Thunderstorm Detection and Prewarning via Multisource Fusion of Precipitation and Atmospheric Electric Field Features
Bibliographic record
Abstract
Traditional detection and prewarning methods based on a single atmospheric electric field (AEF) data source often fail to accurately assess thunderstorm weather conditions. This paper proposes a thunderstorm detection method that employs weighted multi-source fusion of precipitation and AEF features, achieving a high-precision evaluation through multi-modal feature fusion and machine learning model optimization. Based on the detection range of AEF apparatus, spatially matched radar chart precipitation data are extracted to construct physically meaningful precipitation statistic features and time-varying AEF features. Using feature importance analysis, the physical contribution of each feature is determined, and these features are weighted and fused accordingly to generate enhanced features. Furthermore, we establish an improved gradient boosting decision tree model that enhances classification performance in the weighted feature space by adaptively adjusting the learning rate. Experimental results demonstrate that this method achieves a highly competitive F1-score of 0.92 in thunderstorm recognition tasks, representing a 35.3% improvement over the traditional AEF threshold method. The proposed weighted fusion framework provides a novel solution for thunderstorm detection and prewarning through the synergistic use of ground-based AEF and radar-based precipitation data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".