Publikationen

Publikationen

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

Neggers, R., & Griewank, P. J. (2022). A decentralized approach for modeling organized convection based on thermal populations on microgrids. Journal of Advances in Modeling Earth Systems, 14(10), Artikel e2022MS003042. https://doi.org/10.1002/essoar.10510525.1, https://doi.org/10.1029/2022MS003042

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

Strauss, L., Serafin, S., & Dorninger, M. (2022). Probability forecasts of ice accretion on wind turbines derived from multiphysics and neighbourhood ensembles. Quarterly Journal of the Royal Meteorological Society, 148(746), 2446-2467. https://doi.org/10.1002/qj.4311

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

Griewank, P. J., Heus, T., & Neggers, R. A. J. (2022). Size‐Dependent Characteristics of Surface‐Rooted Three‐Dimensional Convective Objects in Continental Shallow Cumulus Simulations. Journal of Advances in Modeling Earth Systems, 14(3), Artikel e2021MS002612. https://doi.org/10.1029/2021MS002612

Göbel, M., Serafin, S., & Rotach, M. W. (2022). Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model. Geoscientific Model Development, 15(2), 669-681. https://doi.org/10.5194/gmd-15-669-2022

Höhlein, K., Weiss, S., Necker, T., Weissmann, M., Miyoshi, T., & Westermann, R. (2022). Evaluation of Volume Representation Networks for Meteorological Ensemble Compression. in VMV 2022: Vision, Modeling, and Visualization https://doi.org/10.2312/vmv.20221198

Geiss, S., Scheck, L., de Lozar, A., & Weissmann, M. (2021). Understanding the model representation of clouds based on visible and infrared satellite observations. Atmospheric Chemistry and Physics, 21(16), 12273-12290. https://doi.org/10.5194/acp-21-12273-2021

Martin, A., Weissmann, M., Reitebuch, O., Rennie, M., Geiss, A., & Cress, A. (2021). Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents. Atmospheric Measurement Techniques, 14(3), 2167-2183. https://doi.org/10.5194/amt-14-2167-2021

Neggers, R. A. J., & Griewank, P. J. (2021). A Binomial Stochastic Framework for Efficiently Modeling Discrete Statistics of Convective Populations. Journal of Advances in Modeling Earth Systems, 13(3), Artikel e2020MS002229. https://doi.org/10.1029/2020MS002229

Strauss, L., Serafin, S., & Dorninger, M. (2020). Skill and Potential Economic Value of Forecasts of Ice Accretion on Wind Turbines. Journal of Applied Meteorology and Climatology, 59(11), 1845–1864. https://doi.org/10.1175/JAMC-D-20-0025.1

Schröttle, J., Weissmann, M., Scheck, L., & Hutt, A. (2020). Assimilating visible and infrared radiances in idealized simulations of deep convection. Monthly Weather Review, 148(11), 4357-4375. https://doi.org/10.1175/MWR-D-20-0002.1

Scheck, L., Weissmann, M., & Bach, L. (2020). Assimilating visible satellite images for convective‐scale numerical weather prediction: A case‐study. Quarterly Journal of the Royal Meteorological Society, 146(732), 3165-3186. https://doi.org/10.1002/qj.3840