The Fast Borealis Ionosphere: High Time‐Resolution Mapping of Polar Ionospheric Flows With SuperDARN
Bibliographic record
Abstract
Abstract Recent improvements to hardware for the Super Dual Auroral Radar Network systems have allowed for a much greater control of radar transmit and receive functionalities than previously possible. One of these functionalities is the application of a new operational mode, known as wide‐beam, which vastly improves the temporal resolution of the radars without compromising their spatial coverage. Wide‐beam allows for the retrieval of line‐of‐sight ionospheric drift velocities at a temporal resolution of 3.7 s, a sixteen‐fold improvement from the one‐minute resolution offered by traditional operational modes. In this paper, we use wide‐beam data from the Borealis SuperDARN systems, located in Canada, to derive local horizontal ionospheric plasma velocity fields above Northern Canada, Greenland, and the polar cap, at a 3.7 s temporal resolution. For this local fitting of ionospheric velocity data, we use the Local Mapping of Ionospheric Electrodynamics (Lompe) spherical elementary current systems technique. This new data product, which we call the Fast Borealis Ionosphere, is compared to both the global SuperDARN spherical harmonic convection pattern data product (the Map Potential technique), as well as Lompe convection patterns derived using the traditional SuperDARN narrow‐beam scanning mode. We show that Lompe systematically produces a better representation of the underlying radar velocity data than Map Potential, that the 3.7 s wide‐beam data contains a significant amount more ionospheric variability than narrow‐beam, and that the high time‐resolution convection patterns can resolve dynamic ionospheric events lasting on the order of tens of seconds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".