Understanding Microphysical Processes Occurring in Lake-Effect Snowbands Using Quasi-Vertical Profiles of WSR-88D Parameters
Bibliographic record
Abstract
Knowing the composition and microphysical processes that occur in lake-effect precipitation systems is important in making sure models capture these processes accurately, and could aid forecasters in predicting snow totals and snow water equivalents. Many studies have shown that processes such as riming and dendritic growth can be seen in dual-polarization radar data through parameters such as equivalent radar reflectivity factor, differential reflectivity (ZDR), correlation coefficient, and differential phase. This research examines WSR-88D data from the KTYX (Montague, NY) radar located on the Tug Hill Plateau for several long-lake-axis-parallel lake-effect systems from the Ontario Winter Lake-effect Systems (OWLeS) field campaign which took place in the winter of 2013-2014, and more recent cases from the winter of 2017-2018. Quasi-vertical profiles (QVPs), in which the radar parameters are azimuthally averaged at a fixed elevation angle and time, were generated. The spatial and temporal variations of reflectivity and the dual-polarization parameters seen in the QVPs were compared with other datasets including surface observations, vertically-pointing micro-rain radars (MRRs), and aircraft data. QVP results show variations in dual-polarization parameters which, along with other datasets, can be used to characterize processes occurring within the bands such as riming and aggregation. Multiple cases show transitions from higher ZDR aloft to near 0 ZDR at the surface. The higher elevation angle QVPs agree well with the MRR data, which shows that QVPs can be applied in lake-effect events, as long as the band is directly over the radar.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".