Potential of the CHIRPS Database for Extreme Precipitation Risk Studies. Assessment in the State of Jalisco (Mexico)
Bibliographic record
Abstract
The Assessment of extreme precipitation as a natural hazard for society requires high temporal and spatial resolutions data.In those terms, satellite-based precipitation products such as CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations) usually are an adequate data sources, not only because the resolution, but also due to the open access and low latency of the information.However, teleconnection efficiency is usually related to aspects such as the physical-geographical characteristics of terrains and the algorithms and parameterizations used in the process, so it is important to carry out a validation process before using this type of data source.Thus, this research aims to evaluate the potential of CHIRPS database in the risk studies associated with extreme precipitation through the study case of Jalisco state (Mé xico).CHIRPS data and in situ data, obtained from the Network of Conventional Climatic Stations of CONAGUA, are compared between 1981-2020, applying several descriptive and correlational statisticians, for this analysis, the daily data and pentads in the study period are used.For comparisons with reference stations, CHIRPS pixels containing those stations were used.The final value representing CHIRPS was obtained from a weighted mean interpolation between each of the pixel vertices and the location of the in situ station, and a series of extreme precipitation index proposed by the IPC C (Intergovernmental Panel on Climate Change).It was found that CHIRPS database is not recommended for the analysis of the maximum annual precipitation in 24 hours, as well as for consecutive days with rain and the total annual days with accumulated above the 95 percentiles.Nevertheless, results indicate a good adjustment for the annual distribution of rainfall and especially for the maximum annual precipitation in five days, useful for recurrence products and agrometeorological indicators.The importance of using CHIRPS data in this work is due to the lack of official information and the importance of combining data from satellite sensors with in situ stations, thus allowing a correction with real data.In the case of the Mexican territory, this product shows an acceptable behavior and its use has been recommended as an auxiliary database in studies related to droughts and average conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".