Research gaps and challenges for impact-based forecasts and warnings: Results of international workshops for High Impact Weather in 2022
Bibliographic record
Abstract
The World Meteorological Organization (WMO) has called for more meaningful warnings to help reduce the impacts of weather-related events. Impact-based forecasts and warnings (IBFW) are being developed by forecasting agencies globally to meet this call. However, there are many challenges facing those implementing such systems. The WMO World Weather Research Programme High Impact Weather project sought to understand the future direction of research on IBFW systems. This research involved a virtual workshop series in late 2022 with over 350 international registrants to identify and analyse challenges that people are facing in developing IBFW systems, and potential solutions. We found that challenges relate to ten themes, in addition to defining the measures of success of an IBFW system Examples of key research gaps are to develop evaluation methods to explore the value of multi-hazard IBFW, in terms of collating data at appropriate scales, and including avoided losses, behavioural responses, and unconventional observations. We need to explore the value of using quantitative approaches in comparison to more efficient qualitative approaches, as well as of dynamic exposure and vulnerability data sets, and tailored warnings. We must investigate how to effectively communicate uncertainty and explore the governance of underpinning data. Further research on these topics will assist with the successful implementation of more meaningful warnings globally, whilst considering the feasibility and effectiveness of the efforts involved. This is our contribution to reducing the impacts of future hazards, at a time where climate-related events are expected to increase in severity. • We sought to understand future research directions for impact-based forecast and warning systems. • Analysis of international workshops found gaps relate to data, methods, and value. • Translating knowledge, tailoring, responsibilities, and communication were further gaps identified. • Further research on these topics will help implement successful IBFW systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".