Diagnosing Primary Condensation Rate Attributed to the Moisture Convergence: Applications to Atmospheric River Analysis and Extratropical Storm Classification
Bibliographic record
Abstract
Abnormally heavy precipitation events can lead to numerous hazards, including flooding, landslides, and avalanches. Their developments require a sufficient supply of moisture and some physical mechanism to produce condensation. Atmospheric rivers (ARs) defined as long and narrow corridors of strong horizontal moisture transport can provide such necessary conditions. The presence and strength of ARs are often described using the integrated water vapor (IWV) and the integrated vapor transport (IVT). However, the associated precipitation is not directly correlated with these two variables. It is the net convergence of moisture that determines the intensity of precipitation. The purpose of this study is to illustrate, in the context of AR analysis, how the converged vapor should be distributed between condensation and air moistening. A simple algorithm is proposed for estimating the heavy precipitation attributable to the IVT convergence. Bearing a strong resemblance to the Kuo-Anthes parameterization scheme for cumulus convection, the proposed algorithm calculates the large-scale primary condensation rate (PCR) as a proportion of the IVT convergence, with a reduction to account for the general moistening in the atmosphere. The amount of reduction is determined by the column relative humidity (CRH), which is defined as the ratio of IWV to its saturation counterpart. It is found that the PCR in an air column with CRH < 0.60 is negligibly small. Based on a one-year dataset from the Canadian global numerical weather prediction (NWP) model, the best cut-off value of CRH for the algorithm is 0.66. It is demonstrated that this diagnosable PCR compares well to the forecast precipitation rate given by the NWP model. Case studies are conducted to illustrate the usefulness of CRH and PCR as two complements to standard AR analysis and impact-based storm classification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".