Socio‐Economic Risk of Rising Compound Precipitation‐Wind Extremes in San Francisco Bay Area
Bibliographic record
Abstract
ABSTRACT Compound precipitation‐wind extremes (CPWEs) pose significant socio‐economic challenges in urban areas, with the most severe impacts often felt by vulnerable populations. While CPWE studies have often focused on large‐scale assessments, localised urban contexts, at a scale of a city and its suburban surroundings, remain understudied, particularly in terms of how CPWEs might differentially impact different socio‐economic communities. This study aims to advance understanding of how CPWEs interact with social vulnerability at a localised scale, using the San Francisco Bay Area (SFBA)—a global hotspot for CPWEs with diverse climates, physiography, and socio‐economic factors—as a test case. Daily precipitation and wind speed data (~4 km) from the gridMET dataset (1982–2022) were used to identify CPWEs during the wet season (October–April). Patterns of CPWE event frequency and trends were then analysed. We found that the return period for CPWEs in the SFBA ranges from 0.5 to 2 years, suggesting nearly annual occurrences. Over recent decades, these events have become more frequent (an increase of two events per decade) and more intense, particularly in terms of daily precipitation trends (an increase of 0–5 mm/decade), across the SFBA. We further examined the connection between CPWEs and social vulnerability using the Social Vulnerability Index from the Centers for Disease Control and Prevention, considering factors such as socio‐economic status, household characteristics, racial and ethnic composition, housing conditions, and access to transportation. Key urban centres, such as San José and Oakland, emerged as hotspots where high CPWE frequency coincides with high levels of social vulnerability due to economic constraints, inadequate housing, and limited transportation access. These findings underscore the need for targeted adaptation measures to protect populations most at risk in the SFBA.
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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 | 0.001 | 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".