The Challenges of Micro-Nowcasting and the Women’s Slope Style Event at the PyeongChang 2018 Olympic Winter Games
Bibliographic record
Abstract
The Women’s Slope Style event of 11–12 February 2018 at the PyeongChang 2018 Olympic Winter Games posed considerable challenges to the competitors and decision-makers, requiring sub-kilometer and sub-minute weather predictions in complex terrain. The gusty wind conditions were unfair and unsafe as the competitors could not achieve sufficient speed to initiate or complete their jumps. The term micro-nowcasting is used here to reflect the extreme high-resolution nature of these science and service requirements. The World Meteorological Organization has conducted several research development and forecast demonstration projects to advance, accelerate and promote the art of nowcasting. Data from compact automatic weather stations, located along the field of play, reported every minute and were post-processed using time series, Hovmöller and wavelet transforms to succinctly present the information. The analyses revealed dominant frequencies of about 20 min, presumed to be associated with vortex shedding from the mountain ridges, but were unable to directly capture the gusts that affected the competitors. The systemic challenges from this and previous projects are reviewed. They include the lack of adequate scientific knowledge of microscale processes, gaps in modeling, the need for post-processing, forecast techniques, managing ever-changing service requirements and highlights the role of observations and the critical role of the forecaster. These challenges also apply to future high-resolution operational weather and warning services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".