NP
Nonlinear Processes in Geosciences

NPG Paper of the Month

NP Paper of the Month: “Representation learning with unconditional denoising diffusion models for dynamical systems”

October 24, 2024
NP Paper of the Month: “Representation learning with unconditional denoising diffusion models for dynamical systems”

About the revolution of generating butterflies Imagine the most vibrant butterfly you can conceive. Congratulations – you’ve just achieved what generative AI does! This technology can create images from simple text descriptions, revolutionising image generation. But as geoscientists, how can we use it to improve the prediction of chaotic system like our weather? How can it help us to discover prop ...[Read More]

NPG Paper of the Month: “A Range of Outcomes: The Combined Effects of Internal Variability and Anthropogenic Forcing on Regional Climate Trends over Europe”

May 19, 2023

The NPG paper of the month for February 2023 was awarded to “A Range of Outcomes: The Combined Effects of Internal Variability and Anthropogenic Forcing on Regional Climate Trends over Europe” by Clara Deser and Adam S. Phillips. How much will Europe warm in the next 50 years? Will precipitation increase or decrease? Are past climate trends unique or could alternate realities have exis ...[Read More]

NPG Paper of the Month: “Brief communication: Climate science as a social process – history, climatic determinism, Mertonian norms and post-normality”

February 16, 2023

The NPG paper of the month for January 2023 was awarded to “Brief communication: Climate science as a social process – history, climatic determinism, Mertonian norms and post-normality” by Hans von Storch. One could argue that climate science is part of geophysics, or more precisely that part of climate science is part of geophysics. But when referring to ”processes” in ”Nonlinear Processes in Geo ...[Read More]

NPG Paper of the Month: “Inferring the instability of a dynamical system from the skill of data assimilation exercises”

April 25, 2022
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).

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]