Severe weather occurrence in the context of solar wind coupling to the magnetosphere-ionosphere-atmosphere system
Bibliographic record
Abstract
<!--!introduction!--> Extreme weather events caused by tropical and extratropical cyclones have destructive impacts on infrastructure, society, and environment. Forecasting extreme weather continues to present challenges. We consider solar wind coupling to the magnetosphere-ionosphere-atmosphere system, mediated by globally propagating aurorally excited atmospheric gravity waves, influencing the occurrence of severe weather. While these gravity waves reach the troposphere with attenuated amplitudes, they are subject to amplification when encountering opposing winds and vertical wind shears. They can release conditional symmetric instability [1] leading to slantwise convection, latent heat release, and contributing to intensification of storms. It has been shown that significant weather events, including explosive extratropical cyclones [2,3], rapid intensification of tropical cyclones [4], and heavy rainfall causing floods and flash floods [5,6] tend to follow arrivals of solar wind high-speed streams from coronal holes. To further support these results, we use various data sets in the superposed epoch analysis of the occurrence of severe weather, including heavy rainfall events and tornado outbreaks, in the context of solar wind coupling to the magnetosphere-ionosphere-atmosphere system. [1] Chen T.-C., et al., J. Atmos. Sci. 75, 2425–2443. doi:10.1175/JAS-D-17-0221.1 [2] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 149, 219–231. doi:10.1016/j.jastp.2016.04.002 [3] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 171, 94–10, 2018. doi:10.1016/j.jastp.2017.07.023 [4] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 183, 36-60, 2019. doi:10.1016/j.jastp.2018.12.009 [5] Prikryl P., et al., Ann. Geophys. 39 (4), 769–93, 2021. doi:10.5194/angeo-39-769-2021 [6] 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".