Downscaling of Open Coarse Precipitation Data through Spatial and Statistical Analysis, Integrating NDVI, NDWI, Elevation, and Distance from Sea
Bibliographic record
Abstract
This study aims to improve the statistical spatial downscaling of coarse precipitation (TRMM 3B43 product) and also to explore its limitations in the Mediterranean area. It was carried out in Morocco and was based on an open dataset including four predictors (NDVI, NDWI, DEM, and distance from sea) that explain TRMM 3B43 product. For this purpose, four groups of models were established based on different combinations of the four predictors, in order to compare from one side NDVI and NDWI based models and the other side stepwise with multiple regression. The models that have given rise to the best approximations and best fits were used to downscale TRMM 3B43 product. The resulting downscaled and calibrated precipitations were validated by independent RGS. Aside from that, the limitations of the proposed approach were assessed in five bioclimatic stages. Furthermore, the influence of the sea was analyzed in five classes of distance. The findings showed that the models built using NDVI and NDWI have a high correlation and therefore can be used to downscale precipitation. The integration of elevation and distance improved the correlation models. According to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn fontstyle="italic">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>, RMSE, bias, and MAE, the study revealed that there is a great agreement between downscaled precipitations and RGS measurements. In addition, the analysis showed that the contribution of the variable (distance from sea) is evident around the coastal area and decreases progressively. Likewise, the study demonstrated that the approach performs well in humid and arid bioclimatic stages compared to others.
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 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".