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  • Brecht, R., Bakels, L., Bihlo, A. & Stohl, A. (2023). Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution. Geosci. Model Dev. 16(8), 2181–2192, doi: https://doi.org/10.5194/gmd-16-2181-2023. 

  • Darbenas, Z., van der Hout, R. & Oliver, M. (2023). Conditional uniqueness of solutions to the Keller–Rubinow model for Liesegang rings in the fast reaction limit. J. Differential Equations 347, 212–245, doi: https://doi.org/10.1016/j.jde.2022.11.038. 

  • Kutsenko, A.A. (2023). Approximation of the Number of Descendants in Branching Processes. J. Stat. Phys. 190(68), doi: https://doi.org/10.1007/s10955-023-03079-6.

  • Kutsenko, A.A. (2023). A note on exotic integrals. Proc. Amer. Math. Soc. 151, 1697-1703, doi: https://doi.org/10.1090/proc/16279.

  • Brecht, R. & Bihlo, A. (2023). Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks. Geophys. Res. Lett. 50(2), e2022GL101452, doi: https://doi.org/10.1029/2022GL101452. 

  • Juricke, S., Bellinghausen, K., Danilov, S., Kutsenko, A. & Oliver, M. (2023). Scale analysis on unstructured grids: Kinetic energy and dissipation power spectra on triangular meshes. J. Adv. Model Earth Sy. 15, e2022MS003280, doi: https://doi.org/10.1029/2022MS003280.

  • Feng, Y., Mazzucato, A.L. & Nobili, C. (2023). Enhanced dissipation by circularly symmetric and parallel pipe flows. Physica D: Nonlinear Phenomena 445, 133640, doi: https://doi.org/10.1016/j.physd.2022.133640. 

  • Strommen, K., Juricke, S. & Cooper, F. (2022). Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation. Weather Clim. Dynam. 3(3), 951–975, doi: https://doi.org/10.5194/wcd-3-951-2022.

  • Uchida, T., Danilov, S., Koldunov, N. et al. (2022). Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models. Geosci. Model Dev. 15, 5829–5856, doi: https://doi.org/10.5194/gmd-15-5829-2022. 

  • Franzke, C.L.E., Gugole, F. & Juricke, S. (2022). Systematic multi-scale decomposition of ocean variability using machine learning. Chaos: An Interdisciplinary Journal of Nonlinear Science 32(7), 073122, doi: https://doi.org/10.1063/5.0090064.