CME front and severe space weather
Bibliographic record
Abstract
Abstract Thanks to the work of a number of scientists who made it known that severe space weather can cause extensive social and economic disruptions in the modern high‐technology society. It is therefore important to understand what determines the severity of space weather and whether it can be predicted. We present results obtained from the analysis of coronal mass ejections (CMEs), solar energetic particle (SEP) events, interplanetary magnetic field (IMF), CME‐magnetosphere coupling, and geomagnetic storms associated with the major space weather events since 1998 by combining data from the ACE and GOES satellites with geomagnetic parameters and the Carrington event of 1859, the Quebec event of 1989, and an event in 1958. The results seem to indicate that (1) it is the impulsive energy mainly due to the impulsive velocity and orientation of IMF B z at the leading edge of the CMEs (or CME front) that determine the severity of space weather. (2) CMEs having high impulsive velocity (sudden nonfluctuating increase by over 275 km s −1 over the background) caused severe space weather (SvSW) in the heliosphere (failure of the solar wind ion mode of Solar Wind Electron Proton Alpha Monitor in ACE) probably by suddenly accelerating the high‐energy particles in the SEPs ahead directly or through the shocks. (3) The impact of such CMEs which also show the IMF B z southward from the leading edge caused SvSW at the Earth including extreme geomagnetic storms of mean Dst MP < −250 nT during main phases, and the known electric power outages happened during some of these SvSW events. (4) The higher the impulsive velocity, the more severe the space weather, like faster weather fronts and tsunami fronts causing more severe damage through impulsive action. (5) The CMEs having IMF B z northward at the leading edge do not seem to cause SvSW on Earth, although, later when the IMF B z turns southward, they can lead to super geomagnetic storms of intensity ( Dst min ) less than even −400 nT.
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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 | 0.001 | 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 | 0.001 |
| 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".