Dr. Caroline Arnold
• Bring machine learning to researchers from Earth & Environment
• ML to emulate numerical models
• Trainings for students and scientists
• Since 2025 AI Consultant at the Helmholtz Centre Hereon. Development of AI models and advising scientists on artificial intelligence in the field of earth and environment.
• 2020–2024 AI Consultant at the German Climate Computing Center. Development of AI models for earth and environmental sciences, user consulting, and training.
• 2019–2020 Postdoc at the Center for Free Electron Laser Science, Hamburg. Numerical modeling of ultrafast light-induced processes.
• 2015 – 2019 Doctorate (Physics), University of Hamburg and German Electron Synchrotron DESY. Topic: Ultrafast Nuclear Dynamics Induced by Light from the XUV to the THz Range.
• 2015 Diploma (Physics), Eberhard Karls University of Tübingen.
- Arnold, C., Sharma, S., Weigel, T., & Greenberg, D. S. (2024): Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5). Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024
- Nalepa, J., Tulczyjew, Ł., Le Saux, B., Longépé, N., Ruszczak, B., Wijata, A. M., Smykała, K., Myller, M., Kawulok, M., Kuzu, R. S., Albrecht, F., Arnold, C., Alasawedah, M., Angeli, S., Nobileau, D., Ballabeni, A., Lotti, A., Locarini, A., Modenini, D., Tortora, P., & Gumiela, M. (2024): Estimating Soil Parameters From Hyperspectral Images: A benchmark dataset and the outcome of the HYPERVIEW challenge. IEEE Geoscience and Remote Sensing Magazine, 12(3), 35 63. https://doi.org/10.1109/MGRS.2024.3394040
- Zhao, D., Heidler, K., Asgarimehr, M., Arnold, C., Xiao, T., Wickert, J., Zhu, X. X., & Mou, L., DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation, Remote Sensing of Environment (294), 113629, https://doi.org/10.1016/j.rse.2023.113629, 2023.
- Asgarimehr, M., Arnold, C., Weigel, T., Ruf, C., Wickert, J., GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet, Remote Sensing of Environment (269), 112801, https://doi.org/10.1016/j.rse.2021.112801, 2022
- Z.-H. Loh, G. Doumy, C. Arnold et al, Observation of the fastest chemical processes in the radiolysis of water. Science (367),179-182, https://doi.org/10.1126/science.aaz4740, 2020