Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices
Bibliographic record
Abstract
Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".