Nowcasting Challenges during the Beijing Olympics: Successes, Failures, and Implications for Future Nowcasting Systems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Beijing 2008 Forecast Demonstration Project (B08FDP) included a variety of nowcasting systems from China, Australia, Canada, and the United States. A goal of the B08FDP was to demonstrate state-of-the-art nowcasting systems within a mutual operational setting. The nowcasting systems were a mix of radar echo extrapolation methods, numerical models, techniques that blended numerical model and extrapolation methods, and systems incorporating forecaster input. This paper focuses on the skill of the nowcasting systems to forecast convective storms that threatened or affected the Summer Olympic Games held in Beijing, China. The topography surrounding Beijing provided unique challenges in that it often enhanced the degree and extent of storm initiation, growth, and dissipation, which took place over short time and space scales. The skill levels of the numerical techniques were inconsistent from hour to hour and day to day and it was speculated that without assimilation of real-time radar reflectivity and Doppler velocity fields to support model initialization, particularly for weakly forced convective events, it would be very difficult for models to provide accurate forecasts on the nowcasting time and space scales. Automated blending techniques tended to be no more skillful than extrapolation since they depended heavily on the models to provide storm initiation, growth, and dissipation. However, even with the cited limitations among individual nowcasting systems, the Chinese Olympic forecasters considered the B08FDP human consensus forecasts to be useful. Key to the success of the human forecasts was the development of nowcasting rules predicated on the character of Beijing convective weather realized over the previous two summers. Based on the B08FDP experience, the status of nowcasting convective storms and future directions are presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it