BAND FORECASTS IN THE NORTHEAST REGIONAL ENSEMBLE
Bibliographic record
Abstract
AbstractIn this study, simulations from the northeast regional Weather Research and Forecasting (WRF) model ensemble of two lake-effect snow events from the 2007-2008 cool season are examined. In these simulations of lake-parallel, lake-effect snow bands downwind of Lake Ontario, a systematic southward bias in forecast snow band location is found with the Advanced Research WRF (WRF-ARW) members of the ensemble, consistent with previous research on mesoscale modeling of lake-effect snow and with qualitative forecaster assessments of other events during the 2007-2008 cool season. The bias is found to degrade the usefulness of the northeast regional ensemble. A series of sensitivity simulations is performed to help diagnose the cause of the southward bias. These simulations revealed that the WRF-ARW ensemble members underestimated the frictional slowing of wind, downwind of the central and eastern Great Lakes, when compared to a Rapid Update Cycle (RUC) analysis and WRF-Nonhydrostatic Mesoscale Model (WRF-NMM) simulations. The underestimation served to reposition the mesoscale convergence boundaries associated with the lake-effect snow bands farther south. These simulations also clearly indicate that model core rather than model physics and physical parameterizations is the primary source of the erroneous boundary layer flow. A final set of sensitivity simulations suggest that version 3 of the WRF-ARW improves upon this bias with forecast accuracy more comparable to the WRF-NMM shown in one case study. Given the results from this study, the feasibility of an operational mesoscale ensemble is discussed. It is shown that while this type of forecast tool shows promise, performing
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".