Progress towards physics-based space weather forecasting with exascale computing

Innocenti, Maria Elena; Johnson, Alec; Amaya, Jorge; Deca, Jan; Olshevsky, Vyacheslav; Lapenta, Giovanni

doi:10.1016/j.advengsoft.2016.06.011

Cited by 24 publications

(24 citation statements)

References 54 publications

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“…A guide field B g ∕B 0 = 0.3, with B 0 the asymptotic value in the x direction, suppresses the formation of the firehose instability [Karimabadi et al, 1999;Le et al, 2013;Goldman et al, 2015]. The code used is the semiimplicit code iPic3D [Markidis et al, 2010;Innocenti et al, 2016].…”

Section: Cuts Of Fields and Moments Across The So-ss/rd Structurementioning

confidence: 99%

Switch‐off slow shock/rotational discontinuity structures in collisionless magnetic reconnection: What to look for in satellite observations

Innocenti

Cazzola

Mistry

et al. 2017

Geophysical Research Letters

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In Innocenti et al. (2015) we have observed and characterized for the first time Petschek‐like switch‐off slow shock/rotational discontinuity (SO‐SS/RD) compound structures in a 2‐D fully kinetic simulation of collisionless magnetic reconnection. Observing these structures in the solar wind or in the magnetotail would corroborate the possibility that Petschek exhausts develop in collisionless media as a result of single X point collisionless reconnection. Here we highlight their signatures in simulations with the aim of easing their identification in observations. The most notable signatures include a four‐peaked ion current profile in the out‐of‐plane direction, associated ion distribution functions, increased electron and ion anisotropy downstream the SS, and increased electron agyrotropy downstream the RDs.

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Section: Cuts Of Fields and Moments Across The So-ss/rd Structurementioning

confidence: 99%

Switch‐off slow shock/rotational discontinuity structures in collisionless magnetic reconnection: What to look for in satellite observations

Innocenti

Cazzola

Mistry

et al. 2017

Geophysical Research Letters

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“…The state of the art in high‐performance computing is petascale (

>

^{15}

floating point operations per second), with exascale computing expected to be achieved in the near future (Wright, ). Innocenti et al () present an approach to space weather prediction that aims toward exascale computing capabilites, using particle‐in‐cell methods to resolve physics on kinetic scales everywhere in the magnetosphere. Other approaches that do not follow the framework approach of coupling models that implement different, domain‐relevant physics (Baker et al, ; Tóth et al, ) include the Vlasiator hybrid‐Vlasov model (von Alfthan et al, ), which evolves the ion distribution function while treating the electrons as a fluid, other kinetic‐fluid modeling approaches (Kolobov & Deluzet, ; Roytershteyn & Delzanno, ), and particle‐in‐cell models (Yang et al, ).…”

Section: Challenges In Numerical Modelingmentioning

confidence: 99%

“…Other approaches that do not follow the framework approach of coupling models that implement different, domain-relevant physics (Baker et al, 2009;Tóth et al, 2005) include the Vlasiator hybrid-Vlasov model (von Alfthan et al, 2014), which evolves the ion distribution function while treating the electrons as a fluid, other kinetic-fluid modeling approaches (Kolobov & Deluzet, 2019;Roytershteyn & Delzanno, 2018), and particle-in-cell models (Yang et al, 2016). Due to computational expense-or more precisely, the availability of computational resources to researchers-these kinetic simulations are currently limited to either small domains (Peng et al, 2015) or to two spatial dimensions (Innocenti et al, 2017;Jarvinen et al, 2018). The next generation of NSWP models should leverage parallelization across heterogeneous systems (Innocenti et al, 2017).…”

Section: Computational Approaches Resources and Analysismentioning

confidence: 99%

Challenges and Opportunities in Magnetospheric Space Weather Prediction

Morley

2020

Space Weather

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Space Weather is the study of the dynamics of the coupled Solar‐Terrestrial environment, as these dynamics impact technological systems and human activity. This paper reviews a selection of the advances, challenges, and new opportunities for magnetospheric space weather, and while the focus is on specific phenomena, many other aspects of space weather will have similar challenges and needs. As a scientific field with direct applications, the field of space weather is partly driven by imperatives from both policy and operations. We provide an introduction to some of the context in which the field exists and discuss how this might shape future developments and norms within the space weather enterprise. We briefly examine benchmarking, as a policy and operationally driven activity, as it provides immediate societal relevance and an opportunity to stretch scientific understanding. As numerical space weather prediction now becomes routine, and exascale computing is in the near future, we identify challenges relating to computational expense and big data, capturing and accounting for uncertainties, and specification of boundary conditions. Here, as with the observations supporting numerical space weather prediction, the key challenge lies in extending the lead time of predictions. We also discuss the role of data, particularly in regard to model validation and empirical modeling. Due to the growing societal impact of space weather, we also examine the relationships between space weather and its terrestrial counterpart and look at the importance of continuous evaluation, monitoring progress in predictive capability, and communication with researchers, forecasters, and end users.

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“…Data: particles coordinates X and velocities V , mesh Ω with n x cells by n y cells Result: distribution function f i , E drop , and E dev for each subset i 1 begin 2 merge cells to increase the number of particles by subset, new mesh Ω is of size nx r × The simulations are performed with the fully kinetic massively parallel implicit moment method Particle-in-Cell code iPic3D (Markidis et al 2010;Innocenti et al 2017). We are particularly interested in a double Harris sheet case where two well separated reconnection sites can be identified.…”

Section: General Proceduresmentioning

confidence: 99%

Characterizing Magnetic Reconnection Regions Using Gaussian Mixture Models on Particle Velocity Distributions

Dupuis

Goldman

Newman

et al. 2020

ApJ

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We present a new method based on unsupervised machine learning to identify regions of interest using particle velocity distributions as a signature pattern. An automatic density estimation technique is applied to particle distributions provided by PIC simulations to study magnetic reconnection. The key components of the method involve: i) a Gaussian mixture model determining the presence of a given number of subpopulations within an overall population, and ii) a model selection technique with Bayesian Information Criterion to estimate the appropriate number of subpopulations. Thus, this method identifies automatically the presence of complex distributions, such as beams or other non-Maxwellian features, and can be used as a detection algorithm able to identify reconnection regions. The approach is demonstrated for a specific double Harris sheet simulations but it can in principle be applied to any other type of simulation and observational data on the particle distribution function.

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Progress towards physics-based space weather forecasting with exascale computing

Cited by 24 publications

References 54 publications

Switch‐off slow shock/rotational discontinuity structures in collisionless magnetic reconnection: What to look for in satellite observations

Switch‐off slow shock/rotational discontinuity structures in collisionless magnetic reconnection: What to look for in satellite observations

Challenges and Opportunities in Magnetospheric Space Weather Prediction

Characterizing Magnetic Reconnection Regions Using Gaussian Mixture Models on Particle Velocity Distributions

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