A Century of Progress in Severe Convective Storm Research and Forecasting
Bibliographic record
Abstract
Abstracts The history of severe thunderstorm research and forecasting over the past century has been a remarkable story involving interactions between technological development of observational and modeling capabilities, research into physical processes, and the forecasting of phenomena with the goal of reducing loss of life and property. Perhaps more so than any other field of meteorology, the relationship between researchers and forecasters has been particularly close in the severe thunderstorm domain, with both groups depending on improved observational capabilities. The advances that have been made have depended on observing systems that did not exist 100 years ago, particularly radar and upper-air systems. They have allowed scientists to observe storm behavior and structure and the environmental setting in which storms occur. This has led to improved understanding of processes, which in turn has allowed forecasters to use those same observational systems to improve forecasts. Because of the relatively rare and small-scale nature of many severe thunderstorm events, severe thunderstorm researchers have developed mobile instrumentation capabilities that have allowed them to collect high-quality observations in the vicinity of storms. Since much of the world is subject to severe thunderstorm hazards, research has taken place around the world, with the local emphasis dependent on what threats are perceived in that area, subject to the availability of resources to study the threat. Frequently, the topics of interest depend upon a single event, or a small number of events, of a particular kind that aroused public or economic interests in that area. International cooperation has been an important contributor to collecting and disseminating knowledge. As the AMS turns 100, the range of research relating to severe thunderstorms is expanding. The time scale of forecasting or projecting is increasing, with work going on to study forecasts on the seasonal to subseasonal time scales, as well as addressing how climate change may influence severe thunderstorms. With its roots in studying weather that impacts the public, severe thunderstorm research now includes significant work from the social science community, some as standalone research and some in active collaborative efforts with physical scientists. In addition, the traditional emphases of the field continue to grow. Improved radar and numerical modeling capabilities allow meteorologists to see and model details that were unobservable and not understood a half century ago. The long tradition of collecting observations in the field has led to improved quality and quantity of observations, as well as the capability to collect them in locations that were previously inaccessible. Much of that work has been driven by the gaps in understanding identified by theoretical and operational practice.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".