Developing a Convective-Scale EnKF Data Assimilation System for the Canadian MEOPAR Project
Bibliographic record
Abstract
Abstract This study discusses the construction of a high-resolution ensemble Kalman filter system (the HREnKF) developed for the Marine Environmental Observation Prediction and Response (MEOPAR) network. The HREnKF runs at a horizontal resolution of 2.5 km and is intended to provide forecasts at lead times up to 12 h. This system was adapted from the global EnKF system in operation at Environment and Climate Change Canada. As a first development step, only the most necessary modifications have been implemented. The changes include an hourly cycling frequency, smaller localization radii, and the explicit representation of microphysical processes. To assess its performance and orient future developments, the HREnKF was continuously cycled for a period of 12 days. Verification against surface observations reveals that the skill of the forecasts initialized from the HREnKF is comparable to that of control forecasts also integrated at a resolution of 2.5 km. A key component of this study is the comparison of correlation estimated from ensembles at resolutions of 2.5, 15, and 50 km. At 2.5 km, correlation lengths are smaller than those found at 15 and 50 km. These short correlation lengths demand a high observational density, which is not available over the west coast domain where the HREnKF was tested. The spatial and temporal variability of the correlations is also assessed for the HREnKF system. It is found that correlation patterns are complex and do not generally decrease monotonically away from the reference point around which they are estimated. This result is important as it indicates that separation distance may not be the ideal parameter to use as a basis for localization strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".