Assessment of Satellite-Based Precipitation Estimates over Egypt
Bibliographic record
Abstract
Ground precipitation measurements face obstacles in many regions of Egypt, where gauge stations are sparse. It is thus necessary to find reliable sources such as satellite-based precipitation products, which provide uninterrupted precipitation time-series with high spatial resolution. This work evaluated the performance of four well-known global satellite precipitation products, i.e., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM3B42V7), Integrated Multi-satellitE Retrievals for global precipitation measurement-Final (IMERG-F), and Global Satellite Mapping of Precipitation (GSMaP)-Gauge, against gauged data of 23 stations in Egypt. GSMaP-Gauge revealed its outstanding abilities over the other three products in detecting rainfall occurrences and estimating the amount of rainfall. Further, Global Satellite Mapping of Precipitation (GSMaP-Gauge) was corrected by three commonly used bias-correction methods: linear scaling; local intensity scaling; and empirical quantile mapping (EQM). All three methods are effective in reducing biases to some extent, especially on the annual scale, with EQM being the best for most cases. This study demonstrates that satellite products can provide an alternative source of rainfall information for Egypt.
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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 | 0.001 | 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 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".