Concept of Probabilistic Impact Based Forecasts for the Canadian Armed Forces
Bibliographic record
Abstract
Environment and Climate Change Canada’s National Programs and Applied Development (NPAD) group, which provides support to the Canadian Armed Forces (CAF), began in 2015, work toward probabilistic impact based forecasts, in order to meet the CAF's needs, for environmental forecasting (meteorological and oceanographic) and the related impacts on their operations. Operations led by CAF are multidisciplinary by nature, cover a wide variety of geographical areas and can span variable durations. They are prepared in advance and are refined as additional information becomes available. These aspects constitute a challenge regarding the development of a decision-support tool that meets the diverse requirements associated with these missions from the initial planning to the final deployments. Both the Meteorological Service of Canada (MSC) transformation initiative and the “data centric approach” promoted by the Canadian Forces Weather and Oceanographic Service (CFWOS) have inspired this situational awareness project, called Consolidated Weather Impact Chart (CWIC). Based on the concept of a suite of systems providing a seamless datasets, post-processing of deterministic and probabilistic models outputs in their respective fields of excellence. A first stage, relying on the Canadian Global Deterministic Prediction System (GDPS) has been operationally implemented to create deterministic “on demand” consolidated weather impact charts and deterministic “impact-grams” for any location. As a second stage, the inclusion of probabilistic outputs aimed to provide to the user a tool with information of likelihood and impact for a specific mission/operation. In order to offer a probabilistic version of CWIC, its conceptual development was inspired by the both National Severe Weather Warning Service weather impact matrix developed by the UK Met Office, which combines likelihood and impacts and the Extreme Forecast Index (EFI) formulated by ECMWF, which characterizes the abnormality of forecasted events with respect to the model-climate. Resulting charts have highlighted its significant capability as a decision support tool for a wide spectrum of customers’ needs due to the integration of adjustable thresholds. A third stage extended the application of the matrix concept in order to provide operational forecasters with standard weather element depictions based on the wealth of information provided by both the Canadian Global and Regional Ensemble Prediction Systems (GEPS/REPS). This brings consistency with information provided by the probabilistic version of CWIC and supports meteorologists in interpreting and conveying risks of impactful weather to the CAF. In addition, probabilistic “impact-grams” relay the uncertainty related to the suggested weather scenario. This presentation aims to expose and share concepts in order to stimulate feedback, discussions and future collaboration.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".