Estimating precipitation phase using a psychrometric energy balance method
Bibliographic record
Abstract
Abstract Precipitation phase is fundamental to a catchment's hydrological response to precipitation events. Phase is particularly variable over time and space in the Canadian Rockies where snowfall or rainfall can occur any month of the year. Phase is controlled by the microphysics of the falling hydrometeor, but microphysical calculations require detailed atmospheric information that is often lacking for hydrological analyses. In hydrology, there have been many methods developed to estimate phase, but most are regionally calibrated, and many depend on air temperature (Ta) and use daily time steps. Phase is not only related to Ta, but to other meteorological variables, and precipitation events are temporally dynamic, adding uncertainty to the use of daily indices to estimate phase. To better predict precipitation phase, the psychrometric energy balance of a falling hydrometeor was calculated and used to develop a method to estimate precipitation phase. High quality precipitation phase and meteorological data were observed at multiple elevations in a small Canadian Rockies catchment, Marmot Creek Research Basin, at 15‐min intervals over several years to develop and test the method. The results of the psychrometric energy balance method were compared to phase observations, to other methods over varying time scales and seasons and at varying elevations and topographic exposures. The results indicate that the psychrometric energy balance method performs much better than Ta index methods and that this improvement, and the accuracy of the psychrometric energy balance method, increases as the time step of calculation decreases. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".