A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset
Bibliographic record
Abstract
A global tropical cyclone precipitation dataset covering the period from January 1979 to February 2023 is presented. Global precipitation estimates were taken from the newly developed high-resolution Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2) and TC tracks were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset. This Global Multi-Source Tropical Cyclone Precipitation (MSTCP) dataset is comprised of two main products and files in the format of tables: the main and profile datasets. The main file provides various TCP statistics per TC track, including mean and maximum precipitation rates over a fixed and symmetrical radius of 500 km. The profile dataset comprises the azimuthally averaged precipitation every 10-km away from the center of each storm (until 500 km). The case study of Hurricane Harvey is used to show that MSWEP estimates agree well with another commonly used satellite product. The main statistics of the dataset are analyzed as well, including the differences in the dataset metrics for each of the six TC basins and for each Saffir-Simpson category for storm intensity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; both teacher heads agree on what is shown here.
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".