A Deep Learning Approach for the Identification of Long-Duration Mixed Precipitation in Montréal (Canada)
Bibliographic record
Abstract
Long-duration mixed-precipitation events (freezing rain and/or ice pellets) are important cold-season hazards and understanding how climate change alters their occurrence is of high societal interest, particularly in urban areas. This study introduces a two-staged approach that employs deep learning to identify long-duration mixed precipitation over the Montréal area (Quebec, Canada) in archived climate model data using large-scale pressure patterns. The dominant dynamic mechanism leading to mixed precipitation in Montréal is pressure-driven channelling of winds along the St. Lawrence River Valley. A convolutional neural network (CNN) identifies the corresponding synoptic pattern by using a large training database derived from an ensemble of the Canadian Regional Climate Model, version 5 (CRCM5). The CRCM5 uses the diagnostic method of Bourgouin (2000) to simulate mixed precipitation and delivers training examples and corresponding class affiliations (labels) for this supervised classification task. The CNN correctly identifies more than 80% of the Bourgouin mixed-precipitation cases. In the next stage, the CNN is combined with temperature and precipitation conditions, which consider important preconditions for mixed precipitation and improve the performance of the approach. The evaluation of a CRCM5 run driven by ERA-Interim reanalysis data gives a Matthews correlation coefficient of 0.50. The deep learning approach can be applied to ensembles of regional climate models on the North American grid of the Coordinated Regional Downscaling Experiment (CORDEX-NA).
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".