The Utility of Additional Soundings for Forecasting Lake-Effect Snow in the Great Lakes Region
Bibliographic record
Abstract
The impact of initializing a mesoscale model with additional sounding data over the Great Lakes region is investigated. As part of the Lake-Induced Convection Experiment (Lake-ICE) field study during the winter of 1997/98, six supplementary Cross-chain Loran Atmospheric Sounding System (CLASS) units and three Integrated Sounding System (ISS) units were used in addition to those from the standard synoptic upper-air network. The three ISS units were in the vicinity of Lake Michigan, and the six CLASS units were in the data-sparse region of central and northeastern Ontario and western Quebec. The Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model running on a doubly nested grid is used to simulate the lake-effect snow event of 4–5 December 1997. This model output from a 30-km horizontal resolution grid shows that the six CLASS soundings capture a warm layer below 850 hPa that appears to be the result of diabatic heating from the Great Lakes. This leads to an improved simulation of the surface pressure fields over the course of the simulation. A nested 10-km horizontal resolution grid shows that the initialization data from the CLASS sites seemed to have a greater influence on the propagation of a mesoalpha-scale trough that caused significant snowfall to the lee of Lake Michigan than data from the ISS sites. The inclusion of the CLASS sounding data changes the track of the precipitation maximum by approximately 25 km and agrees better with reflectivity data from the Weather Surveillance Radar-1988 Doppler. Implications for forecasters in the Great Lakes region are discussed.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".