, June 2005,
https://doi.org/10.1145/1066157.1066213, 2005. a
Gong, X., Si, Y.-W., Fong, S., and Mohammed, S.: NSPRING:
Normalization-supported SPRING for subsequence matching on time series
streams, in: 2014 IEEE 15th International Symposium on Computational
Intelligence and Informatics (CINTI), Budapest, Hungary, November 2014, 373–378,
https://doi.org/10.1109/CINTI.2014.7028704, 2014. a
Griewank, A. and Walther, A.: Evaluating Derivatives: Principles and Techniques
of Algorithmic Differentiation, second edn., SIAM, https://doi.org/10.1137/1.9780898717761, 2008. a
Harrower, M. and Brewer, C. A.: ColorBrewer.org: An Online Tool for Selecting
Colour Schemes for Maps, Cartogr. J., 40, 27–37,
https://doi.org/10.1179/000870403235002042, 2003. a
He, J., Chen, H., Chen, Y., Tang, X., and Zou, Y.: Diverse Visualization
Techniques and Methods of Moving-Object-Trajectory Data: A Review, ISPRS
Int. J. Geo-Inf., 8, 63, https://doi.org/10.3390/ijgi8020063, 2019. a
Hieronymus, M.: wavestoweather/AD_Sensitivity_Analysis: Source for Reference
Paper, Zenodo [code], https://doi.org/10.5281/zenodo.6645540, 2022. a
Hieronymus, M. and Oertel, A.: Trajectory Data with Sensitivities to Cloud
Microphysical Parameters, Zenodo [data set], https://doi.org/10.5281/zenodo.8043592, 2023. a
Jeyaratnam, J., Booth, J. F., Naud, C. M., Luo, Z. J., and Homeyer, C. R.:
Upright Convection in Extratropical Cyclones: A Survey Using Ground‐Based
Radar Data Over the United States, Geophys. Res. Lett., 47, e2019GL086620,
https://doi.org/10.1029/2019GL086620, 2020. a, b
Joos, H. and Forbes, R. M.: Impact of different IFS microphysics on a warm
conveyor belt and the downstream flow evolution, Q. J. Roy. Meteor. Soc.,
142, 2727–2739, https://doi.org/10.1002/qj.2863, 2016. a
Kappe, C., Böttinger, M., and Leitte, H.: Topology-Based Feature Analysis
of Scalar Field Ensembles: An Application to Climate (Change) Analysis,
Comput. Graph., 104, 59–71, https://doi.org/10.1016/j.cag.2022.03.004, 2022. a
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based parameterization
of cirrus cloud formation for use in global atmospheric models, J. Geophys.
Res.-Atmos., 111, D01205, https://doi.org/10.1029/2005JD006219, 2006. a
Kern, M., Hewson, T., Sadlo, F., Westermann, R., and Rautenhaus, M.: Robust
Detection and Visualization of Jet-stream Core Lines in Atmospheric Flow,
IEEE T. Vis. Comput. Gr., 24, 893–902,
https://doi.org/10.1109/tvcg.2017.2743989, 2018. a
Kern, M., Hewson, T., Schatler, A., Westermann, R., and Rautenhaus, M.:
Interactive 3D Visual Analysis of Atmospheric Fronts, IEEE
T. Vis. Comput. Gr., 25, 1080–1090,
https://doi.org/10.1109/TVCG.2018.2864806, 2019. a
Leutbecher, M. and Palmer, T.: Ensemble Forecasting, J. Comput.
Phys., 227, 3515–3539, https://doi.org/10.1016/j.jcp.2007.02.014, 2008. a
Love, A. L., Pang, A., and Kao, D. L.: Visualizing spatial multivalue data,
IEEE Computer Graphics and Applications, 25, 69–79, 2005. a
Madonna, E., Wernli, H., Joos, H., and Martius, O.: Warm Conveyor Belts in the
ERA-Interim Dataset (1979–2010). Part I: Climatology and Potential
Vorticity Evolution, J. Climate, 27, 3–26, https://doi.org/10.1175/JCLI-D-12-00720.1,
2014. a, b
Mazoyer, M., Ricard, D., Rivière, G., Delanoë, J., Arbogast, P., Vié,
B., Lac, C., Cazenave, Q., and Pelon, P.: Microphysics Impacts on the Warm
Conveyor Belt and Ridge Building of the NAWDEX IOP6 Cyclone, Mon. Weather
Rev., 149, 3961–3980, https://doi.org/10.1175/MWR-D-21-0061.1, 2021. a
Meyer, M., Polkova, I., Modali, K. R., Schaffer, L., Baehr, J., Olbrich, S., and Rautenhaus, M.: Interactive 3-D visual analysis of ERA5 data: improving diagnostic indices for marine cold air outbreaks and polar lows, Weather Clim. Dynam., 2, 867–891, https://doi.org/10.5194/wcd-2-867-2021, 2021. a
Miltenberger, A. K., Pfahl, S., and Wernli, H.: An online trajectory module (version 1.0) for the nonhydrostatic numerical weather prediction model COSMO, Geosci. Model Dev., 6, 1989–2004, https://doi.org/10.5194/gmd-6-1989-2013, 2013. a
Munzner, T.: Visualization Analysis and Design, chap. 5, 1 edn., A K Peters/CRC Press,
https://doi.org/10.1201/b17511, 2014. a
Neuhauser, C., Wang, J., Kern, M., and Westermann, R.: Efficient High-Quality
Rendering of Ribbons and Twisted Lines, in: Vision, Modeling, and
Visualization, The Eurographics Association, 135–1439,
https://doi.org/10.2312/vmv.20221213, 2022. a, b
Neuhauser, C., Hieronymus, M., Kern, M., and Met.3D Contributors:
chrismile/met.3d: 1.6.0-multivar1, Zenodo [code], https://doi.org/10.5281/zenodo.8082371, 2023. a
Nguyen, D. B., Zhang, L., Laramee, R. S., Thompson, D., Monico, R. O., and
Chen, G.: Unsteady Flow Visualization via Physics Based Pathline Exploration,
in: 2019 IEEE Visualization Conference (VIS), Vancouver, BC, Canada, October 2019, 286–290,
https://doi.org/10.1109/VISUAL.2019.8933578, 2019. a
Nguyen, D. B., Zhang, L., Laramee, R. S., Thompson, D., Monico, R. O., and
Chen, G.: Physics-based Pathline Clustering and Exploration, Comput.
Graph. Forum, 40, 22–37, https://doi.org/10.1111/cgf.14093, 2021. a
Oertel, A., Boettcher, M., Joos, H., Sprenger, M., Konow, H., Hagen, M., and
Wernli, H.: Convective activity in an extratropical cyclone and its warm
conveyor belt – a case‐study combining observations and a
convection‐permitting model simulation, Q. J. Roy. Meteor. Soc., 145,
1406–1426, https://doi.org/10.1002/qj.3500, 2019. a, b
Oertel, A., Boettcher, M., Joos, H., Sprenger, M., and Wernli, H.: Potential vorticity structure of embedded convection in a warm conveyor belt and its relevance for large-scale dynamics, Weather Clim. Dynam., 1, 127–153, https://doi.org/10.5194/wcd-1-127-2020, 2020. a, b, c, d
Oertel, A., Sprenger, M., Joos, H., Boettcher, M., Konow, H., Hagen, M., and Wernli, H.: Observations and simulation of intense convection embedded in a warm conveyor belt – how ambient vertical wind shear determines the dynamical impact, Weather Clim. Dynam., 2, 89–110, https://doi.org/10.5194/wcd-2-89-2021, 2021. a, b
Ollinaho, P., Lock, S.-J., Leutbecher, M., Bechtold, P., Beljaars, A., Bozzo,
A., Forbes, R. M., Haiden, T., Hogan, R. J., and Sandu, I.: Towards
process-level representation of model uncertainties: stochastically perturbed
parametrizations in the ECMWF ensemble, Q. J. Roy. Meteor. Soc., 143,
408–422, https://doi.org/10.1002/qj.2931, 2017. a
Orf, L., Wilhelmson, R., Lee, B., Finley, C., and Houston, A.: Evolution of a
Long-Track Violent Tornado within a Simulated Supercell, B. Am.
Meteorol. Soc., 98, 45–68, https://doi.org/10.1175/bams-d-15-00073.1, 2016. a
Pfahl, S., Madonna, E., Boettcher, M., Joos, H., and Wernli, H.: Warm Conveyor
Belts in the ERA-Interim Dataset (1979–2010). Part II: Moisture Origin
and Relevance for Precipitation, J. Climate, 27, 27–40, https://doi.org/10.1175/JCLI-D-13-00223.1,
2014. a
Phillips, V. T. J., DeMott, P. J., and Andronache, C.: An Empirical
Parameterization of Heterogeneous Ice Nucleation for Multiple Chemical
Species of Aerosol, J. Atmos. Sci, 65, 2757–2783,
https://doi.org/10.1175/2007JAS2546.1, 2008. a
Pickl, M., Lang, S. T. K., Leutbecher, M., and Grams, C. M.: The effect of
stochastically perturbed parametrisation tendencies (SPPT) on rapidly
ascending air streams, Q. J. Roy. Meteor. Soc., 148, 1242–1261,
https://doi.org/10.1002/qj.4257, 2022. a
Potter, K., Kniss, J., Riesenfeld, R., and Johnson, C. R.: Visualizing Summary
Statistics and Uncertainty, Comput. Graph. Forum, 29, 823–832,
https://doi.org/10.1111/j.1467-8659.2009.01677.x, 2010. a
Rao, R. and Card, S. K.: The Table Lens: Merging Graphical and Symbolic
Representations in an Interactive Focus + Context Visualization for Tabular
Information, in: Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, CHI '94, Association for Computing
Machinery, New York, NY, USA, Boston, Massachusetts, USA, April 1994, 318–322, https://doi.org/10.1145/191666.191776, 1994. a
Rautenhaus, M., Grams, C. M., Schäfler, A., and Westermann, R.: Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns, Geosci. Model Dev., 8, 2355–2377, https://doi.org/10.5194/gmd-8-2355-2015, 2015a. a, b, c
Rautenhaus, M., Kern, M., Schäfler, A., and Westermann, R.: Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0), Geosci. Model Dev., 8, 2329–2353, https://doi.org/10.5194/gmd-8-2329-2015, 2015b. a
Rautenhaus, M., Bottinger, M., Siemen, S., Hoffman, R., Kirby, R. M.,
Mirzargar, M., Röber, N., and Westermann, R.: Visualization in
Meteorology – A Survey of Techniques and Tools for Data
Analysis Tasks, IEEE Trans. Visual. Comput. Graphics, 24, 3268–3296,
https://doi.org/10.1109/tvcg.2017.2779501, 2018. a, b
Russig, B., Graß, D., Dachselt, R., and Gumhold, S.: On-Tube Attribute
Visualization for Multivariate Trajectory Data, IEEE T.
Vis. Comput. Gr., 29, 1288–1298,
https://doi.org/10.1109/TVCG.2022.3209400, 2023. a, b
Sadlo, F., Peikert, R., and Sick, M.: Visualization Tools for Vorticity
Transport Analysis in Incompressible Flow, IEEE T. Vis. Comput. Gr., 12, 949–956, https://doi.org/10.1109/TVCG.2006.199, 2006. a
Sagebaum, M., Albring, T., and Gauger, N. R.: High-Performance Derivative
Computations using CoDiPack, ACM Trans. Math. Softw., 45, 38,
https://doi.org/10.1145/3356900, 2019. a
Sakurai, Y., Faloutsos, C., and Yamamuro, M.: Stream Monitoring under the Time
Warping Distance, in: 2007 IEEE 23rd International Conference on Data
Engineering, Istanbul, Turkey, April 2007, 1046–1055, https://doi.org/10.1109/ICDE.2007.368963, 2007. a
Sanyal, J., Zhang, S., Dyer, J., Mercer, A., Amburn, P., and Moorhead, R.:
Noodles: A tool for visualization of numerical weather model ensemble
uncertainty, IEEE T. Vis. Comput. Gr., 16,
1421–1430, 2010. a
Schäfler, A., Craig, G., Wernli, H., Arbogast, P., Doyle, J. D.,
McTaggart-Cowan, R., Methven, J., Rivière, G., Ament, F., Boettcher, M.,
Bramberger, M., Cazenave, Q., Cotton, R., Crewell, S., Delanoë, J.,
Dörnbrack, A., Ehrlich, A., Ewald, F., Fix, A., Grams, C. M., Gray, S. L.,
Grob, H., Groß, S., Hagen, M., Harvey, B., Hirsch, L., Jacob, M., Kölling,
T., Konow, H., Lemmerz, C., Lux, O., Magnusson, L., Mayer, B., Mech, M.,
Moore, R., Pelon, J., Quinting, J., Rahm, S., Rapp, M., Rautenhaus, M.,
Reitebuch, O., Reynolds, C. A., Sodemann, H., Spengler, T., Vaughan, G.,
Wendisch, M., Wirth, M., Witschas, B., Wolf, K., and Zinner, T.: The North
Atlantic Waveguide and Downstream Impact Experiment, B. Am. Meteorol.
Soc., 99, 1607–1637, https://doi.org/10.1175/BAMS-D-17-0003.1, 2018.
a, b
Seifert, A.: On the Parameterization of Evaporation of Raindrops as Simulated
by a One-Dimensional Rainshaft Model, J. Atmos. Sci, 65, 3608–3619,
https://doi.org/10.1175/2008JAS2586.1, 2008. a, b, c
Steinarsson, S.: Downsampling Time Series for Visual Representation, Master's
thesis, University of Iceland, https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf (last access: 9 August 2023), 2013. a
Stevens, S. S.: Psychophysics: Introduction to Its Perceptual, Neural, and
Social Prospects, p. 103, 104, 115, Wiley, ISBN 9780471824374, 1975. a
Stoll, C., Gumhold, S., and Seidel, H.-P.: Visualization with stylized
line primitives, in: VIS 05. IEEE Visualization, Minneapolis, MN, USA, October 2005, 2005, 695–702,
https://doi.org/10.1109/VISUAL.2005.1532859, 2005. a
Toyoda, M. and Sakurai, Y.: Subsequence Matching in Data Streams, NTT Technical
Review, 11, 208–208, 2013. a
Wang, J., Hazarika, S., Li, C., and Shen, H.: Visualization and Visual Analysis
of Ensemble Data: A Survey, IEEE T. Vis. Comput.
Gr., 25, 2853–2872, https://doi.org/10.1109/TVCG.2018.2853721, 2018. a
Wernli, H.: A lagrangian-based analysis of extratropical cyclones. II: A
detailed case-study, Q. J. Roy. Meteor. Soc., 123, 1677–1706,
https://doi.org/10.1002/qj.49712354211, 1997. a, b
Yoshizumi, A., Coffer, M. M., Collins, E. L., Gaines, M. D., Gao, X., Jones,
K., McGregor, I. R., McQuillan, K. A., Perin, V., Tomkins, L. M., Worm, T.,
and Tateosian, L.: A Review of Geospatial Content in IEEE Visualization
Publications, in: 2020 IEEE Visualization Conference (VIS), 51–55, Salt Lake City, UT, USA, October 2020,
https://doi.org/10.1109/VIS47514.2020.00017, 2020. a
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,...
