Three papers on predictability of blocking predictability, frontal data assimilation, and machine learning prediction of sea-surface temperature were presented at EGU26, held in Vienna, Austria.
- Nomura, S. and Enomoto, T.: Dynamical linkage between blocking predictability and jet stream quasi-stationary states, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20078, https://doi.org/10.5194/egusphere-egu26-20078, 2026.
- Nakashita, S. and Enomoto, T.: Evaluation of data assimilation methods suitable for frontal structures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21740, https://doi.org/10.5194/egusphere-egu26-21740, 2026.
- Enomoto, T., Saito, A., and Nakashita, S.: Machine learning sea-surface temperature forecasting based on empirical orthogonal functions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18999, https://doi.org/10.5194/egusphere-egu26-18999, 2026.