The Conversion of Total Column Ozone Data to Numerical Weather Prediction Model Initializing Fields, with Simulations of the 24–25 January 2000 East Coast Snowstorm
Bibliographic record
Abstract
Abstract Satellites are uniquely capable of providing uniform data coverage globally. Motivated by such capability, this study builds on a previously described methodology that generates numerical weather prediction (NWP) model initial conditions (ICs) from satellite total column ozone (TCO) data. The methodology is based on three principal steps: 1) conversion of TCO to mean potential vorticity (MPV) via linear regression, 2) conversion of two-dimensional MPV to three-dimensional potential vorticity (PV) via vertical mapping onto average PV profiles, and 3) inversion of the three-dimensional PV field to obtain model-initializing height, temperature, and wind fields in the mid- and upper troposphere. The overall accuracy of the process has been significantly increased through a substantial reworking of the details of this previous version. For instance, in recognition of the fact that TCO ridges tend to be less reliable than troughs, the authors vertically map an MPV field that is a synthesis of ozone-derived MPV troughs and analysis MPV ridges. The vertical mapping procedure itself produces a more physical three-dimensional PV field by eliminating unrealistically strong features at upper levels. It is found that the ozone-influenced upper-level initializing fields improve the quantitative precipitation forecast (QPF) of the 24–25 January 2000 East Coast snowstorm for two of the three (re)analyses. Furthermore, the best QPF involves ozone-influenced upper-level initializing fields. Its high threat scores reflect a superior placement, amplitude, and structure. This best QPF is apparently superior to a forecast of the same case where TCO data were assimilated using four-dimensional variational data assimilation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".