Publikationen


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Davy, R., & Griewank, P. (2023). Arctic amplification has already peaked. Environmental Research Letters, 18(8), Artikel 084003. https://doi.org/10.1088/1748-9326/ace273

Griewank, P. J., Weissmann, M., Necker, T., Nomokonova, T., & Löhnert, U. (2023). Ensemble‐based estimates of the impact of potential observations. Quarterly Journal of the Royal Meteorological Society, 149(754), 1546-1571. https://doi.org/10.1002/qj.4464

Borne, M., Knippertz, P., Weissmann, M., Martin, A., Rennie, M., & Cress, A. (2023). Impact of Aeolus wind lidar observations on the representation of the West African monsoon circulation in the ECMWF and DWD forecasting systems. Quarterly Journal of the Royal Meteorological Society, 149(752), 933-958. https://doi.org/10.1002/qj.4442

Diefenbach, T., Craig, G., Keil, C., Scheck, L., & Weissmann, M. (2023). Partial Analysis Increments as Diagnostic for LETKF Data Assimilation Systems. Quarterly Journal of the Royal Meteorological Society, 149(752), 740-756. https://doi.org/10.1002/qj.4419

Hu, G., Dance, S. L., Bannister, R. N., Chipilski, H. G., Guillet, O., Macpherson, B., Weissmann, M., & Yussouf, N. (2023). Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021. Atmospheric Science Letters, 24(1), Artikel e1130. https://doi.org/10.1002/asl.1130

Nomokonova, T., Griewank, P., Loehnert, U., Miyoshi, T., Necker, T., & Weissmann, M. (2023). Estimating the benefit of Doppler wind lidars for short-term low-level wind ensemble forecasts. Quarterly Journal of the Royal Meteorological Society, 149(750), 192-210. https://doi.org/10.1002/qj.4402

Farokhmanesh, F., Höhlein, K., Necker, T., & Weissmann, M. (2023). Deep Learning–Based Parameter Transfer in Meteorological Data. Artificial Intelligence for the Earth Systems, 2(1), Artikel e220024. https://doi.org/10.1175/AIES-D-22-0024.1

Farokhmanesh, F., Höhlein, K., Neuhauser, C., Necker, T., Weissmann, M., Miyoshi, T., & Westermann, R. (2023). Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles. in VMV 2023: Vision, Modeling, and Visualization https://doi.org/10.2312/vmv.20231229

Krüger, K., Schäfler, A., Wirth, M., Weissmann, M., & Craig, G. C. (2022). Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes. Atmospheric Chemistry and Physics, 22(23), 15559-15577. https://doi.org/10.5194/acp-22-15559-2022

Manzato, A., Serafin, S., Miglietta, M. M., Kirshbaum, D. J., & Schulz, W. (2022). A pan-Alpine climatology of lightning and convective initiation. Monthly Weather Review, 150(9), 2213-2230. https://doi.org/10.1175/MWR-D-21-0149.1

Craig, G. C., Puh, M., Keil, C., Tempest, K., Necker, T., Ruiz , J., Weissmann, M., & Miyoshi, T. (2022). Distributions and convergence of forecast variables in a 1000 member convection-permitting ensemble. Quarterly Journal of the Royal Meteorological Society, 148(746), 2325-2343. https://doi.org/10.1002/qj.4305

Rotach, M. W., Serafin, S., Ward, H. C., Arpagaus, M., Colfescu, I., Cuxart, J., De Wekker, S. F. J., Grubisic, V., Karl, T., Kirshbaum, D. J., Lehner, M., Mobbs, S. D., Paci, A., Palazzi, E., Bailey, A., Schmidli, J., Wittmann, C., Wohlfahrt, G., & Zardi, D. (2022). A collaborative effort to better understand, measure and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society, 103(5), E1282-E1295. https://doi.org/10.1175/BAMS-D-21-0232.1

Pepin, N. C., Arnone, E., Gobiet, A., Haslinger, K., Kotlarski, S., Notarnicola, C., Palazzi, E., Seibert, P., Serafin, S., Schöner, W., Terzago, S., Thornton, J. M., Vuille, M., & Adler, C. (2022). Climate changes and their elevational patterns in the mountains of the world. Reviews of Geophysics, 60(1), Artikel e2020RG000730. https://doi.org/10.1029/2020RG000730

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