Radar Data Assimilation in the Canadian High-Resolution Ensemble Kalman Filter System: Performance and Verification with Real Summer Cases
Bibliographic record
Abstract
Abstract An 80-member high-resolution ensemble Kalman filter (HREnKF) is implemented for assimilating radar observations with the Canadian Meteorological Center’s (CMC’s) Global Environmental Multiscale Limited-Area Model (GEM-LAM). This system covers the Montréal, Canada, region and assimilates radar data from the McGill Radar Observatory with 4-km data thinning. The GEM-LAM operates in fully nonhydrostatic mode with 58 hybrid vertical levels and 1-km horizontal grid spacing. As a first step toward full radar data assimilation, only radial velocities are directly assimilated in this study. The HREnKF is applied on three 2011 summer cases having different precipitation structures: squall-line structure, isolated small-scale structures, and widespread stratiform precipitation. The short-term (<2 h) accuracy of the HREnKF analyses and forecasts is examined. In HREnKF, the ensemble spread is sufficient to cover the estimated error from innovations and lead to filter convergence. It results in part from a realistic initiation of HREnKF data assimilation cycle by using a Canadian regional EnKF system (itself coupled to a global EnKF) working at meso- and synoptic scales. The filter convergence is confirmed by the HREnKF background fields gradually approaching to radar observations as the assimilation cycling proceeds. At each analysis step, it is clearly shown that unobserved variables are significantly modified through HREnKF cross correlation of errors from the ensemble. Radar reflectivity observations are used to verify the improvements in analyses and short-term forecasts achievable by assimilating only radial velocities. Further developments of the analysis system are discussed.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".