Precipitable water characteristics during the 2013 Colorado flood using ground-based GPS measurements
Bibliographic record
Abstract
Abstract. During 9–16 September 2013, the Front Range region of Colorado experienced heavy rainfall that resulted in severe flooding. Precipitation totals for the event exceeded 450 mm, damages to public and private properties were estimated to be over USD 2 billion, and nine lives were lost. This study analyzes the characteristics of precipitable water (PW) surrounding the event using 10 years of high-resolution GPS PW data in Boulder, Colorado, which was located within the region of maximum rainfall. PW in Boulder is dominated by seasonal variability with an average summertime maximum of 36 mm. In 2013, the seasonal PW maximum extended into early September and the September monthly mean PW exceeded the 99th percentile of climatology with a value 25 % higher than the 40-year climatology. Prior to the flood, around 18:00 UTC on 8 September, PW rapidly increased from 22 to 32 mm and remained around 30 mm for the entire event as a result of the nearly saturated atmosphere. The frequency distribution of September PW for Boulder is typically normal, but in 2013 the distribution was bimodal due to a combination of above-average PW values from 1 to 15 September and much drier conditions from 16 to 30 September. The above-normal, near-saturation PW values during the flood were the result of large-scale moisture transport into Colorado from the Tropical Eastern Pacific and the Gulf of Mexico. This moisture transport was the product of a stagnating cutoff low over the southwestern United States working in conjunction with an anticyclone located over the southeastern United States. A blocking ridge located over the Canadian Rocky Mountains kept both of the synoptic features in place over the course of several days, which helped to provide continuous moisture to the storm, thus enhancing the accumulated precipitation totals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".