Severe weather influenced by aurorally excited gravity waves contributing to release of conditional symmetric instability?
Bibliographic record
Abstract
Forecasting weather has significantly improved but continues to present challenges, such as prediction of flash floods, tornado outbreaks, and rapid intensification of tropical cyclones. We consider a possible influence on severe weather occurrence through solar wind coupling to the magnetosphere-ionosphere-atmosphere system, mediated by aurorally excited atmospheric gravity waves. Solar wind disturbances, including high-speed streams, high-density plasma adjacent to the heliospheric current sheet, and interplanetary coronal mass ejections, cause intensifications of ionospheric currents at high latitudes launching gravity waves globally propagating in the atmosphere [1]. While these gravity waves reach the troposphere with much attenuated amplitudes, they are subject to amplification when encountering opposing winds and vertical wind shears. They may contribute to release of conditional symmetric instabilities [2] leading to slantwise convection, latent heat release and intensification of storms. The ERA5 re-analysis is used to evaluate slantwise convective available potential energy (SCAPE) that is of importance in the development of storms. It has been shown that significant weather events, including explosive extratropical cyclones [3,4], rapid intensification of tropical cyclones [5], and heavy rainfall causing floods and flash floods [6,7] tend to occur following arrivals of solar wind high-speed streams from coronal holes. Further evidence is provided by superposed-epoch analysis of high-rate precipitation occurrence obtained from satellite-based precipitation data sets. To support the published results, the occurrence of heavy-rainfall-induced floods and cool season precipitation events in Canada, as well as large tornado outbreaks in the United States are studied in the context of solar wind. [1] Mayr H.G., et al., Space Sci. Rev. 54, 297–375, 1990. doi:10.1007/BF00177800 [2] Chen T.-C., et al., J. Atmos. Sci. 75, 2425–2443. doi:10.1175/JAS-D-17-0221.1[3] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 149, 219–231. doi:10.1016/j.jastp.2016.04.002[4] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 171, 94–10, 2018. doi:10.1016/j.jastp.2017.07.023[5] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 183, 36-60, 2019. doi:10.1016/j.jastp.2018.12.009[6] Prikryl P., et al., Ann. Geophys. 39 (4), 769–93, 2021. doi:10.5194/angeo-39-769-2021[7] Prikryl P., et al., Atmosphere 12 (9), 2021. doi:10.3390/atmos12091186.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".