WRF model fields for TITAN tracking of simulated storms, Morrison microphysics scheme
Bibliographic record
Abstract
This dataset contains reflectivity and maximum hail diameter fields extracted from WRF v4.0.1 simulations, run using HAILCAST and the P3 microphysics scheme. The data is subset to the specified region of interest over Switzerland. The file 'coordinates.nc' contains the latitude/longitude coordinates for each included point, as well as global WRF attributes for the model run. Full model run details are listed in a manuscript in the journal Geoscientific Model Development (https://gmd.copernicus.org/articles/14/6495/2021/gmd-14-6495-2021.html). <strong>Author contributions:</strong> <br> AM configured and ran the model with input from THR, THR wrote the corresponding manuscript, OM oversaw the project. <strong>References:</strong> <strong>WRF:</strong> <br> Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D., and yu Huang, X.: A Description of the Advanced Research WRF Version 4, Tech. rep., National Center for Atmospheric Research, Boulder, Colorado, USA, https://doi.org/10.5065/1dfh-6p97, NCAR technical note NCAR/TN-556+STR, 2019. <strong>HAILCAST: </strong><br> Adams-Selin, R. D. and Ziegler, C. L.: Forecasting Hail Using a One-Dimensional Hail Growth Model within WRF, Mon. Weather Rev., 144, 4919–4939, https://doi.org/10.1175/MWR-D-16-0027.1, 2016.<br> Brimelow, J. C., Reuter, G. W., and Poolman, E. R.: Modeling Maximum Hail Size in Alberta Thunderstorms, Weather Forecast., 17, 1048–1062, https://doi.org/10.1175/1520-0434(2002)017<1048:MMHSIA>2.0.CO;2, 2002. <strong>Morrison microphysics scheme: </strong><br> Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.140 | 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".