Synoptic versus orographic control on stationary convective banding
Bibliographic record
Abstract
Quasi‐stationary convective bands can cause large localised rainfall accumulations and are often anchored by topographic features. Here, the predictability of and mechanisms causing one such band are determined using ensembles of the Met Office Unified Model at convection‐permitting resolution (1.5 km grid length). The band was stationary over the UK for 3 h and produced rainfall accumulations of up to 34 mm. The amount and location of the predicted rainfall was highly variable despite only small differences between the large‐scale conditions of the ensemble members. Only three of 21 members of the control ensemble produced a stationary rain band; these three had the weakest upstream winds and hence lowest Froude number. Band formation was due to the superposition of two processes: lee‐side convergence resulting from flow around an upstream obstacle and thermally forced convergence resulting from elevated heating over the upstream terrain. Both mechanisms were enhanced when the Froude number was lower. By increasing the terrain height (thus reducing the Froude number), the band became more predictable. An ensemble approach is required to successfully predict the possible occurrence of such quasi‐stationary convective events because the rainfall variability is largely modulated by small variations of the large‐scale flow. However, high‐resolution models are required to accurately resolve the small‐scale interactions of the flow with the topography upon which the band formation depends. Thus, although topography provides some predictability, the quasi‐stationary convective bands anchored by it are likely to remain a forecasting challenge for many years to come.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".