Higher Resolution in an Operational Ensemble Kalman Filter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Recently, the computing facilities available to the Meteorological Service of Canada were significantly upgraded. This provided an opportunity to improve the resolution of the global ensemble Kalman filter (EnKF) and the medium-range Global Ensemble Prediction System (GEPS). In the EnKF, the main upgrades include improved horizontal, vertical, and temporal resolution. With the introduction of the higher horizontal resolution, it was decided to use a filtered topography in order to address an occasional instability problem. At the same time, the number of assimilated radiance observations was increased via a relaxation of the data-thinning procedures. In the medium-range GEPS, which already used the higher horizontal resolution, the filtered topography was also adopted. Likewise, the temporal resolution was increased to be the same as in the short-range integrations of the EnKF. With these changes, the grid used by the Canadian EnKF has 600 × 300 points in the horizontal and 74 vertical levels. The forecast model uses a 20-min time step and, for time interpolation of the model trajectories, model states are stored every hour. The EnKF uses an ensemble having 192 members. This paper sequentially examines the impact of these implemented changes. The upgraded EnKF became operational at the Canadian Meteorological Centre in mid-February 2013.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.045 | 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 it