Recent  

In this study, we improve weather forecasts by incorporating land and atmospheric data in a model. We focus on soil moisture, crucial for predicting droughts and floods. By using soil moisture data, we enhance temperature and precipitation predictions. However, challenges remain, and further research is needed to refine the approach using satellite data and higher-resolution models.

Clustering is a vital phenomenon for planktonic microorganisms, which is related to their predation and mating. We develop a Lagrangian model for plankton micro-swimmers to consider the effect of fluid inertial torque on their motion. This torque is recently reported to drive micro-swimmers to swim against gravity. We use direct numerical simulation to analyze the how fluid inertial torque causes phenomena of clustering and preferential sampling of specific flow regions for micro-swimmers.

Forecasting wind fields over mountains is of high importance for several applications and particularly to understand how wind erodes and deposes snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large scale wind forecasts in mountains and increase their spatial resolution.

The wPSOGSA algorithm is compared with PSO and GSA in evaluating 1D resistivity models from MT data. MT data from various geological terrains are used to signify the relevance of these methods, generating a large number of models that fit the apparent resistivity and phase curves. The best-fitting models were then chosen for statistical analysis, including posterior PDF, correlation matrix, and stability analysis, to enhance the accuracy and efficacy of the mean model with the least uncertainty

Increasing amount of data allows us today to move from considering low order moments of fluctuating observations to their Probability Density Functions (PDFs). We found a new analytic form of the PDF for two years of sea surface current increments data in the Gulf of Trieste (Northern Adriatic Sea) using the maximum entropy principle twice: in a short timescale and in a longer one. The data for the different wind forcings follow the same analytic PDF form, pointing towards a universal behaviour.

Many systems in nature are characterized by the coexistence of different stable states for given environmental parameters and external forcing. Examples can be found in different fields of science ranging from ecosystems to climate dynamics. Perturbations can lead to critical transitions (tipping) from one stable state to another. The study of these transitions requires the development of new methodological approaches that allow for modeling, analyzing, and predicting them.

Electron holes have been reported in space plasmas along with suprathermal plasmas far of Maxwellian equilibrium. In particular electron distributions with elongated suprathermal distributions (kappa distributions) are of current interest for space plasmas. This work consider the regularized kappa distributions, which are well beyond the Maxwellian equilibrium and showing a harder suprathermal electron population.

This manuscript provides with the perspective that the displacement vector field of physical state fields should be determined by the tensor fields associated to the physical fields. The advantage of this perspective is that certain physical quantities can be conserved while applying a displacement vector field to transfer the original physical field. A direct application90of this perspective is the physically constrained covariance inflation scheme proposed in this manuscript.