Abstracted/indexed

In non-equilibrium statistical mechanics, which describes forced-dissipative systems, as air-sea interaction, there is no universal probability density function (pdf). Some such systems have recently been demonstrated to exhibit a symmetry called a fluctuation theorem (FT), which strongly constrains the shape of the pdf. Using satellite data, the mechanical power-input to the ocean by air-sea interaction is questioned to follow or not a FT. A FT is found to apply over specific ocean regions.

The assimilation of observations using standard algorithms can lead to violation of physical laws such as mass conservation, which has been shown to have detrimental impact on the system's forecast. We use a neural network (NN) to correct for this mass violation, using training data generated from expensive algorithms that can constrain such physical properties. We found in idealized setup that the NN can match the performance of these expensive algorithms, at neglectable computational costs.

Earthquakes have triggered numerous landslides, and much progress has been made in their study. But so far, the dynamics behavior of slope disasters is still lack of understanding, such as “Is there a definite energy and spacial distribution of earthquake-induced landslides? Do giant landslides and small landslides follow different formation mechanisms?”. In this study, we attempts to answer these questions from the point of view of the Self-Organized Critical (SOC) theory.

Long-lived eddies are an important part of the ocean circulation. Here a dataset for studying eddies in the Gulf of Mexico is created through the analysis of trajectories of drifting instruments. The method involves the identification of quasi-periodic signals, characteristic of particles trapped in eddies, from the displacement records, followed by the creation of a measure of statistical significance. It is expected that this dataset will be of use to other authors studying this region.