Deep learning for multi-source precipitation fusion on the Qinghai–Tibet Plateau
Bibliographic record
Abstract
Accurate precipitation estimation remains a considerable challenge across the Qinghai–Tibet Plateau (QTP). By integrating the strengths of in situ gauge accuracy and the spatiotemporal continuity of gridded products, this study developed a highly accurate spatiotemporal deep fusion model using Convolutional Neural Networks (CNN) and long short-term memory (LSTM) networks (CNN-LSTM) to combine remote sensing estimates (TRMM-3B42V7, IMERG-FinalV06), reanalysis products (ERA5-Land), and in situ rain gauges. The CNN handles spatial feature extraction, whereas the LSTM captures temporal dependencies. Evaluations at 104 rain gauge stations across the QTP demonstrated that CNN-LSTM model significantly outperforms both traditional ANN/CNN fusion models and individual gridded precipitation products. It achieved an overall correlation coefficient (CC) of 0.61 and a root mean square error (RMSE) of 2.73 mm/d at the daily scale. Furthermore, its advantages are manifested in three key aspects: temporally, it consistently performs best across multiple time scales (daily, monthly, seasonal, and annual); spatially, it performs better at the vast majority of stations, particularly in the southeastern and southern regions with complex precipitation gradients; additionally, it demonstrates superior detection performance across all precipitation intensities, particularly for heavy and torrential rainfall events. This result enhance the understanding of the water cycle in the QTP.
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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 | 0.001 |
| 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".