scholarly journals Validating the Space Weather Modeling Framework (SWMF) for applications in northern Europe

2020 ◽  
Vol 10 ◽  
pp. 33
Author(s):  
Norah Kaggwa Kwagala ◽  
Michael Hesse ◽  
Therese Moretto ◽  
Paul Tenfjord ◽  
Cecilia Norgren ◽  
...  
Keyword(s):  
Space Weather ◽  
Geomagnetic Storms ◽  
Skill Score ◽  
Probability Of Detection ◽  
Rate Of Change ◽  
Local Times ◽  
False Detection ◽  
Modeling Framework ◽  
Heidke Skill Score ◽  
Weather Modeling

In this study we investigate the performance of the University of Michigan’s Space Weather Modeling Framework (SWMF) in prediction of ground magnetic perturbations (ΔB) and their rate of change with time (dB/dt), which is directly connected to geomagnetically induced currents (GICs). We use the SWMF set-up where the global magnetosphere provided by the Block Adaptive Tree Solar-wind Roe-type Upwind Scheme (BATS-R-US) MHD code, is coupled to the inner magnetosphere and the ionospheric electrodynamics. The validation is done for ΔB and dB/dt separately. The performance is evaluated via data-model comparison through a metrics-based approach. For ΔB, the normalized root mean square error (nRMS) and the correlation coefficient are used. For dB/dt, the probability of detection, the probability of false detection, the Heidke skill score, and the frequency bias are used for different dB/dt thresholds. The performance is evaluated for eleven ground magnetometer stations located between 59° and 85° magnetic latitude and spanning about five magnetic local times. Eight geomagnetic storms are studied. Our results show that the SWMF predicts the northward component of the perturbations better at lower latitudes (59°–67°) than at higher latitudes (>67°), whereas for the eastward component, the model performs better at high latitudes. Generally, the SWMF performs well in the prediction of dB/dt for a 0.3 nT/s threshold, with a high probability of detection ≈0.8, low probability of false detection (<0.4), and Heidke skill score above zero. To a large extent the model tends to predict events as often as they are actually occurring in nature (frequency bias 1). With respect to the metrics measures, the dB/dt prediction performance generally decreases as the threshold is raised, except for the probability of false detection, which improves.

The quantification and possible sources of spatially structured and large amplitude geomagnetic depressions during strong geomagnetic storms measured by IMAGE

2021 ◽  
Author(s):  
Andrew Dimmock ◽  
Lisa Rosenqvist ◽  
Ari Viljanen ◽  
Colin Forsyth ◽  
Mervyn Freeman ◽  
...  
Keyword(s):  
Space Weather ◽  
Geomagnetic Storms ◽  
Dynamical Behaviour ◽  
Geomagnetically Induced Currents ◽  
Geomagnetic Disturbances ◽  
Modeling Framework ◽  
Coupling Dynamics ◽  
Weather Modeling ◽  
Induced Currents ◽  
Coupling Processes

&lt;p&gt;Geomagnetically Induced Currents (GICs) are a space weather hazard that can negatively impact large ground-based infrastructures such as power lines, pipelines, and railways. They are driven by the dynamic spatiotemporal behaviour of currents flowing in geospace, which drive rapid geomagnetic disturbances on the ground. In some cases, geomagnetic disturbances are highly localised and spatially structured due to the dynamical behaviour of geospace currents and magnetosphere-ionosphere (M-I) coupling dynamics, which are complex and often unclear.&lt;/p&gt;&lt;p&gt;In this work, we investigate and quantify the spatial structure of large geomagnetic depressions exceeding several hundred nT according to the 10 strongest events measured over Fennoscandia by IMAGE. Using ground magnetometer measurements we connect these spatially structured geomagnetic disturbances to possible M-I coupling processes and identify their likely magnetospheric origin. In addition, the ability for these disturbances to drive large GICs is assessed by calculating their respective geoelectric fields in Sweden using the SMAP ground conductivity model. To compliment the observations, we also utilise high resolution runs (&gt;7 million cells) of the Space Weather Modeling Framework (SWMF) to determine to what extent global MHD models can capture this behaviour.&lt;/p&gt;

scholarly journals Integration of RAM-SCB into the Space Weather Modeling Framework

2018 ◽  
Vol 177 ◽  
pp. 160-168 ◽  
Author(s):  
Daniel T. Welling ◽  
Gabor Toth ◽  
Vania K. Jordanova ◽  
Yiqun Yu
Keyword(s):  
Space Weather ◽  
Modeling Framework ◽  
Weather Modeling

scholarly journals Halloween Storm Simulations with the Space Weather Modeling Framework

2006 ◽  
Author(s):  
Tamas Gombosi ◽  
Gabor Toth ◽  
Igor Sokolov ◽  
Ward Manchester ◽  
Aaron Ridley ◽  
...  
Keyword(s):  
Space Weather ◽  
Modeling Framework ◽  
Weather Modeling

scholarly journals The Space Weather Modeling Framework Goes Open Access

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Tuija Pulkkinen ◽  
Tamas Gombosi ◽  
Aaron Ridley ◽  
Gabor Toth ◽  
Shasha Zou
Keyword(s):  
Open Access ◽  
Space Weather ◽  
Computational Models ◽  
Power Grid ◽  
Magnetic Storms ◽  
Modeling Framework ◽  
Weather Modeling

A versatile suite of computational models, already used to forecast magnetic storms and potential power grid and telecommunications disruptions, is preparing to welcome a larger group of users.

SOLSTICE: Space Weather Modeling Meets Machine Learning

2020 ◽  
Author(s):  
Tamas Gombosi ◽  
Keyword(s):  
Machine Learning ◽  
Space Weather ◽  
High Performance ◽  
Numerical Models ◽  
Machine Learning Techniques ◽  
Grand Challenge ◽  
Science Data ◽  
Modeling Framework ◽  
Weather Modeling ◽  
Solar Storms

&lt;p&gt;The last decade has truly witnessed the rise of the machine age. The enormous expansion of technology that can generate and&amp;#160;manipulate massive amounts of information has transformed all aspects of society. Missions such as SDO and MMS, and numerical models such as the Space Weather Modeling Framework (SWMF) are now routinely generating terabytes of science data, far beyond what can be analyzed directly by humans. Fortunately, concurrent with this explosion in information has come the development of powerful capabilities, such as machine learning (ML) and artificial intelligence (AI), that can retrieve revolutionary new understanding and utility from the massive data sets.&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;SOLSTICE (Solar Storms and Terrestrial Impacts Center) is a recently selected NASA/NSF DRIVE Center. It&lt;/span&gt;&amp;#160;will serve as the vanguard for&amp;#160;developing and applying ML methods, which will then raise the capabilities of the entire community. We will combine&amp;#160;next generation ML technology with our world-leading numerical models and the exquisite data from the space missions to make&amp;#160;breakthrough advances in Heliophysics understanding and space weather capabilities, and then transition our technology to the&amp;#160;CCMC for the benefit of all.&lt;/p&gt;&lt;p&gt;We use ML to attack Grand Challenge Problems that cover the major aspects of space weather science:&amp;#160;(i) use interpretable deep learning models, archived solar observations and high-performance physics-based simulations to identify the&amp;#160;onset mechanism of solar flares and coronal mass ejections; and&amp;#160;(ii) use high-cadence observations and physics-based feature learning to predict solar storms many hours before eruption, training&amp;#160;time-to-event models to predict event times and flare magnitudes using innovative machine learning techniques.&lt;/p&gt;

Effect of selecting different simulation configurations on the prediction performance of the Space Weather Modeling Framework regarding ground magnetic perturbations

2020 ◽  
Author(s):  
Norah Kaggwa Kwagala ◽  
Michael Hesse ◽  
Therese M. Jorgensen ◽  
Paul Tenfjord ◽  
Cecilia Norgren ◽  
...  
Keyword(s):  
Space Weather ◽  
Prediction Performance ◽  
Mhd Equations ◽  
Polar Cap ◽  
Modeling Framework ◽  
Inner Magnetosphere ◽  
Integration Schemes ◽  
Magnetic Perturbations ◽  
Weather Modeling ◽  
Ground Magnetic

&lt;p&gt;&lt;span&gt;This study investigates the effect of selecting different simulation configurations of the Space Weather Modeling Framework (SWMF) on the predictions of ground magnetic perturbations. A historic geomagnetic storm, the St. Patrick Storm 2015, is simulated with several different model configurations. The objective is to investigate how the different configurations affect the prediction performance regarding ground magnetic perturbations. For each simulation, the modeled ground magnetic perturbations are compared to the measured perturbations from several ground magnetometer stations located at sub-auroral, auroral and polar cap latitudes. Among the magnetometer stations are the Norwegian and Greenland magnetometer chains. The comparison is based on metrics for both &lt;em&gt;&amp;#916;B&lt;/em&gt;&amp;#160;&lt;/span&gt;&lt;span&gt;and &lt;em&gt;dB/dt&lt;/em&gt;&lt;/span&gt;&lt;span&gt;. The SWMF configurations investigated include variations in grid resolution and integration schemes for the MHD equations, and different settings for the inner magnetosphere, the ionosphere electrodynamics, and the magnetosphere-ionosphere coupling. &lt;/span&gt;&lt;/p&gt;

scholarly journals Validation of the Space Weather Modeling Framework using observations from CHAMP and DMSP

Space Weather ◽  
2008 ◽  
Vol 6 (3) ◽  
pp. n/a-n/a ◽  
Author(s):  
H. Wang ◽  
A. J. Ridley ◽  
H. Lühr
Keyword(s):  
Space Weather ◽  
Modeling Framework ◽  
Weather Modeling

scholarly journals The August 24, 2002 coronal mass ejection: when a western limb event connects to earth

2008 ◽  
Vol 4 (S257) ◽  
pp. 391-398 ◽  
Author(s):  
Noé Lugaz ◽  
Ilia I. Roussev ◽  
Igor V. Sokolov
Keyword(s):  
Coronal Mass Ejection ◽  
Space Weather ◽  
Three Dimensional ◽  
Active Regions ◽  
Modeling Framework ◽  
Initiation Mechanism ◽  
Weather Modeling ◽  
Field Lines ◽  
Mass Ejection ◽  
Western Limb

AbstractWe discuss how some coronal mass ejections (CMEs) originating from the western limb of the Sun are associated with space weather effects such as solar energetic particles (SEPs), shocks or geo-effective ejecta at Earth. We focus on the August 24, 2002 coronal mass ejection, a fast (~2000 km s−1) eruption originating from W81. Using a three-dimensional magneto-hydrodynamic simulation of this ejection with the Space Weather Modeling Framework (SWMF), we show how a realistic initiation mechanism enables us to study the deflection of the CME in the corona and the heliosphere. Reconnection of the erupting magnetic field with that of neighboring streamers and active regions modify the solar connectivity of the field lines connecting to Earth and can also partly explain the deflection of the eruption during the first tens of minutes. Comparing the results at 1 AU of our simulation with observations by the ACE spacecraft, we find that the simulated shock does not reach Earth, but has a maximum angular span of about 120°, and reaches 35° West of Earth in 58 hours. We find no significant deflection of the CME and its associated shock wave in the heliosphere, and we discuss the consequences for the shock angular span.

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