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News and Relevant Information
Submissions are welcomed for the Special Collection "Combined machine learning and data assimilation for the atmosphere and ocean science".
Latest publications/results:
- Can we use data assimilation to estimate dynamical quantities of the model? The answer is given in this recent article, where we proved that the outcome of a properly tuned data assimilation can tell on the spectrum of Lyapunov exponents of the model and its Kolmogorov entropy.
Articles under review:
- Scheffler, G., A. Carrassi, J. Ruiz and M. Pulido. Dynamical effects of inflation in ensemble-based data assimilation under the presence of model error. Under review on Q.J.Roy.Met.Soc.
- Ayers, D., J. Lau, J. Amezcua, A. Carrassi and V. Ojha. Supervised machine learning to estimate instabilities in chaotic systems: estimation of local Lyapunov exponents. Under review on Q.J.Roy.Met.Soc. Preprint available here.
- Schevenhoven, F.J. and A. Carrassi. Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO - v.1. Under review on Geoscientific Model Development. Preprint available here.
Upcoming relevant conferences:
- 23-27 May EGU General Assembly 2022: Session NP5.2 Inverse problems, Predictability, and Uncertainty Quantification in Geosciences using data assimilation and its combination with machine learning. Vienna (Austria)
- 21-23 June. Conference: Machine Learning and Data Assimilation for Dynamical Systems (MLDADS 2022). London (UK).
