Calibration and bias correction of seasonal weather forecasts from the North American Multi-Model Ensemble: Potential applications for regional crop modelling and irrigation management
Bibliographic record
Abstract
Abstract Reliable seasonal weather forecasts are essential for irrigation management and crop yield prediction, particularly in regions with limited water resources. This study aimed to improve the usability of the North American Multi-Model Ensemble (NMME), an experimental real-time seasonal weather forecast system, for regional crop modelling and irrigation decision-making. Coarse resolution of NMME may introduce bias and uncertainty at regional/local scales. To address this, a statistical downscaling method with bias correction for both mean and variability was used to produce 1-km gridded daily weather projections for temperature and precipitation across the contiguous United States from a representative NMME model, the Canadian Coupled Climate Model version 4 (CanCM4). The daily surface weather and climatological summaries (DAYMET) data were used to calibrate the downscaled hindcast projections of CanCM4. The reliability of downscaled CanCM4 forecasts for local crop modelling was evaluated at lead times of up to six months using a calibrated DSSAT model at a research station in the semi-arid Texas Rolling Plains region. Cross-validation during the hindcast period demonstrated strong forecast skill, with R 2 values of 0.72 and 0.71 for maximum and minimum temperatures, respectively. The precipitation forecast remained sensitive to extreme events, with seasonal and annual relative errors of 31 and 1 %, respectively. Crop yield predictions had a relative error of 9 %, and irrigation water requirements closely matched field observations, outperforming both raw CanCM4 and multi-model mean methods. The downscaling method used in this study significantly improved NMME data reliability, although the degree of improvement may vary with time and location.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".