Gridded 20-year Parameterization of a Stochastic Weather Generator (CLIGEN) to Fill Gaps in Coverage in the Northern Hemisphere
Bibliographic record
Abstract
CLImate GENerator (CLIGEN) is a stochastic weather generator that produces weather time series for soil erosion modeling and various other applications. The generated time series are statistically similar to observed long-term time series. This gridded CLIGEN parameterization with 0.25° spatial resolution complements existing global coverages by filling in remaining gaps that existed in the northern hemisphere (see the map layer *.kmz file with all grid point locations). The coverage is largely represented by Canada, Europe, and Russia and encompasses countries north of ~40°N with no previous known coverage. The CLIGEN inputs may be used to generate daily precipitation, temperature, dewpoint, solar radiation, and wind time series, as well as sub-daily precipitation patterns. The gridded parameterization allows CLIGEN time series to be generated at any point the grid. In particular, the dataset can provide climate drivers for climate-related research in ungauged areas where observed climate records are unavailable. The data are formatted as CLIGEN *.par files, which are the only required input for CLIGEN. The files are contained in the "Grid Files" download with n=114,150 files corresponding to the total number of grid points. The files are labeled according to grid point lat/lon coordinates (WGS84) in decimal degrees. The labeling convention uses 'N' and 'E' (north, east) to represent coordinates with a positive sign and 'S' and 'W' (south, west) to represent coordinates with a negative sign.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.080 |
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".