Assessing Consistency across High-Resolution North American Precipitation Datasets
Bibliographic record
Abstract
Abstract In recent decades, there has been a surge in the availability of precipitation datasets, each leveraging diverse data sources and sophisticated algorithms. However, the density of the gauged station network presents a substantial challenge in thoroughly assessing these datasets, particularly in regions with sparse coverage. This study seeks to quantify the observational uncertainty of precipitation across North America by introducing a novel framework. The framework is designed to detect potential outliers, categorize datasets based on their level of agreement, and operate at both monthly and daily time steps. Eleven datasets spanning from 2002 to 2017 are investigated: Australian National University Splines (ANUSPLIN), Canadian Precipitation Analysis (CaPA), Climate Hazards group Infrared Precipitation with Stations (CHIRPS), CMORPH, Daily Surface Weather Data on a 1-km Grid for North America (DAYMET), ERA5, GSMaP, IMERG, Multi-Source Weighted-Ensemble Precipitation (MSWEP), PERSIANN, and PRISM. Analyses at the gridcell scale revealed that on average and across the entire domain, excluding outlier datasets reduced observational uncertainty by ∼17% for the monthly time step and ∼39% for the daily time step. Aggregating results over 24 hydrological entities provided a broader perspective and revealed that certain datasets were consistently classified in the same group. Notably, MSWEP was always in the high-consistency group, while PRISM and CaPA were predominantly in that group. Satellite-based datasets (CHIRPS, CMORPH, GSMaP, and IMERG) were mainly classified in the high-consistency group during summer, reflecting their ability to capture convective precipitation. ERA5 appeared in both the high- and low-consistency groups, depending on the season and region. Although our framework identified sets of consistent datasets and potentially reduces uncertainties, we emphasize that uncertainties remain and encourage the scientific community, to consider these observational uncertainties in their analyses. Significance Statement Numerous precipitation datasets are being developed for long-term environmental studies, but important observational uncertainties arise from the differences among these datasets. In this context, our study presents a framework to quantify and categorize these uncertainties into three groups: high-consistency, low-consistency, and outliers. By identifying consistent datasets and excluding outliers, we offer a method to reduce uncertainties. However, substantial uncertainties remain, underscoring the importance of intercomparing precipitation datasets for all related analyses. We urge the scientific community to incorporate these uncertainties into their work to ensure the reliability and robustness of their findings, ultimately improving the quality of environmental studies.
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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 | 0.001 | 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".