Machine Learning Approaches to Improve North American Precipitation Forecasts
Bibliographic record
Abstract
Numerical weather prediction (NWP) is a challenging task which involves working with micro and macro-scale spatio-temporal parameters susceptible to biases and accuracy problems. In recent years, machine learning has grown in popularity with the increasing demand in accurate weather predictions. In this study, we adopt a multimodel (ensemble) forecasting approach by collecting precipitation data from multiple NWP models of Canadian, American and European weather agencies in an effort to deploy an optimal machine learning-based weather model for real-time precipitation forecasting that will outperform the baseline. We considered 8 NWP models as inputs and combined them to create ensemble predictors using 5 different machine learning techniques along with a baseline model (mean of eight input NWP models). We demonstrate that machine learning approaches can improve upon the results of the individual NWP models. The best results were obtained by the neural-network variants with 17% improvement in the mean absolute error, 3% in the root mean squared error, 47% in the median absolute error, 5% in the maximum error, 70% in the relative bias, 41% in the false alarm ratio and 8% in the critical score index over the baseline. Neural networks also complied with the practicality constraints, with minutes of training time and near-real time prediction time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".