Ensemble Numerical Simulations of Realistic SEP Events and the Inspiration for Space Weather Awareness
Bibliographic record
Abstract
Abstract The solar energetic particle (SEP) event is a kind of hazardous space weather phenomena, so its quantitative forecast is of great importance from the aspect of space environmental situation awareness. We present here a set of SEP forecast tools, which consists of three components : (1) a simple polytropic solar wind model to estimate the background solar wind conditions at the inner boundary of 0.1 AU (about 20 R ⊙ ); (2) an ice-cream-cone model to estimate the erupted coronal mass ejection (CME) parameters; and (3) the improved Particle Acceleration and Transport in the Heliosphere (iPATH) model to calculate particle fluxes and energy spectra. By utilizing the above models, we have simulated six realistic SEP events from 2010 August 14 to 2014 September 10, and compared the simulated results to the Geostationary Operational Environmental Satellites (GOES) spacecraft observations. The results show that the simulated fluxes of >10 MeV particles agree with the observations while the simulated fluxes of >100 MeV particles are higher than the observed data. One of the possible reasons is that we have adopted a simple method in the model to calculate the injection rate of energetic particles. Furthermore, we have conducted the ensemble numerical simulations over these events and investigated the effects of different background solar wind conditions at the inner boundary on SEP events. The results imply that the initial CME density plays an important role in determining the power spectrum, while the effect of varying background solar wind temperature is not significant. Naturally, we have examined the influence of CME initial density on the numerical prediction results for virtual SEP cases with different CME ejection speeds. The result shows that the effect of initial CME density variation is inversely associated with CME speed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".