WRF Simulation, Model Sensitivity, and Analysis of the December 2013 New\n England Ice Storm
Bibliographic record
Abstract
Ice storms pose significant damage risk to electric utility infrastructure.\nIn an attempt to improve storm response and minimize costs, energy companies\nhave supported the development of ice accretion forecasting techniques\nutilizing meteorological output from numerical weather prediction (NWP) models.\nThe majority of scientific literature in this area focuses on the application\nof NWP models, such as the Weather Research and Forecasting (WRF) model, to ice\nstorm case studies, but such analyses tend to provide little verification of\noutput fidelity prior to use. This study evaluates the performance of WRF in\ndepicting the 21-23 December 2013 New England ice storm at the surface and in\nvertical profile. A series of sensitivity tests are run using eight planetary\nboundary layer (PBL) physics parameterizations, three reanalysis datasets, two\nvertical level configurations, and with and without grid nudging. Simulated\nvalues of precipitation, temperature, wind speed, and wind direction are\nvalidated against surface and radiosonde observations at several station\nlocations across northeastern U.S. and southeastern Canada. The results show\nthat, while the spatially and temporally averaged statistics for near-surface\nvariables are consistent with those of select ice-storm case studies,\nnear-surface variables are highly sensitive to model when examined at the\nstation level. No single model configuration produces the most robust solution\nfor all variables or station locations, although one scheme generally yields\nmodel output with the least realism. In all, we find that careful model\nsensitivity testing and extensive validation are necessary components for\nminimizing model-based biases in simulations of ice storms.\n
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".