Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment
Why this work is in the frame
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Bibliographic record
Abstract
Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global Satellite Mapping of Precipitation (GSMaP) constellation over an arid environment like the Sultanate of Oman that is characterized by a complex topography and extremely variable rainfall patterns. Global Satellite-based Precipitation Estimates (GSPEs) can provide wide coverage and high spatial and temporal resolutions, but evaluating their accuracy is a mandatory step before involving them in different hydrological applications. In this paper, the reliability of the Integrated Multi-satellitE Retrievals for the GPM (IMERG) V04 and GSMaP V06 products were evaluated using the reference in-situ rain gauges at sub-daily (e.g., 6, 12, and 18 h) and daily time scales during the period of March 2014–December 2016. A set of continuous difference statistical indices (e.g., mean absolute difference, root mean square error, mean difference, and unconditional bias), and categorical metrics (e.g., probability of detection, critical success index, false alarm ratio, and frequency bias index) were used to evaluate recorded precipitation occurrences. The results showed that the five GSPEs could generally delineate the spatial and temporal patterns of rainfall while they might have over- and under-estimations of in-situ gauge measurements. The overall quality of the GSMaP runs was superior to the IMERG products; however, it also encountered an exaggeration in case of light rain and an underestimation for heavy rain. The effects of the gauge calibration algorithm (GCA) used in the final IMERG (IMERG-F) were investigated by comparison with early and late runs. The IMERG-F V04 product did not show a significant improvement over the early (i.e., after 4 h of rainfall observations) and late (i.e., after 12 h of rainfall observations) products. The results indicated that GCA could not reduce the missed precipitation records considerably.
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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 | 0.000 |
| 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 it