A regionally refined quarter-degree global atmospheric rivers database based on ERA5
Bibliographic record
Abstract
Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle. The continually developing Tracking Atmospheric Rivers Globally as Elongated Targets (tARget) algorithm identifies AR objects at individual time steps based on thresholding integrated water vapor transport (IVT) and other requirements, and tracks each AR object in time and space. Building on previous versions of tARget, this paper discusses further refinements to the algorithm to better handle ARs in tropical and polar areas, as well as "zonal" ARs which the previous versions of the algorithm were not designed to capture. This further regionally refined algorithm is applied to the ERA5 reanalysis over 1940-2023 at 6 h intervals and a 0.25° × 0.25° horizontal resolution. The AR detection results are evaluated in terms of key AR characteristics. We anticipate this regionally refined global AR database will aid further understanding of ARs such as AR process studies, evaluation of AR simulations and predictions, and assessment of climate change impacts on ARs.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.004 | 0.002 |
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".