A seasonal forecast scheme for spring dust storm predictions in Northern China
Bibliographic record
Abstract
Abstract The theme discussed in the present study is that of spring dust storm seasonal forecasts in Northern China. A comprehensive investigation of observations collected from 65 stations in Northern China, which studied strong winds for 35 years (1971–2005) and dust storms for 48 years (1961–2008), concluded that strong winds, which are recognized as a crucially dynamic factor, have unsurprisingly proven to be strongly related to dust storm activity. Therefore, determining effective predictors for strong winds should be helpful in spring dust storm forecasts. By employing this idea, comprehensive correlation analyses among the strong winds, dust storms and other influential elements from the oceans and the atmospheric circulations can be seen. From the spatial correlation fields between prior sea surface temperatures and the strong winds, four regions with higher oceanic coefficiencies are confirmed. The method of EOF (Empirical Orthogonal Function) decomposition is adopted to extract forecast signals from prior precipitation in Northern China and sea surface temperatures of those regions. The multivariable step‐regression model is employed to select efficient predictors and the multivariable regression model is used to create forecast equations. With the cross validation approach, six series of 48 year hindcasts with six different predictor sets are conducted. Furthermore, the three‐classification forecast method is used to judge successful or failed dust storm forecasts. Together, forecast skills of probability of detection and skill score suggest that series forecasts are better than random forecasts. The best forecast skill is gained from using the predictor set selected by the multivariable step‐regression model. Copyright © 2010 Royal Meteorological Society
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".