| Both sides previous revisionPrevious revisionNext revision | Previous revision |
| publications [2026/08/07 10:36] – [2026] potthast | publications [2026/09/08 22:54] (current) – [2026] potthast |
|---|
| |
| ==== 2026 ==== | ==== 2026 ==== |
| | |
| | * Tobias Goecke, Marek Jacob, Florian Prill, Michael Denhard, Felix Fundel, Jan Keller, Roland Potthast, Hendrik Reich, Britta Seegebrecht, Sven Ulbrich, Arianna Valmassoi, Sabrina Wahl: AICON: An operational global machine learning weather forecasting model, Preprint [[https://arxiv.org/abs/2608.24651]] |
| | |
| | * Rösch, T., Bruch, V., Becker, N. R., Schlemmer, L., Potthast, R., and Kaiser-Weiss, A. (2026): The DWD inversion system based on ICON-ART. promet – Meteorological Training, Deutscher Wetterdienst. |
| |
| * Becker, N., Bruch, V., Rösch, T., Keil, N. H., Steil, M. J., Jimenez de la Cuesta Otero, D., Ellerhoff, B., Potthast, R., & Kaiser-Weiss, A. K. (2026). Concurrent assimilation of methane fluxes and concentrations with a 4D LETKF for Germany in 2021 based on ICON–ART. Preprint [[https://egusphere.copernicus.org/preprints/2026/egusphere-2026-3221/|egusphere]] | * Becker, N., Bruch, V., Rösch, T., Keil, N. H., Steil, M. J., Jimenez de la Cuesta Otero, D., Ellerhoff, B., Potthast, R., & Kaiser-Weiss, A. K. (2026). Concurrent assimilation of methane fluxes and concentrations with a 4D LETKF for Germany in 2021 based on ICON–ART. Preprint [[https://egusphere.copernicus.org/preprints/2026/egusphere-2026-3221/|egusphere]] |
| * Schühl, L., Schomburg, A., Deppisch, T., Scheck, L., Schraff, C., Köpken‑Watts, C., Faulwetter, R., Potthast, R., & Weissmann, M. (2026). Operational assimilation of visible satellite observations in the regional ICON‑D2 model at DWD [Preprint]. | * Schühl, L., Schomburg, A., Deppisch, T., Scheck, L., Schraff, C., Köpken‑Watts, C., Faulwetter, R., Potthast, R., & Weissmann, M. (2026). Operational assimilation of visible satellite observations in the regional ICON‑D2 model at DWD [Preprint]. |
| |
| * McTaggart-Cowan, R., Magnusson, L., Polichtchouk, I., Ackerley, D., Köhler, M., Casati, B., Chen, J.-H., Hudson, D., Ujiie, M., Amir Aziz, N., Bonavita, M., Ben Bouallègue, Z., de Burgh-Day, C., Chamberland, S., Cho, K., Coelho, C. A. S., Fadeev, R., Fuentes, M., Garcia-Franco, J. L., Gilbert, C., ... Potthast, R., ... Zhu, H. (2026). WP-MIP: An artificial intelligence, hybrid and physically based model intercomparison project for weather prediction. Preprint [[https://arxiv.org/pdf/2604.16643]] | * McTaggart-Cowan, R., Magnusson, L., Polichtchouk, I., Ackerley, D., Köhler, M., Casati, B., Chen, J.-H., Hudson, D., Ujiie, M., Amir Aziz, N., Bonavita, M., Ben Bouallègue, Z., de Burgh-Day, C., Chamberland, S., Cho, K., Coelho, C. A. S., Fadeev, R., Fuentes, M., Garcia-Franco, J. L., Gilbert, C., ... Potthast, R., ... Zhu, H. (2026). WP-MIP: An artificial intelligence, hybrid and physically based model intercomparison project for weather prediction. Accepted by BAMS 2026 [[https://arxiv.org/pdf/2604.16643]] |
| |
| * Gian Luca Buono, Stefanie Hollborn, Roland Potthast, Jörg Schäfer, Martin Simon, Learning Data-driven Surrogate and Correction Models for Satellite Observations in Numerical Weather Prediction, submitted [[https://arxiv.org/abs/2603.22037]] | * Gian Luca Buono, Stefanie Hollborn, Roland Potthast, Jörg Schäfer, Martin Simon, Learning Data-driven Surrogate and Correction Models for Satellite Observations in Numerical Weather Prediction, submitted [[https://arxiv.org/abs/2603.22037]] |
