Heavy rainfall, floods, and flash floods in the context of solar wind coupling to the magnetosphere-ionosphere-atmosphere system
Bibliographic record
Abstract
Abstract. Heavy rainfall events causing floods and flash floods are examined in the context of solar wind coupling to the magnetosphere-ionosphere-atmosphere system. The superposed epoch (SPE) analyses of solar wind variables have shown a tendency of severe weather to follow arrivals of high-speed streams from solar coronal holes (Prikryl et al., 2018). Precipitation datasets based on rain-gauge and satellite sensor measurements are used to examine the relationship between the solar wind high-speed streams and daily precipitation rates over several mid-latitude regions. The SPE analysis results show an increase in occurrence of high precipitation rates following arrivals of high-speed streams, including recurrence with a periodicity of 27 days. The cross-correlation analysis applied to the SPE averages of the green (Fe XIV, 530.3 nm) corona intensity observed by ground-based coronagraphs, solar wind parameters and daily precipitation rates show correlation peaks at lags spaced by solar rotation period. When the SPE analysis is limited to years around the solar minimum (2008–2009), which was dominated by recurrent coronal holes separated by ~120˚ in heliographic longitude, significant cross-correlation peaks are found at lags spaced by 9 days. These results are further demonstrated by cases of heavy rainfall, floods and flash floods in Europe, Japan, and the U.S., highlighting the role of solar wind coupling to the magnetosphere-ionosphere-atmosphere system in severe weather, mediated by aurorally excited atmospheric gravity waves.
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 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 | 0.001 |
| 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".