An object falling in a fluid reaches a terminal velocity when the drag force and its weight are balanced. Contrastingly, an object impacting into a granular medium rapidly dissipates all its energy and comes to rest always at a shallow depth. Here we study, experimentally and theoretically, the penetration dynamics of a projectile in a very long silo filled with expanded polystyrene particles. We discovered that, above a critical mass, the projectile reaches a terminal velocity and, therefore, an endless penetration.

The fractal dimension estimated as the area–perimeter relation constitutes a tool for the characterization of clouds. In this article, we apply it for the first time to study the separation of cirrus and cumulonimbus in the Caribbean area. Based on satellite data, we show that the two types can be differentiated by their area–perimeter ratio as follows: D_Ci = 1.37 ± 0.02 for cirrus, and D_Cb = 1.18 ± 0.05 for cumulonimbus. We also show that the ‘global’ area–perimeter ratio can be used to determine the composition of the cloud field. Moreover, we are able to quantify, through the area–perimeter ratio, the transformation of cumulonimbus into cirrus. Our findings have potential applications for the automatic classification of clouds, and for the understanding of their formation processes.

The short-term prediction of precipitation is a difficult spatio-temporal task due to the non-uniform characterization of meteorological structures over time. Currently, neural networks such as convolutional LSTM have shown ability for the spatio-temporal prediction of complex problems. In this research, we propose an LSTM convolutional neural network (CNN-LSTM) architecture for immediate prediction of various short-term precipitation events using satellite data. The CNN-LSTM is trained with NASA Global Precipitation Measurement (GPM) precipitation data sets, each at 30-min intervals. The trained neural network model is used to predict the sixteenth precipitation data of the corresponding fifteen precipitation sequence and up to a time interval of 180 min. The results show that the increase in the number of layers, as well as in the amount of data in the training data set, improves the quality of the forecast.

Experimental observation of a new mechanism of sandpile formation is reported. As a steady stream of dry sand is poured onto a horizontal surface, a pile forms which has a thin river of sand on one side flowing from the apex of the pile to the edge of its base. The river rotates about the pile, depositing a new layer of sand with each revolution, thereby causing the pile to grow. For small piles the river is steady and the pile formed is smooth. For larger piles, the river becomes intermittent and the surface of the pile becomes undulating. The essential features of the system that produce the phenomenon are discussed, and the robustness of the phenomena is demonstrated with experiments using different boundary conditions and sands.

Understanding the penetration dynamics of intruders in granular beds is relevant not only for fundamental physics, but also for geophysical processes and construction on sediments or granular soils in areas potentially affected by earthquakes. While the penetration of intruders in two dimensional (2D) laboratory granular beds can be followed using video recording, this is useless in three dimensional (3D) beds of non-transparent materials such as common sand. Here, we propose a method to quantify the sink dynamics of an intruder into laterally shaken granular beds based on the temporal correlations between the signals from a reference accelerometer fixed to the shaken granular bed, and a probe accelerometer deployed inside the intruder. Due to its analogy with the working principle of a lock-in amplifier, we call this technique lock-in accelerometry.