Self-Attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts
Bibliographic record
Abstract
Abstract Current postprocessing techniques often require separate models for each lead time and disregard possible interensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we tackle these shortcomings with an innovative, fast, and accurate transformer which postprocesses each ensemble member individually while allowing information exchange across variables, spatial dimensions, and lead times by means of multiheaded self-attention. Weather forecasts are postprocessed over 20 lead times simultaneously while including up to fifteen meteorological predictors. We use the EUPPBenchmark dataset for training which contains ensemble predictions from the European Centre for Medium-Range Weather Forecasts’ integrated forecasting system alongside corresponding observations. The work presented here is the first to postprocess the 10- and 100-m wind speed forecasts within this benchmark dataset, while also correcting 2-m temperature. Our approach significantly improves the original forecasts, as measured by the continuous ranked probability score (CRPS), with 16.5% for 2-m temperature, 10% for 10-m wind speed, and 9% for 100-m wind speed, outperforming a classical member-by-member approach employed as a competitive benchmark. Furthermore, being up to 6 times faster, it fulfills the demand for rapid operational weather forecasts in various downstream applications, including renewable energy forecasting. Significance Statement Accurate weather forecasts are essential for the daily functioning of a myriad of societal and economical actors. Current weather forecasting models, however, still contain significant biases and insufficient model uncertainty. Therefore, in this study, we develop a model to improve the accuracy of wind speed and temperature forecasts, two essential weather variables, for e.g., the agricultural, transport, or renewable energy sector. The method is a fast and flexible deep learning algorithm which improves around 16.5% for temperature and 10% for wind speed as compared to the original forecasts. Future research could further enhance accuracy by tailoring the model to specific geographic regions classified based on different climatological conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".