Improving Predictions of Precipitation Phase over Canada with the Development of a Spatially Aware Parameterization of Rain/Snow Partitioning
Bibliographic record
Abstract
Partitioning precipitation into rain or snow is an important aspect of hydrologic and \nclimatological modelling, affecting a wide variety of downstream processes (Harpold et al., \n2017). Current conventional methods typically rely on surface temperature, either as a \nstrict threshold or to inform a probability function which predicts a 0% chance of rain \nat one temperature to 100% chance of rain at another (Jennings et al., 2019, Feiccabrino \net al., 2015). However, recent studies have shown that variables such as wind, pressure, \nhumidity, and atmospheric profiles of temperature can all have a significant effect on pre- \ncipitation phase at the surface (Wang et al., 2019, Sims and Liu, 2015, Jennings et al., \n2018). This study utilized CloudSat data of precipitation phase and associated environ- \nmental variables ground-truthed at ECCC stations across Canada to build an improved \nstatistical parameterization of precipitation phase. Our results showed that using a ran- \ndom forest model with atmospheric profiles of wetbulb temperature in addition to surface \nwetbulb temperature, elevation, and wind resulted in a probability of detection of 97.8% \nacross the -1, 4°C temperature interval, compared to a probability of detection of below \n80% across the same interval for conventional methods. The random forest parameteri- \nzation was also spatially robust, performing well on stations it had not been trained on. \nAdditionally, adding Sturm’s snow classes as an indicator variable to the model did not \nresult in any significant improvement, indicating that a model trained on all available data \nis adequately able to capture spatial variability in rain-snow partitioning across the study \narea.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".