Multimodal Measurement Framework for Thunderstorm Charge Motion: Spatiotemporal Sensor Fusion With Bayesian-Optimized Localization
Bibliographic record
Abstract
The accurate measurement of thunderstorm cloud point charge motion is critical for analyzing thunderstorm dynamics but remains challenging due to the limitations of single-modal atmospheric electric field (AEF) data. This paper presents an enhanced multimodal sensor fusion framework that synergizes three-dimensional (3D) AEF measurements with radar echo intensity (REI) and precipitation data. The methodology introduces several key instrumentation advancements: 1) A spatiotemporal calibration method that resolves the sampling rate conflict (1s AEF vs. 6min radar) and incorporates two AEF features other than statistics—amplitude of change and zero-crossing time—to better capture non-stationary AEF signal dynamics; 2) A physics-constrained feature fusion strategy that integrates the AEF features with REI and precipitation data, followed by a Cohen’s d-based selection to construct a highly discriminative feature vector; 3) A multi-objective Bayesian optimization scheme for the random forest model, simultaneously minimizing classification error and model complexity; and 4) A trajectory post-processing module that applies smoothing and denoising to the 3D point charge localization results, yielding physically plausible motion paths. Validated against multiple weather events, the framework demonstrates superior performance in weather attribute classification and achieves dynamic, high-fidelity visualization of charge trajectories that show strong consistency with independent radar observations, establishing a new channel for extreme weather monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".