Impact of Assimilating C‐Band Phased‐Array Radar Data With EnKF on the Forecast of Convection Initiation: A Case Study in Beijing, China
Bibliographic record
Abstract
Abstract This study used a Weather Research and Forecasting (WRF)‐based Ensemble Kalman Filter (EnKF) system to assimilate reflectivity (Z) and radial velocity (Vr) data in precipitating and clear‐air regions from the Beijing Daxing International Airport C‐band phased‐array radar (C‐PAR) to improve the forecasts of a convective initiation (CI) case occurred on 18 June 2020. The results showed that high‐frequency assimilating the C‐PAR Vr in clear‐air region is conducive to increase the forecast lead time of CI by significantly improving the initial dynamic and thermodynamic fields, which creates a more accurate pre‐CI environment. After assimilating the C‐PAR clear‐air Vr, the CI case can be accurately predicted with a 20 min forecast lead time in the best‐case scenario. This is the first real‐case study to demonstrate the benefits of assimilating high spatiotemporal resolution PAR clear‐air radial velocity data for the CI process.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".