Improving solar wind modeling at Mercury: Incorporating transient solar phenomena into the WSA‐ENLIL model with the Cone extension
Bibliographic record
Abstract
Abstract Coronal mass ejections (CMEs) and other transient solar phenomena play important roles in magnetospheric and exospheric dynamics. Although a planet may interact only occasionally with the interplanetary consequences of these events, such transient phenomena can result in departures from the background solar wind that often involve more than an order of magnitude greater ram pressure and interplanetary electric field applied to the planetary magnetosphere. For Mercury, an order of magnitude greater ram pressure combined with high Alfvén speeds and reconnection rates can push the magnetopause essentially to the planet's surface, exposing the surface directly to the solar wind flow. In order to understand how the solar wind interacts with Mercury's magnetosphere and exosphere, previous studies have used the Wang‐Sheeley‐Arge (WSA)‐ENLIL solar wind modeling tool to calculate basic and composite solar wind parameters at Mercury's orbital location. This model forecasts only the background solar wind, however, and does not include major transient events. The Cone extension permits the inclusion of CMEs and related solar wind perturbations and thus enables characterization of the effects of strong solar wind disturbances on the Mercury system. The Cone extension is predicated on the assumption of constant angular and radial velocities of CMEs to integrate these phenomena into the WSA‐ENLIL coupled model. Comparisons of the model results with observations by the MESSENGER spacecraft indicate that the WSA‐ENLIL+Cone model more accurately forecasts total solar wind conditions at Mercury and has greater predictive power for magnetospheric and exospheric processes than the WSA‐ENLIL model alone.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".