NP
Nonlinear Processes in Geosciences

ensemble

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]

NPG Paper of the Month: “Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean”

March 16, 2021
NPG Paper of the Month: “Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean”
Result of the OPTICS algorithm applied to the trajectories modelled in the Bickley Jet model flow. (a), (d) and (g) show the reachability plot with different DBSCAN clustering results, indicated by the black horizontal line. The corresponding clustering results of each choice of DBSCAN parameter is shown on the right of the reachability plots for different times. Grey particles correspond to noise. Axis units in the centre and right column are in 1000 km.

The February 2021 NPG Paper of the Month award goes David Wichmann and his co-authors for their paper “Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean“. Understanding the transport of tracers and particulates is an important topic in oceanography and in fluid dynamics in general. Th ...[Read More]

NPG Paper of the Month: “Statistical postprocessing of ensemble forecasts for
 severe weather at Deutscher Wetterdienst”

December 23, 2020
NPG Paper of the Month: “Statistical postprocessing of ensemble forecasts for
 severe weather at Deutscher Wetterdienst”
Probabilities of wind gusts higher than 14 m/s on
a regular 1 km grid over Germany; 13 h forecast lied time from Ensemble-MOS for COSMO-DE-EPS from 29 October 2018. Licence: Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst.

The October 2020 NPG Paper of the Month award goes to Reinhold Hess for the paper “Statistical postprocessing of ensemble forecasts for 
severe weather at Deutscher Wetterdienst“. Ensemble Forecasting rose with the understanding of the limited predictability of weather. In a perfect ensemble system, the obtained ensemble of forecasts expresses the distribution of possible weather scena ...[Read More]

NPG Paper of the Month: “Applications of matrix factorization methods to climate data”

December 7, 2020
NPG Paper of the Month: “Applications of matrix factorization methods to climate data”
Spatial patterns of geopotential height anomalies corresponding to thek-means centroids (first column), archetypes (secondcolumn), convex coding basis vectors. License: https://doi.org/10.5194/npg-27-453-2020

The September 2020 NPG Paper of the Month award goes to Dylan Harries and Terence J. O’Kane for their paper “Applications of matrix factorization methods to climate data” (https://doi.org/10.5194/npg-27-453-2020). Dylan is a postdoctoral fellow within the Oceans and Atmosphere business unit of CSIRO (Australia). His current research focuses on methods for learning reduced-order models from d ...[Read More]