Application of the GPM-IMERG Products in Flash Flood Warning: A Case Study in Yunnan, China
Bibliographic record
Abstract
NASA’s Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is a major source of precipitation data, having a larger coverage, higher precision, and a higher spatiotemporal resolution than previous products, such as the Tropical Rainfall Measuring Mission (TRMM). However, there rarely has been an application of IMERG products in flash flood warnings. Taking Yunnan Province as the typical study area, this study first evaluated the accuracy of the near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-hourly temporal resolution. Then the performance of the two products was analyzed with the improved Rainfall Triggering Index (RTI) in the flash flood warning. Results show that (1) IMERG-F presents acceptable accuracy over the study area, with a relatively high hourly correlation coefficient of 0.46 and relative bias of 23.33% on the grid, which performs better than IMERG-E; and (2) when the RTI model is calibrated with the gauge data, the IMERG-F results matched well with the gauge data, indicating that it is viable to use MERG-F in flash flood warnings. However, as the flash flood occurrence increases, both gauge and IMERG-F data capture fewer flash flood events, and IMERG-F overestimates actual precipitation. Nevertheless, IMERG-F can capture more flood events than IMERG-E and can contribute to improving the accuracy of the flash flood warnings in Yunnan Province and other flood-prone areas.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".