Calibration and Validation of Swarm Plasma Densities and Electron Temperatures Using Ground‐Based Radars and Satellite Radio Occultation Measurements
Bibliographic record
Abstract
Abstract In this study we calibrate and validate in situ ionospheric electron density (Ne) and temperature (Te) measured with Langmuir probes (LPs) on the three Swarm satellites orbiting the Earth in circular, nearly polar orbits at ~500 km altitude. We assess the accuracy and reliability of the LP data (December 2013 to June 2016) by using nearly coincident measurements from low‐ and middle‐latitude incoherent scatter radars (ISRs), low‐latitude ionosondes, and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites, covering all latitudes. The comparison results for plasma frequency ( ) for each Swarm satellite are consistent across these three, principally different measurement techniques. It shows that the Swarm LPs systematically underestimate plasma frequency by about 10% (0.5–0.6 MHz). The correlation coefficients are high (≥0.97), indicating accurate relative variation in the Swarm LP densities. The comparison of Te from high‐gain LPs and those from ISRs reveals that all three satellites overestimate it by 300–400 K but exhibit high correlations (0.92–0.97) against the validation data. The low‐gain LP Te data show larger overestimation (~700 K) and lower correlation (0.86–0.90). The adjustment of the Swarm LP data based on Swarm‐ISR comparison results removes the systematic biases in the Swarm data and gives plasma frequencies and high‐ and low‐gain electron temperatures that are precise within about 0.4 MHz (8%), 150–230 K, and 260–360 K, respectively. We demonstrate that the applied correction significantly improves the agreement between (1) the plasma densities from Swarm, and from ionosondes and COSMIC, and (2) the Te from Swarm LPs and International Reference Ionosphere 2016.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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 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".