Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products
Bibliographic record
Abstract
Abstract Atmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR‐related precipitation using 18 years (2001–2018) of globally gridded AR locations derived from Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi‐satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias‐adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN‐CDR]) and reanalysis (MERRA‐2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR‐related events compared to non‐AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN‐CDR are fairly high, considerable discrepancies are shown in their estimation of AR‐related events (i.e., overall IMERG and CMORPH show a higher fraction of AR‐related precipitation). It was found that the degree of consistency between reanalysis and satellite‐based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".