Forecasting local daily precipitation patterns in a climate change scenario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study introduces a statistical procedure for obtaining long-term local daily precipitation forecasts in a climate change scenario. It is based on a regression model that uses climate variables properly reproduced by a General Circulation Model (GCM) as predictors. The daily rainfall model used consists of a logistic regression as the occurrence model and a generalized linear model (GLM) with Gamma error distribution as the quantity model. The ability of the model to generate plausible long-term projections is analysed by studying and comparing its behaviour using observed and GCM simulated data as input. The method is applied to forecast the rainfall pattern in the area of Zaragoza (Spain) for the period 2090-2100, in an IS92a scenario. We use the data corresponding to an experiment with the CGCM1 model, the first version of the coupled GCM of the Canadian Centre for Climate Modelling and Analysis (CCCma). The results obtained show that no significant change in global rainfall frequency or in the annual accumulated amount are to be expected; however, an important modification of the seasonal cycle, with a high decrease in rainfall frequency and in the amount collected in spring, is forecasted.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it