Observed and Simulated Precipitation over Northeastern North America: How Do Daily and Subdaily Extremes Scale in Space and Time?
Bibliographic record
Abstract
Abstract The characterization of extreme precipitation at fine spatiotemporal scale represents a paramount challenge in hydroclimate sciences due to large uncertainties affecting the precipitation estimation from existing datasets. Comparing the spatiotemporal structure of precipitation extremes estimated from different datasets thus represents an essential step for climate model evaluation, as it provides insight into a model’s ability to simulate atmospheric processes occurring at different scales. This study compares the probability distributions and the annual and diurnal cycles of occurrence of daily and subdaily precipitation annual maxima (AM) estimated over northeastern North America from five observed and simulated datasets: meteorological station series, the bias-corrected (CRT) satellite CMORPH, version 1.0, and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), version 2, gridded datasets, various Canadian RCM, version 5 (CRCM5), simulations, and a 13-yr convection-permitting WRF, version 3.4.1, simulation. ERA-Interim-driven CRCM5 and WRF simulations well reproduced subdaily extreme quantiles and the AM annual and diurnal cycles observed at stations, while CMORPH and MSWEP displayed good performance only for daily and longer extreme statistics. The spatiotemporal statistical structure of precipitation extremes is then assessed considering the variation of AM quantiles across various spatial scales and durations. The results suggest that a two-parameter analytical relationship well describes the AM spatiotemporal structure at the regional scale, allowing us to approximate some crucial properties of point precipitation extremes from gridded datasets. Averaging the estimates from various members of the initial-condition CRCM5 Large Ensemble (CRCM5-LE) also made it possible to reduce the sampling errors and robustly estimate the AM spatiotemporal structure at the local scale of each model grid box.
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 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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".