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publications [2026/08/07 10:35] – [2026] potthastpublications [2026/09/08 22:54] (current) – [2026] potthast
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-  * Niklas Becker, Valentin Bruch, Thomas Rösch, Niels Heinrich Keil, Martin Jakob Steil, Diego Jimenez de la Cuesta Otero, Beatrice Ellerhoff, Roland Potthast, and Andrea K. Kaiser-WeissConcurrent assimilation of methane fluxes and concentrations with a 4D LETKF for Germany in 2021 based on ICON–ART [[https://egusphere.copernicus.org/preprints/2026/egusphere-2026-3221/|egusphere]]+  * 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]] 
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 +  * 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. 
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 +  * 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]]
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