The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Large‐eddy simulations over Germany using ICON: a comprehensive evaluation
Open accessAbstract
Large‐eddy simulations (LES) with the new ICOsahedral Non‐hydrostatic atmosphere model (ICON) covering Germany are evaluated for four days in spring 2013 using observational data from various sources. Reference simulations with the established Consortium for Small‐scale Modelling (COSMO) numerical weather prediction model and further standard LES codes are performed and used as a reference. This comprehensive evaluation approach covers multiple parameters and scales, focusing on boundary‐layer variables, clouds and precipitation. The evaluation points to the need to work on parametrizations influencing the surface energy balance, and possibly on ice cloud microphysics. The central purpose for the development and application of ICON in the LES configuration is the use of simulation results to improve the understanding of moist processes, as well as their parametrization in climate models. The evaluation thus aims at building confidence in the model's ability to simulate small‐ to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high‐resolution model matches the observed variability much better at small‐ to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time‐scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high‐resolution model.
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Deep learning to represent subgrid processes in climate models
Rasp, S., Pritchard, M. S., & Gentine, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810286115 · Cited by 1,044
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Self-citationOmanovic, N., Goger, B., & Lohmann, U. (2024) · Atmospheric Chemistry and Physics · doi:10.5194/acp-24-14145-2024 · Cited by 4
Metadata
- Authors
- Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D., & Quaas, J.
- Venue
- Quarterly Journal of the Royal Meteorological Society
- Publisher
- Wiley
- Published
- 2017-01
- Volume
- 143
- Issue
- 702
- Pages
- 69-100
- ISSN
- 0035-9009 (ISSN-L)
- 1477-870X
- OpenAlex
- W2546188170
- Cited by
- 293
- References
- 78
- FWCI
- 16.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., . . . Quaas, J. (2017). Large‐eddy simulations over Germany using ICON: a comprehensive evaluation. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.2947
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