Quality Assessment of Meteorological Data for the Beaufort and Chukchi Sea Coastal Region using Automated Routines
Bibliographic record
Abstract
Meteorological observations from more than 250 stations in the Beaufort and Chukchi Sea coastal, interior, and offshore regions were gathered and quality-controlled for the period 1979 through 2009. These stations represent many different observing networks that operate in the region for the purposes of aviation, fire weather, coastal weather, climate, surface radiation, and hydrology and report data hourly or sub-hourly. A unified data quality control (QC) has been applied to these multi-resource data, incorporating three main QC procedures: the threshold test (identifying instances of an observation falling outside of a normal range); the step change test (identifying consecutive values that are excessively different); and the persistence test (flagging instances of excessively high or low variability in the observations). Methods previously developed for daily data QC do not work well for hourly data because they flag too many data entries. Improvements were developed to obtain the proper limits for hourly data QC. These QC procedures are able to identify the suspect data while producing far fewer Type I errors (the erroneous flagging of valid data). The fraction of flagged data for the entire database illustrates that the persistence test was failed the most often (1.34%), followed by the threshold (0.99%) and step change tests (0.02%). Comparisons based on neighboring stations were not performed for the database; however, correlations between nearby stations show promise, indicating that this type of check may be a viable option in such cases. This integrated high temporal resolution dataset will be invaluable for weather and climate analysis, as well as regional modeling applications, in an area that is undergoing significant climatic change.
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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 | 0.000 |
| 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".