A deep learning approach for the identification of the synoptic-scale drivers of long-duration mixed precipitation in Montréal (Canada)
Bibliographic record
Abstract
Climate change is altering the Earth’s atmospheric circulation and the dynamic drivers of extreme events. Extreme weather events pose a great potential risk to infrastructure and human security. In Montréal (Québec, Canada) long-duration mixed precipitation events (freezing rain and/or ice pellets) are high-impact cold-season hazards and an understanding of how climate change alters their occurrence is of high societal interest. Here, we introduce a two-staged deep learning approach that uses the synoptic-scale drivers of mixed precipitation to identify these extreme events in archived climate model data. The approach is destined for the application on regional climate model (RCM) data over the Montréal area. The dominant dynamic mechanism leading to mixed precipitation in Montréal is pressure-driven channeling of winds along the St. Lawrence river valley. The identification of the synoptic-scale pressure pattern related to pressure-driven channeling is a visual image classification task that is addressed with supervised machine learning. A convolutional neural network (CNN) is trained on the classification of the synoptic-scale pressure patterns by using a large training database derived from an ensemble of the Canadian Regional Climate Model version 5 (CRCM5). The CRCM5 is to our knowledge the only RCM available so far that employs the diagnostic method by Bourgouin to simulate mixed precipitation inline and thus delivers training examples and labels for this supervised classification task. The CNN correctly identifies 90 % of the Bourgouin mixed precipitation cases in the test set. The weak point of the approach is a high type I error, which is enhanced in a second stage by applying a temperature condition. The evaluation on an CRCM5 run driven by ERA-Interim reanalysis reveals a still low precision of 21 % and thus a Matthews correlation coefficient of 0.39. The deep learning approach can be applied to ensembles of regional climate models on the North America 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".