Neighborhood‐Based Verification of Precipitation Forecasts at the Local Scale: An Application Over Southern Quebec
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Bibliographic record
Abstract
ABSTRACT The emergence of high‐resolution numerical weather prediction (NWP) systems over recent decades has brought new verification challenges, namely accounting for the “double penalty” effect. While spatial verification methods have been developed to mitigate this issue, they generally provide domain‐wide performance assessments, potentially obscuring spatial heterogeneity in the NWP performances. This study introduces a novel methodology for evaluating the NWP performances at the local scale within a neighborhood‐based framework. Local contingency tables are constructed for each cell of the grid, populated with events occurring within a defined neighborhood window, allowing for the compensation of spatial location errors. These local contingency tables are then temporally aggregated across a set of forecasts to produce a temporal local contingency table at each grid point, thereby enabling localized performance assessment. The methodology was applied to a large region centered in Southern Quebec using forecasts from six NWP systems (GDPS, RDPS, HRDPS, GFS, NAM, and RAP) over a 2‐year period (2022–2023). Analyses were conducted across four precipitation intensity thresholds (0.1, 5, 10, and 25 mm/6 h) and three forecast lead‐time categories (Days 1–2, 3–4, and 5–7 combined, depending on data availability). Four metrics were employed in the evaluation: Bias, false alarm ratio (FAR), probability of detection (POD), and equitable threat score (ETS). The performance is primarily governed by the precipitation intensity threshold, with forecast skill deteriorating as the threshold increases, particularly, for intense and extreme events. Although forecast lead‐time has a secondary yet nonnegligible influence, spatial variability of metric values becomes increasingly pronounced at higher intensity thresholds, despite certain limitations in evaluating extreme precipitation events. Notably, the evaluation at the local scale and the delineation of homogeneous regions proved particularly valuable at the 5 mm/6 h threshold, underscoring the relevance of localized verification approaches for moderate precipitation events.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it