Evaluation of Forecast Performance of Asian Summer Monsoon Low-Level Winds Using the TIGGE Dataset
Bibliographic record
Abstract
Abstract The forecast performances of the East Asian summer monsoon (EASM) and South Asian summer monsoon (SASM) by six THORPEX Interactive Grand Global Ensemble (TIGGE) centers during the summers of 2008–13 were evaluated to reflect the current predictability of state-of-the-art numerical weather prediction. The results show that the EASM is overestimated by all TIGGE centers except the Canadian Meteorological Centre (CMC). The SASM is overestimated by the European Centre for Medium-Range Weather Forecasts (ECMWF), the China Meteorological Administration (CMA), and the CMC, but is underpredicted by the Japan Meteorological Agency (JMA). Additionally, the SASM is overestimated for early lead times and underestimated for longer lead times by the National Centers for Environmental Prediction (NCEP) and the Met Office (UKMO). Further analysis suggests that such biases are likely associated with land–sea thermal contrasts. The EASM surge is overestimated by the NCEP and CMA and mainly underestimated by the others. The bias predictabilities for the SASM surge are similar to those of the SASM. The peaks of the SASM and EASM, including their surges, are mainly underestimated, whereas the valleys are mostly overestimated. Overall, the ECMWF and UKMO have the highest forecast skill in predicting the SASM and EASM and both have respective advantages. The TIGGE centers generally show higher skill in predicting the SASM than the EASM, and their skill in forecasting the SASM and EASM is superior to that for their respective surges. Moreover, bias-correction forecast skills show improvement with higher correlation coefficients in raw forecast verification.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".