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.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".