Characterization of orographic cloud and precipitation features over southern Baffin Island and surrounding area
Bibliographic record
Abstract
Improved characterization of cloud and precipitation features are required to understand the impact of a changing climate in high latitude regions and accurately represent these features in models. The importance of cold season precipitation to regional moisture cycling and our limited understanding of orographic cloud and precipitation processes in the Arctic provide the motivation for this research. Using high-resolution datasets collected during the Storm Studies in the Arctic (STAR) field project this thesis examines cloud and precipitation features over southern Baffin Island in Nunavut. Cloud and precipitation features were shown to differ over orography compared to the adjacent ocean regions upstream. Gravity waves, terrain shape, atmospheric stability and atmosphere-ocean exchanges were all associated with precipitation enhancement. In addition, high sea ice extent, low-level blocking in the upstream environment and sublimation were factors that reduced precipitation. The nature of hydrometeors was variable and accretion and aggregation were found to be important determinants of whether precipitation reached the ground. The processes controlling a snowfall event over southern Baffin Island were found to be complex, representing a significant challenge for modelling in the region. Low-level convection over adjacent ocean regions, strong upslope flow over the terrain, and the passing of a weak trough collectively produced the event. Analysis of the Global Environmental Multi-scale limited area model (GEM-LAM 2.5) revealed that upstream convection and upslope processes were affected by model errors. Consequently, precipitation onset was delayed and total modelled accumulation was 50% less than observations. Further evaluation of a numerical weather prediction model during STAR cases provided descriptions of model errors and proficiencies for different synoptic forcing and surface environments. Overall the model overestimated temperature and had difficulties representing thermal inversions over sea ice. The model generally over-predicted moisture with the exception of profiles over sea ice and land. Wind speed was frequently underestimated, weakening upslope processes and errors in wind direction were large at times. Cloud-tops were usually too high and cloud-bases too low. Where multiple cloud layers were present, the dry layer depth was inaccurate. Model errors were shown to have implications for cloud and precipitation production and their forecast.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".