268 research outputs found
Resonant and non-resonant whistlers-particle interaction in the radiation belts
We study the wave-particle interactions between lower band chorus whistlers and an anisotropic tenuous population of relativistic electrons. We present the first direct comparison of first-principle Part
Vlasov simulations of Kinetic Alfv\'en Waves at proton kinetic scales
Kinetic Alfv\'en waves represent an important subject in space plasma
physics, since they are thought to play a crucial role in the development of
the turbulent energy cascade in the solar wind plasma at short wavelengths (of
the order of the proton inertial length and beyond). A full understanding
of the physical mechanisms which govern the kinetic plasma dynamics at these
scales can provide important clues on the problem of the turbulent dissipation
and heating in collisionless systems. In this paper, hybrid Vlasov-Maxwell
simulations are employed to analyze in detail the features of the kinetic
Alfv\'en waves at proton kinetic scales, in typical conditions of the solar
wind environment. In particular, linear and nonlinear regimes of propagation of
these fluctuations have been investigated in a single-wave situation, focusing
on the physical processes of collisionless Landau damping and wave-particle
resonant interaction. Interestingly, since for wavelengths close to and
proton plasma beta of order unity the kinetic Alfv\'en waves have small
phase speed compared to the proton thermal velocity, wave-particle interaction
processes produce significant deformations in the core of the particle velocity
distribution, appearing as phase space vortices and resulting in flat-top
velocity profiles. Moreover, as the Eulerian hybrid Vlasov-Maxwell algorithm
allows for a clean almost noise-free description of the velocity space,
three-dimensional plots of the proton velocity distribution help to emphasize
how the plasma departs from the Maxwellian configuration of thermodynamic
equilibrium due to nonlinear kinetic effects
Approximate semi-analytical solutions for the steady-state expansion of a contactor plasma
We study the steady-state expansion of a collisionless, electrostatic, quasi-neutral plasma
plume into vacuum, with a fluid model. We analyze approximate semi-analytical solutions,
that can be used in lieu of much more expensive numerical solutions. In particular, we focus
on the earlier studies presented in Parks and Katz (1979 American Institute of Aeronautics,
Astronautics Conf. vol 1), Korsun and Tverdokhlebova (1997 33rd Joint Prop. Conf.
(Seattle, WA) AIAA-97-3065), and Ashkenazy and Fruchtman (2001 27th Int. Electric
Propulsion Conf. (Pasadena, CA)). By calculating the error with respect to the numerical
solution, we can judge the range of validity for each solution. Moreover, we introduce a
generalization of earlier models that has a wider range of applicability, in terms of plasma
injection profiles. We conclude by showing a straightforward way to extend the discussed
solutions to the case of a plasma plume injected with non-null azimuthal velocity
Probabilistic prediction of Dst storms one-day-ahead using Full-Disk SoHO Images
We present a new model for the probability that the Disturbance storm time
(Dst) index exceeds -100 nT, with a lead time between 1 and 3 days.
provides essential information about the strength of the ring current around
the Earth caused by the protons and electrons from the solar wind, and it is
routinely used as a proxy for geomagnetic storms. The model is developed using
an ensemble of Convolutional Neural Networks (CNNs) that are trained using SoHO
images (MDI, EIT and LASCO). The relationship between the SoHO images and the
solar wind has been investigated by many researchers, but these studies have
not explicitly considered using SoHO images to predict the index.
This work presents a novel methodology to train the individual models and to
learn the optimal ensemble weights iteratively, by using a customized
class-balanced mean square error (CB-MSE) loss function tied to a least-squares
(LS) based ensemble.
The proposed model can predict the probability that Dst<-100 nT 24 hours
ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation
Coefficient (MCC) of 0.37. The weighted TSS and MCC from Guastavino et al.
(2021) is 0.68 and 0.47, respectively. An additional validation during
non-Earth-directed CME periods is also conducted which yields a good TSS and
MCC score.Comment: accepted by journal <Space Weather
On the generation of probabilistic forecasts from deterministic models
Most of the methods that produce space weather forecasts are based on deterministic models. In order to generate a probabilistic forecast, a model needs to be run several times sampling the input parameter space, in order to generate an ensemble from which the distribution of outputs can be inferred. However, ensemble simulations are costly and often preclude the possibility of real-time forecasting. We introduce a simple and robust method to generate uncertainties from deterministic models, that does not require ensemble simulations. The method is based on the simple consideration that a probabilistic forecast needs to be both accurate and well calibrated (reliable). We argue that these two requirements are equally important, and we introduce the Accuracy-Reliability cost function that quantitatively measures the trade-off between accuracy and reliability. We then define the optimal uncertainties as the standard deviation of the Gaussian distribution that minimizes the cost function. We demonstrate that this simple strategy, implemented here by means of a deep neural network, produces accurate and well-calibrated forecasts, showing examples both on synthetic and real-world space weather data
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