Traveling Ionospheric Disturbances Induced by the Secondary Gravity Waves From the Tonga Eruption on 15 January 2022: Modeling With MESORAC‐HIAMCM‐SAMI3 and Comparison With GPS/TEC and Ionosonde Data
Bibliographic record
Abstract
Abstract We simulate the gravity waves (GWs) and traveling ionospheric disturbances (TIDs) created by the Hunga Tonga‐Hunga Ha'apai (hereafter “Tonga”) volcanic eruption on 15 January 2022 at ∼04:15 UT. We calculate the primary GWs and forces/heatings generated where they dissipate with MESORAC, the secondary GWs with HIAMCM, and the TIDs with SAMI3. We find that medium and large‐scale TIDs (MSTIDs and LSTIDs) are induced by the secondary GWs, with horizontal phase speeds c H ≃ 100–750 m/s, horizontal wavelengths λ H ≃ 600–6,000 km, and ground‐based periods τ r ≃ 30 min to 3 hr. The LSTID amplitudes over New Zealand are ≃2–3 TECU, but decrease sharply ≃ 5,000 km from Tonga. The LSTID amplitudes are extremely small over Australia and South Africa because body forces create highly asymmetric GW fields and the GWs propagate perpendicular to the magnetic field there. We analyze the TIDs from SAMI3 and find that a 30 min detrend window eliminates the fastest far‐field LSTIDs. We analyze the GPS/TEC via detrending with 2–3 hr windows, and find that the fastest LSTIDs reach the US and South America at ∼8:30–9:00 UT with c H ≃ 680 m/s, λ H ≃ 3,400 km, and τ r ≃ 83 min, in good agreement with model results. We find good agreement between modeled and observed TIDs over New Zealand, Australia, Hawaii, Japan and Norway. The observed F‐peak height, hmF2, drops by ≃ 110–140 km over the western US with a 2.8 hr periodicity from 8:00 to 13:00 UT. We show that the Lamb waves (LWs) observed by AIRS with λ H = 380 km have amplitudes that are ≃ 2.3% that of the primary GWs at z ≃ 110 km. We conclude that the observed TIDs can be fully explained by secondary GWs rather than by “leaked” LWs.
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 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".