Convective-scale radar data assimilation and adaptive radar observation with the Ensemble Kalman Filter
Bibliographic record
Abstract
The flow-dependent background error statistics and other uncertainties involved in Ensemble Kalman Filter (EnKF), such as model error, initial perturbations, etc., are studied by a numerical weather prediction model and a few simple idealized experiments, respectively. Following the aforementioned studies, a convective-scale EnKF system is implemented to assimilate real radar data of radial velocity provided by the McGill J. S. Marshall Radar Observatory. The performance of this system and its impact on short-term ensemble forecasts are examined in three summer cases with different precipitation structures. In order to enhance and prolong the improvement brought by radar data assimilation on weather prediction, an adaptive radar observation method is proposed based on the background error statistics in EnKF. This method takes advantage of the phased-array radar technique to adaptively place observations where the important and unobserved model variable has more chances of improvement.The idealized experiments of EnKF suggest that a better analysis requires sufficient ensemble spread in initial perturbation, accurate estimation of model and observation errors, and radar data thinning if necessary. The studies on the background error statistics showed that homogeneous isotropic background error perturbations can develop into situation-dependent features in 15 minutes, and the error structures in the regions with and without precipitation are different. Results from the high-resolution EnKF system indicate that the analysis uncertainty can be reduced after a 1-h cycling process; and that radial velocity assimilation has an impact on the precipitation field. Additionally, the improvement in ensemble forecasts is evident in observation space within a 2-hour lead-time. When the adaptive radar observation method is applied on radial velocity assimilation, the unobserved vertical velocity can be better improved in areas where background error cross-covariances is more significant. Nevertheless, the improvement on the unobserved variable is much smaller than for the observed variable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".