Enhanced Global Tropical Cyclone Identification in ERA5 through Bayesian Inference and Dynamic Tracking (BIDTrack) Algorithm
Bibliographic record
Abstract
Abstract In this study, the Bayesian Inference and Dynamic Programming Tracking (BIDTrack) algorithm is developed for enhanced global tropical cyclone (TC) tracking in reanalysis datasets, particularly the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack addresses challenges like trajectory discontinuities and parameter sensitivity in traditional methods by combining Bayesian inference with dynamic programming. The algorithm is optimized through a Bayesian interval optimization (BIO) process, which refines the parameters to retain cyclone candidates that are statistically significant and physically meaningful. Results indicate a strong spatial correlation between BIDTrack-derived trajectories and International Best Track Archive for Climate Stewardship (IBTrACS) observations, especially in cyclone-prone regions like the North Atlantic and western Pacific. BIDTrack captures both major hurricanes and weak storms, providing a reliable tool for cyclone path reconstruction and climate impact assessments. This research demonstrates BIDTrack’s potential in improving TC tracking and enhancing the understanding of cyclone dynamics in ERA5. Significance Statement Tropical cyclones, such as hurricanes, are powerful storms that pose significant risks to coastal communities. Tracking their paths accurately is crucial for understanding their behavior and mitigating their impacts. In this study, an emerging method, Bayesian Inference and Dynamic Programming Tracking (BIDTrack), is introduced by combining Bayesian inference with dynamic programming to enhance cyclone tracking in the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack generates cyclone paths with probability estimates, providing a more precise assessment of whether a given track point corresponds to the actual cyclone. This algorithm is effective in tracking both strong hurricanes and weaker storms, making it a valuable tool for researchers and decision-makers interested in cyclone behavior and climate impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".