George Miloshevich

In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a fully convolutional neural network (FCNN). Electron pressure plays an important role in generalized Ohm’s law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit particle-in-cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with a smaller number of particles per cell and showing that our results generalize to a simulation with a large number of particles per cell. We evaluate the statistical properties of the learned equation of state, with a focus on pressure-strain interaction, which is crucial for understanding energy channels in turbulent plasmas. The resulting equation of state learned via FCNN significantly outperforms local closures, such as those learned by multi-layer perceptron (MLP) or double adiabatic expressions. We report that the overall spatial distribution of pressure-strain and its conditional averages are reconstructed well. However, some small-scale features are missed, especially for the off-diagonal components of the pressure tensor. Nevertheless, the results are substantially improved with more training data, indicating favorable scaling and potential for improvement, which will be addressed in future work.

Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Extreme heatwaves are, and likely will be in the future, among the deadliest weather events. Forecasting their occurrence probability a few days, weeks, or months in advance is a primary challenge for risk assessment and attribution but also for fundamental studies about processes, dataset and model validation, and climate change studies. In this work we develop a methodology to build forecasting models which are based on convolutional neural networks, trained on extremely long 8000-year climate model outputs. This approach is parallel to weather model forecasting and has complementary scopes. Because the relation between extreme events is intrinsically probabilistic, we emphasize probabilistic forecast and validation. We demonstrate that neural networks have positive predictive skills, with respect to random climatological forecasts, for the occurrence of long-lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height fields), and also at much longer lead times for slow physical drivers (soil moisture). This forecast is made seamlessly in time and space, for fast hemispheric and slow local drivers. The method is easily implemented and versatile. We find that the neural network selects extreme heatwaves associated with a north hemisphere wave-number 3 pattern. We argue that this machine learning approach should be key in the future for quantitative process studies, model intercomparisons, and dataset studies. For instance, we find that the 2 meter temperature field does not contain any new useful statistical information for heatwave forecast, when added to the 500 hPa geopotential height and soil moisture fields. The main scientific message is that most of the times, training neural networks for predicting extreme heatwaves occurs in a regime of lack of data. We suggest that this is likely to be the case for most other applications to large-scale atmosphere and climate phenomena. Depending on the information to be learned, training might require dataset lengths as long as several thousands of years, or even more, for optimal forecasting skill. For instance, using 100-year-long training sets, a regime of drastic lack of data, leads to severely lower predictive skills and general inability to extract useful information available in the 500 hPa geopotential height field at a hemispheric scale in contrast to the dataset of several thousand years long. Even with several-thousand-year-long datasets, no convergence is observed in the predictive skills coming from hemispheric geopotential height fields. We discuss perspectives for dealing with the lack of data regime, for instance, rare event simulations and how transfer learning may play a role in this latter task.

The main goal of this article is to explore the possible sources for large-scale coherent magnetic structures that can be created in turbulent electron-proton plasmas. We work with the model developed for applications to solar wind, which is obtained as a fluid closure of gyrokinetic equations that describe magnetized collisionless plasmas. It can be thought of as a generalisation of magnetohydrodynamics spaning the scales above and below the ion Larmor radius (intermediate scale), which leads to nonlinear dispersive phenomenology of interacting kinetic Alfvén waves (KAW). The chief quantity of interest is Generalized Cross Helicity (GCH) which measures the energy of imbalance between the two KAW eigenmodes. At large scales they merge with Alfvén wave branches which propagate forward and backward relative to the magnetic field line. It was known that at large scales the cascade is forward like in usual Kolmogorov phenomenology and there were theoretical reasons to believe the cascade reversed sign at small scales. Using 3D numerical simulations we have found that the inverse cascade is suppressed at intermediate scales, which leads to the formation of large elongated magnetic vortices on the Larmor scale (termed finite-scale condensate). This is interesting since it is perhaps related to the result by another group (Meyrand at al 2021), whereby the forward cascade in the similar system was also suppressed at the Larmor scale. This process can potentially explain ion-kinetic spectral breaks in plasma turbulence, and provide theory alternative to that of, where the effects of Landau damping were phenomenologically included. Furthermore, we study the decay instability of KAW, i.e. the decay of a pump mode into two daughter modes, one with larger wavenumber and one with smaller. This is resonant behavior which is possible due to the dispersive nature of KAW. Related to these events are instabilities of the balanced state (between the two KAW eigenmodes)