Energy-based predictions in Lorenz system by a unified formalism and neural network modelling

Abstract

Abstract. In the framework of a unified formalism for Kolmogorov-Lorenz systems, predictions of times of regime transitions in the classical Lorenz model can be successfully achieved by considering orbits characterised by energy or Casimir maxima. However, little uncertainties in the starting energy usually lead to high uncertainties in the return energy, so precluding the chance of accurate multi-step forecasts. In this paper, the problem of obtaining good forecasts of maximum return energy is faced by means of a neural network model. The results of its application show promising results.

Climatic attribution at the regional scale: a case study on the role of circulation patterns and external forcings

“…More specifically, here we adopt an NN tool developed some years ago by Pasini and Potestà () for both diagnostic characterization and forecast in complex systems. Over the years, it has been applied to several problems in the atmospheric boundary layer (Pasini and Potestà, ; Pasini et al , ; Pasini and Ameli, ; Pasini et al , ), to the analysis of toy models of climatic relevance (Pasini, ; Pasini et al , ), to climatic impacts on fauna (Pasini et al , ) and, finally, to the problem of attribution at global and regional scales (Pasini et al , ; Pasini and Langone, , ).…”

Oscillating forcings and new regimes in the Lorenz system: a four-lobe attractor

“…In our case, we use the Lie-Poisson structure of Lorenz system, which corresponds to the simplest Nambu dynamics. In what follows, we adopt a unified formalism previously developed (Pasini et al, 1998;Pasini and Pelino, 2000;Pelino and Pasini, 2001;Pasini et al, 2010;Gianfelice et al, 2012) in order to clearly discuss the effect of different forcings and test some general cases, finally reaching the evidence of creation of new regimes and a four lobe Lorenz attractor.…”