Precipitation regridding – Impacts at global scale
Bibliographic record
Abstract
<p>Re-gridding considerably alters precipitation statistics. Despite this fact, regridding precipitation datasets is commonly performed for coupling or comparing different models/datasets. In general, several studies have highlighted the effects of regridding at regional scale. In this study, the effects of re-gridding precipitation are emphasized at a global scale using different regridding methods, size of the shifts and resolutions of the dataset. Substantial differences are noted at high quantiles and precipitation dry (or wet-dry frequency) is altered to a great extent. Specifically, a difference of 46 mm in high (0.95) quantiles and a reduction of 30% wet-dry frequency is noted. The differences increase with the size of the grid shift at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. Spatially, large differences at high quantiles in tropical land regions, and at low quantiles in polar regions are noted. These impacts are approximately same for the three different (first order conservative, bilinear, and distance weighted averaging) regridding methods considered in this study. Overall, re-gridding should be performed with caution as it can alter the statistical properties of precipitation to a great extent and adds uncertainty to further analysis of using in any models or in combined precipitation products.</p>
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 | 0.000 |
| 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) | 0.060 | 0.001 |
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; both teacher heads agree on what is shown here.
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".