From deterministic forecasts to probabilistic impact-based forecasts in meteorology: Theory and practice
Bibliographic record
Abstract
Modern weather forecasts and warnings are essential for billions of people around the globe, providing information about daily conditions as well as possible hazards. Despite the significance, they struggle with the challenge of in-complete accuracy in communication due to several factors, such as atmospheric unpredictability, data interpretation, uncertainty of composing forecasts, and forecast interpretation (Gill et al., 2008, p. 6). This study critically examines the prevalent deterministic approach in public meteorological communication, often presenting predictions as definite. The author explores the necessity for a shift towards uncertainty-informed impact-based forecasts, advocated by the World Meteorological Organization (WMO, 2021). Through multidisciplinary inquiry, this research reveals deterministic forecasts' limitations in conveying uncertainty sources, eroding public trust when forecasts do not align with reality, subsequently hindering decision-making (Burgeno and Joslyn, 2023). In contrast, probabilistic forecasts with transparent uncertainty margins might offer a more nuanced depiction of atmospheric variability, fostering public confidence and reducing bias adjustments. A multidisciplinary approach from cognitive studies, meteorology, and geography is employed to understand how communication induces behavioural responses to ambient and severe weather.Embracing uncertainty not only enhances forecast accuracy but also facilitates informed prioritisation of precautionary measures, bolstering societal resilience to weather-related hazards. Moreover, integrating uncertainty communication into impact-based forecasts informs broader activities such as transportation planning and emergency preparedness. This is especially important in the case of unlikely yet high-disruption events, as well as highly likely yet limited-disruption events.The current overview of forecast communication practices from meteorological institutes and agencies across Europe is presented and scrutinised against the existing theoretical basis. Accessible strategies for communication advancement are discussed, and possible future solutions in personalised forecasting are theorised. From a wider perspective, this research aims to guide informed activity in space and in relation to transport choices, ad-hoc plan adjustment, and protective action towards self and personal property.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".