Environmental Conditions Controlling the Morphology of Shallow Orographic Convection
Bibliographic record
Abstract
Abstract Quasi-stationary rainbands capable of producing heavy localized precipitation have been observed over the Oregon Coastal Range and other mesoscale mountain ridges. These bands thus present an important forecasting problem, which is challenged by the inability of operational NWP models to accurately resolve them. To aid the prediction of these events, this study synthesizes an observational climatology with idealized large-eddy simulations of shallow convection over the Coastal Range. The climatology identified cases with banded and cellular morphologies over the Coastal Range and determined composite upstream soundings for each morphology. While prominent differences in these soundings included stronger low-level winds and dry static stability in the banded events, these differences alone did not fully determine the resulting cloud morphology in the simulations. Another key factor was the turbulence intensity in the impinging atmospheric boundary layer (ABL), which is partially controlled by the sea–air temperature difference over the eastern Pacific Ocean (ΔTSA). While banded events mostly exhibit ΔTSA < 0 and weak ABL turbulence, cellular events mostly exhibit the opposite. ABL turbulence was thus hypothesized to favor cells by disrupting the lee-wave circulations responsible for organizing the bands. A new parameter R was developed to predict cloud morphology based on the ratio of the TKE of transient turbulent velocity fluctuations to that of stationary lee-wave perturbations. This parameter accurately predicted the cloud morphology in numerous simulations with varying upstream flows and ΔTSA. It also provides a simple explanation for why the observed characteristics of banded events (stronger low-level winds and ABL stabilities, ΔTSA < 0) all favor band development. Significance Statement This study synthesizes observations and numerical simulations to determine the environmental conditions distinguishing quasi-stationary banded convection from transient cellular convection over the Oregon Coastal Range. The former morphology can concentrate heavy precipitation over narrow regions to greatly enhance flash-flooding risks. The results provide quantitative guidance for forecasters to aid their analysis of operational NWP forecasts.
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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 | 0.000 | 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 | 0.000 |
| 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".