Identifying the microphysical sensitivities of mesoscale and synoptic precipitation using an ensemble framework
Bibliographic record
Abstract
Through ensemble sensitivity analysis, this dissertation aims to identify the amount of forecast uncertainty that stems from the representation of mixed-phase cloud microphysics within the Weather Research and Forecasting Model (WRF). The first research thrust focuses on how the evolution of ice crystal shape and choice of ice nucleation parameterization in the Adaptive Habit Microphysics Model (AHM) influences the lake-effect storm that occurred during Intensive Operating Period 4 (IOP4) of the Ontario Winter Lake Effect Systems (OWLeS) Field Campaign. This localized snowstorm produced total liquid-equivalent precipitation amounts up to 17.92 mm during a 16-hour time period, providing a natural laboratory to investigate the ice-liquid partitioning within the cloud and various microphysical process rates, as well as the accumulated precipitation magnitude and its associated spatial distribution. Two nucleation parameterizations were implemented, and aerosol data from a size-resolved Advanced Particle Microphysics (APM) model were ingested into the AHM for use in parameterizing ice and cloud condensation nuclei. Simulations allowing ice crystals to grow nonspherically produced 1.6–2.3% greater precipitation while altering the nucleation parameterization changed the type of accumulating hydrometeors. In addition, all simulations were highly sensitive to the domain resolution and the source of initial and boundary conditions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".