High-resolution Poynting fluxes derived from the ESA Swarm mission: How much are we underestimating?
Bibliographic record
Abstract
The ESA Swarm constellation of satellites have been measuring the ionospheric electric and perturbation magnetic fields since 2013. Recently, the entire dataset of Swarm electric fields has been reprocessed into a 16Hz data product, allowing the analysis of ionospheric dynamics on sub-kilometre scales. In combination with the on-board magnetometer data, the Swarm satellites can use the electric field measurements to determine the total electromagnetic energy into and out of the ionosphere, the Poynting flux. The 16Hz dataset allows for the capturing of much smaller scale sizes than previously considered, thus presenting the opportunity to study how much Poynting flux is missed when utilizing data across typically monitored scales (usually on the order of tens to hundreds of kilometres). We present a statistical analysis of the Swarm A and B derived 16Hz Poynting flux, utilising various low-pass filters on the electric and magnetic field data to simulate smoothing the data to larger scale sizes. We find that by increasing the width of the low-pass filters, measured Poynting flux decreases significantly and quickly. Our results show that there is an over 50% underestimation in the total hemisphere integrated Poynting flux when observing it on scale sizes of a few hundred kilometres, compared to the raw 16Hz measurements that correspond to scales of around 0.5km. Under certain circumstances, as much as a 10% underestimation in the Poynting flux is observed by increasing scale size to only 5km. These results stress the importance observing small-scale electric and magnetic fields, as they may account for a large proportion of the ionosphere-thermosphere energy budget.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".