A Diagnostic Framework for Understanding Climatology of Tails of Hourly Precipitation Extremes in the United States
Bibliographic record
Abstract
Abstract Hourly precipitation extremes are crucial in hydrological design. Their frequency and magnitude is encapsulated in the probability distribution tail. Traditional extreme‐analysis methods rely on theorems, like the Pickands‐Balkema‐de Haan, indicating specific type of tails assuming asymptotic convergence—a questionable assumption for real‐world samples. Moreover, popular stochastic models for hourly precipitation presume light‐tailed distributions to facilitate their mathematical formulation. In practice, limited information on hourly precipitation extremes makes identifying and quantifying their tail highly uncertain, especially on a station‐by‐station basis. Yet no comprehensive regional analysis of tails has been undertaken to quantify a climatology of tails for diagnostic and prognostic purposes. Here we undertake such an analysis for the conterminous United States. We introduce a novel Bayesian‐adjustment approach to assess the best model between power‐type and stretched‐exponential tails showing that the latter performs better. We present climatology of the tail and quantify its heaviness in over 4,000 hourly precipitation records across the United States and present three main conclusions. First, we show that hourly precipitation tails are heavier than those commonly used with important implications including underestimation of extremes. Second, we provide spatial maps of the tail behavior which reveal some strikingly coherent spatial patterns that can be used for inference in the absence of local observations. Third, we find a nonlinear increase in the tail heaviness with elevation and we formulate parametric functions to describe this law . These results can improve the accuracy of frequency analysis, probabilistic prediction, rainfall‐runoff modeling, and downscaling of historical observations and climate model projections.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".