Bibliographic record
Abstract
CLAMPS observations from both CLAMPS1 and CLAMPS2 during PBL Tops: Evaluating Polarimetric Retrievals of Boundary Layer Height Using State-of-the-Art Boundary Layer Profiling, a project supported by OU/CIWRO's Director's Discretionary Funds. Details about the instruments onboard CLAMPS can be found on the NSSL CLAMPS webpage. These datasets include boundary-layer profiles of wind, temperature, and humidity collected from the CLAMPS systems. These data can be accessed on the NOAA National Severe Storms THREDDS Archive. The readme contained here provides information about how to access and available resources for users. Observations cover a 4-week observation period, split into two 2-week blocks. During block 1, each CLAMPS will deploy near KTLX (Norman, OK) at a radar-adjacent site (0-5 km) and a radar-distant site at Kessler Atmospheric and Ecologial Field Station (10-20 km), respectively. During block 2, the radar-distant CLAMPS will reposition to a radar-adjacent site at KSHV (Shreveport, LA). The observational approach providess one continuous 4-week time series within 5 km of KTLX. By moving the second CLAMPS platform between the two blocks (from Kessler Station to KSHV), the data can be used to evaluate differences due to distance from radar and the local climatology or land-use characteristics, which may modify expected boundary-layer evolution. Owing to the sparse distribution of atmospheric profilers, numerous studies have sought to use radar detection of Bragg scattering to observe the evolution of planetary boundary layer (BL) height, including efforts using the operational NWS WSR-88D network by Banghoff et al. (2018; hereafter B18) using. The B18 method uses quasi-vertical profiles obtained from azimuthal averaging (Ryzhkov et al. 2016) and offers the promise of national, real-time estimates of BL height. However, the B18 method was only initally validated using twice-daily radiosonde observations, thus obscuring its efficacy for retrieving BL height estimates during periods of evolution and complex structure (e.g., morning/evening transitions, residual layers, etc.). The observations in this dataset were collected in an effort to (1) validate the polarimetric B18 method using specialized independent measurements from both CLAMPS; (2) extend validation times to include important BL evolution periods beyond synoptic sampling times and identify failure periods and regimes; (3) contextualize and interpret signatures observed in B18 using novel near-continuous data (e.g., overnight signatures); and (4) quantify the skill of the B18 method. Banghoff, J.R., D.J. Stensrud, and M.R. Kumjian, 2018: Convective Boundary Layer Depth Estimation from S-Band Dual-Polarization Radar. J. Atmos. Oceanic Technol., 35, 1723–1733. Ryzhkov, A., P. Zhang, H. Reeves, M. Kumjian, T. Tschallener, S. Trömel, and C. Simmer, 2016: Quasi-Vertical Profiles—A New Way to Look at Polarimetric Radar Data. J. Atmos. Oceanic Technol., 33, 551–562.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.032 |
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; both teacher heads agree on what is shown here.
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".