Insights from Nowcasting and Mesoscale Research Working Group Projects of the World Weather Research Programme
Bibliographic record
Abstract
Abstract Insights from Forecast Demonstration Projects and Research Development Projects, training workshops, and symposia, conducted between 2000 and 2024 are summarized. The projects were organized by the Nowcasting and Mesoscale Research Working Group of the World Weather Research Programme of the World Meteorological Organization. The objective was to advance, promote, and build capacity in nowcasting and very short-range forecasting. The projects were associated with the Olympic Games, emergency management, and aviation services. They brought international experts together to work in a collaborative fashion. Extensive interaction with end users and decision-makers expanded and extended the scope of services from traditional weather hazards (heavy rain, wind, hail, lightning) to include specific user needs (e.g., visibility in complex terrain or airport runways, periods of calm winds or light rain, heat stress). Substantial progress has been made in many areas including advanced radar nowcasting algorithms, stochastic nowcasts, kilometric and hectometric numerical weather prediction models, blending of observations and models, and multimodel systems. Verification was a key and valuable component of the projects quantifying the results. Also, the types of services have expanded to include both summer and winter services, complex terrain and urban environments, air transport, air quality, hydrology, and health. Insights are presented in all aspects of nowcasting and very short-range forecasting from end-user decision-making, critical role of the forecaster, forecast systems (models, heuristics, observations), to science and knowledge gaps. Significance Statement The goal of nowcasting and very short-range forecasting is to provide predictions with lead times of 0–2 and 0–6 h, respectively, and with sufficient skill to issue reliable warnings for weather hazards. It has been an elusive goal. In the past 25 years, the scope has expanded from summer thunderstorm warnings to winter, aviation, air quality, hydrology, and health applications. Led by the World Meteorological Organization’s Nowcasting and Mesoscale Research Working Group, considerable progress has been made through leveraging international expertise, forecast and research projects at the Olympics, engagement with emergency management agencies, training workshops, and symposia. This paper summarizes and discusses the insights, lessons learned, and challenges.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".