Prediction of the summertime Northwest Pacific subtropical high based on ConvLSTM
Bibliographic record
Abstract
The Northwest Pacific subtropical high (NWPSH) significantly affects East Asian weather and climate, rendering the prediction of its intensity and location critically important. This study aims to evaluate the performance of the Convolutional Long and Short-Term Memory (ConvLSTM) model for predicting the summertime 500 hPa geopotential height and NWPSH intensity and area at a lead time of three months, and to compare it with the dynamical models of the Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS1.0) and the Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2). The mean latitude-weighted RMSE (RMSE w ), anomaly correlation coefficient (ACC), and NWPSH indices are used as evaluation metrics. For both summer mean and monthly prediction, the ConvLSTM model outperforms the two dynamical models in terms of RMSE w and ACC for the 500 hPa geopotential height over the western Pacific region. The correlation coefficients between the NWPSH intensity index predicted by the ConvLSTM model and the observations are higher than those obtained from the two dynamical models. Regarding the NWPSH area index, the ConvLSTM model shows more stable performance. Particularly in August, the improvement of the ConvLSTM model compared to the two dynamical models is more significant, indicating the robust capability in capturing late-summer circulation patterns. Therefore, the ConvLSTM model demonstrates significant potential for summer NWPSH prediction, offering a new perspective and approach for climate prediction in this region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".