An innovative multi-source precipitation merging method with the identification of rain and no rain
Bibliographic record
Abstract
Multi-source precipitation merging is a crucial way to estimate the spatiotemporal distribution of precipitation accurately. The commonly used merging methods mainly focus on bias correction of the total precipitation amount or precipitation intensity but often neglect to identify short-duration precipitation. In this study, we proposed a merging framework of multi-source precipitation by identifying rain and no rain and constructed a precipitation merging method considering both rain area identification and rainfall estimation by coupling the geographical weighted logistic regression (GWLR) and geographically weighted regression models (GWR). Then, the merging experiments of the Multi-Source Weighted-Ensemble Precipitation Version 2.1 (MSWEP V2.1) and the daily precipitation observed by the ground gauges network over the Han River basin were implemented. The results show that the proposed method successfully reproduces the spatial pattern of rain and no rain and catches the precipitation center. It overall strengthens the performance of MSWEP V2.1 to estimate ground precipitation, reduces the false alarm rate (RFA) and false precipitation (PF) by more than 60%, and improves the critical success index (ICS) and Kling-Gupta efficiency coefficient (EKG) by more than 40%. Moreover, the gains of correcting PF and improving EKG are higher than 10% against the spatially interpolated precipitation. Meanwhile, compared with reference data, precipitation fusion enhances the classification accuracy of heavy precipitation events (intensity ≥ 50 mm/d) by not less than 60%. The innovative method effectively improves the performance of precipitation estimation and provides a new idea for multi-source precipitation merging.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".