Impacts of Hydrometeor Drift on Orographic Precipitation: Two Case Studies of Landfalling Atmospheric Rivers in British Columbia, Canada
Bibliographic record
Abstract
Abstract Two severe winter storms in 2016 and 2017 caused by landfalling atmospheric rivers over British Columbia (BC) are investigated in this study. Our main concern is the impact of hydrometeor drift on the orographic precipitation. It is shown that the dominant contribution to the windward orographic precipitation was from the horizontal moisture convergence. The precipitation distributions across southern BC were also influenced by the convergence/divergence of condensed water due to the wind-driven effect on hydrometeors. Observed hourly and daily precipitation amounts are used to verify the performances of three Canadian numerical weather prediction systems. Our results indicate that these operational systems were capable of predicting the general features of orographic precipitation in BC. However, the two coarse-resolution systems used a diagnostic precipitation scheme that does not fully simulate the hydrometeor drift process. The High-Resolution Deterministic Prediction System (HRDPS) with a prognostic precipitation scheme was substantially more accurate and skillful in predicting the upwind precipitation as well as the spillover of precipitation on the leeward slopes for these two storms. There was evidence suggesting that the spillover effect was overpredicted by the HRDPS due to a systematic bias originating in the model microphysics. This problem has been improved in the current HRDPS with a new microphysics scheme. Based on our atmospheric water balance analysis, we also proposed two postprocessing schemes that could be applied to improve the quantitative precipitation forecasts of the diagnostic precipitation schemes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".