Use of single Doppler radar observations in data assimilation at convective scale with model as a weak constraint
Bibliographic record
Abstract
In this work, we have considered several aspects of the McGill radar assimilation system. Currently, information other than radar observations is included in the assimilation system: the forecast of a high-resolution numerical weather prediction model. Besides, the structure of errors of this background term has been improved using a recursive filter. With single-Doppler S-band radar observations, the analyses from the assimilation system proved to successfully generate convection in a rainfall-free background. Furthermore, the system successfully simulated the evolution of a convective storm for more than 30 minutes. To account for the rapid evolution of the convective storms and to correct the forecast errors with time, a cycling process has also been applied. The cycling process helps to maintain the intensity of storm cells for a longer period of time. However, a comparison of radar observations with the 90-min simulation indicated an error in the position of the convective cells. Errors in forecasts, frequently referred to as background errors, result from errors in the initial conditions that grow through non-linear model equations with imperfect physical parameterizations. However, estimating forecast errors is not straightforward since the true atmospheric state is never exactly known. Ensemble forecasting is a feasible way to characterize the structure of forecasting errors and represent the probability distribution of plausible atmospheric states. In our work, an ensemble scheme has been applied to understand the structure of background errors at convective scale. The analysis has consisted in perturbing radar observations with two sets of simulated errors: one that neglects the spatial correlation of radar errors, and another where their spatial correlation is prescribed. The sensitivity of the system to such perturbations has been studied over a convective case. The results demonstrated that neglecting the correlation of radar errors badly limits the spread of ensembles and underestimates the model error correlation. In addition, further studies on the cross-correlations between different control variables illustrated the strong connection between the dynamics and the microphysical processes as depicted by the model. Our work also included the analysis of the different features of the background error within and outside the precipitation regions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".