The NPG paper of the month was awarded to “Inferring the instability of a dynamical system from the skill of data assimilation exercises” by Yumeng Chen, Alberto Carrassi, and Valerio Lucarini. Geophysical systems are usually described by a set of dynamical equations that are often non-linear and chaotic (Ghil and Lucarini, 2020). Errors about the initial state can grow, shrink, or sta ...[Read More]
NPG Paper of the Month: “Inferring the instability of a dynamical system from the skill of data assimilation exercises”
Scatter plots of the largest Lyapunov exponent (left) and Kolmogorov-Sinai entropy (right) against the skill of DA measured as the RMSE of the analysis. The experiments are done with model configurations displaying fully developed chaos (main panels) and with weakly chaotic configurations (insets). In all cases a strong observational constraint is applied: we measure the entire state vector at each time step. The theoretical error upper bound are also displayed (red markers).