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SASIP March Newsletter
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Science Updates

Investigating the relation between elastic and relaxation properties of dry, frictional granular media during shear deformation

In this new preprint available on arXiv, Aurélien Rigotti et al. investigate the mechanical behavior of sheared, dry, frictional granular media, revealing a transition from a transient stage to a stationary state. Microscale grain rearrangements induce elastic damage, linking elastic and relaxation properties to a complex, damage-dependent rheology. In granular sea ice, this damage dependence arises from floe rearrangement rather than fracturing, as seen in perennial ice. This underscores the need for an elasto-visco-plastic rheology to accurately capture its mechanical behavior.

Figure 22. Residual pressure Pc / σyy as a function of bulk modulus damage dK for different imposed loading. A change of state is exhibit by granular assembly with a elato-visco-plastic behaviour below dK = 0.2 and a viscous behaviour beyond.

Four-dimensional variational data assimilation with a sea-ice thickness emulator

In this recent preprint submitted to The Cryosphere, Charlotte Durand et al. introduce a 4D-Var data assimilation system based on a sea-ice thickness emulator to assimilate real-world CS2SMOS satellite retrievals. The developed system achieves similar forecasting scores as the operational neXtSIM-F system, while being orders of magnitude faster.

Figure 8. Visualization of the forecast for 2021-03-26 is shown. The upper left panel displays the neXtSIM-F 9-day forecast, while the upper right panel shows the 9-day forecast from the 4D–Var. The lower left panel presents the CS2SMOS observations, and the lower right panel illustrates the difference between the 4D–Var forecast and the CS2SMOS observations.

Ensemble Kalman filter in latent space using a variational auto encoder pair

Ivo Pasmans et al. submitted a preprint to QJRMS. They tackle the issue of non-Gaussian data assimilation, as often encountered in sea-ice modelling, by combining two variational autoencoders with an ensemble Kalman filter. Tested on simple circular cases, this novel approach stays nearer to the known truth than the classical ensemble Kalman filter, showing its effectiveness in obeying to the underlying manifold.

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March webinar on Friday 28, 4pm CET with Michael Ying, research scientist from NERSC working on data assimilation

Welcome to François Dupé, new intern working on WP5 at CECI-CERFACS, France