Identifying Efficient Ensemble Perturbations for Initializing Probabilistic Subseasonal‐To‐Seasonal Prediction
- Royal Meteorological Institute, Climatological Research, Brussels, Belgium (jodemaey@meteo.be)
Nowadays, weather forecasts systems are probabilist, based on ensemble of model integrations starting from different initial conditions. How to define efficient sets of initial conditions is now a well settled problem for weather forecasts, but still an open question for longer forecast ranges.
Here, a method to construct initial conditions which produce reliable ensemble forecasts at the particularly challenging subseasonal-to-seasonal forecast range is presented. These initial conditions are obtained by perturbing the analysis with random perturbations projected onto the Koopman and Perron-Frobenius operators’ eigenfunctions, which describe the time-evolution of observables and probability distributions of the system dynamics, respectively. In practice, the perturbations are projected on approximations of these eigenfunctions provided by the Dynamic Mode Decomposition data-driven algorithm, potentially allowing this method to be applied to high-dimensional state-of-the-art prediction models. The effectiveness of this approach is illustrated in the framework of a low-order coupled ocean-atmosphere model, and by comparing it to other well-known ensemble initialization methods based on the Empirical Orthogonal Functions of the model trajectory and on the backward and covariant Lyapunov vectors of the model dynamics. Explanations are provided on why this method is effective and could be applied to operational forecasting models.
References
- Demaeyer, J., Penny, S. G., & Vannitsem, S. Identifying efficient ensemble perturbations for initializing subseasonal-to-seasonal prediction. Journal of Advances in Modeling Earth Systems, 14, e2021MS002828, 2022. https://doi.org/10.1029/2021MS002828
- Vannitsem, S., J. Demaeyer, L. De Cruz, M Ghil, Low-frequency variability and heat transport in a low-order nonlinear coupled ocean-atmosphere model. Physica D, 309, 71-85, 2015. https://doi.org/10.1016/j.physd.2015.07.006
How to cite: Demaeyer, J.: Identifying Efficient Ensemble Perturbations for Initializing Probabilistic Subseasonal‐To‐Seasonal Prediction, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-231, https://doi.org/10.5194/ems2023-231, 2023.
