El Niño–Southern Oscillation influence on winter maximum daily precipitation in California in a spatial model
Bibliographic record
Abstract
Recent studies have found that the El Niño–Southern Oscillation (ENSO) has statistically significant influences on extreme precipitation. A limitation of most existing work is that a separate generalized extreme value (GEV) distribution is fitted for each individual site. Such models cannot address important questions that involve events jointly defined across multiple sites; for instance, what is the probability that the 50 year return levels of three sites in the vicinity of a city occur in the same season? With the latest statistical methodology for spatial extremes, we fit max‐stable process models to winter maximum daily precipitation of 192 sites in California over 55 years. A composite likelihood approach is used since the full likelihood is unavailable either analytically or numerically. In addition to latitude, longitude, and elevation, the Southern Oscillation Index (SOI) is incorporated into the parameters of the marginal GEV models. We find that, in a spatial context, the ENSO has a significant influence on the extreme precipitation in California by shifting the location parameter of the GEV distributions, with higher values of the SOI corresponding to lower maximum winter daily precipitation. The joint spatial model is used to assess risks concerning joint extremal events at network of sites with spatial dependence properly accounted for. The probability of extremal events occurring at multiple sites in the same season is found to be much higher than what would be expected under the independence assumption.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".