Results of the Tokyo Metropolitan Area Convection Study for Extreme Weather Resilient Cities (TOMACS)
Bibliographic record
Abstract
Abstract The Tokyo Metropolitan Area Convection Study for Extreme Weather Resilient Cities (TOMACS) began as a Japanese domestic research project in 2010 and aimed to elucidate the mechanisms behind local high-impact weather (LHIW) in urban areas, to improve forecasting techniques for LHIW, and to provide high-resolution weather information to end-users (local governments, private companies, and the general public) through social experiments. Since 2013, the project has been expanded as an international Research and Development Project (RDP) of the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO). Through this project, the following results were obtained: 1) observation data for LHIW around Tokyo were recorded using a dense network of X-band radars, a C-band polarimetric radar, a Ku-band fast-scanning radar, coherent Doppler lidars, and the Global Navigation Satellite System; 2) quantitative precipitation estimation algorithms for X-band polarimetric radars have been developed as part of an international collaboration; 3) convection initiation by the interaction of sea breezes and urban impacts on the occurrence of heavy precipitation around Tokyo were elucidated by a dense observation network, high-resolution numerical simulations, and different urban surface models; 4) an “imminent” nowcast system based on the vertically integrated liquid water derived from the X-band polarimetric radar network has been developed; 5) assimilation methods for data from advanced observation instruments such as coherent Doppler lidars and polarimetric radars were developed; and 6) public use of high-resolution radar data were promoted through the social experiments.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".