Near-Real-Time Regional Ionospheric Data Assimilation Using The Local Ensemble Transform Kalman Filter
Bibliographic record
Abstract
The high latitude ionosphere provides a challenging environment for space weather forecasting due to its highly dynamic behaviour and the sparsity of data. By design, climatological models cannot adequately capture the short-term variability of the ionosphere. To be able to provide the best possible understanding of the current state of the ionosphere, augmenting traditional empirical models with near-real-time data sources is necessary. A number of instruments are able to provide data with less than two-hour latency, including Global Navigation Satellite System (GNSS) receivers, ionosondes, and satellite-borne altimeters and GNSS receivers. These instruments are both sparsely and unevenly distributed at high latitudes, which creates challenges for traditional assimilation approaches. The locations of ground-based GNSS receivers preclude tomography in much of the region, a condition which is worsened when assimilation is limited to those stations that provide near-real-time data. We use a Local Ensemble Transform Kalman Filter (LETKF) to assimilate data from the above sources [1]. The assimilation is defined over a HEALPIX grid aligned with the geomagnetic pole, ensuring an even spacing of grid points and minimizing computational footprint [2]. The vertical electron density is parameterized as a modified semi-epstein layer, fitted from an empirical model. The Empirical Canadian High Arctic Model (ECHAIM) is used as a basis for the assimilation above 450 magnetic latitude, with the NeQuick model used below [3, 4]. We will here present results using real data collected during the geomagnetic storm during early September 2017. By limiting our assimilation data to those sources that provide data in near-real-time, we can test the capabilities of the assimilation during a large scale disturbance from climatology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".