Exploration of Predictors for Statistical‐Dynamical Subseasonal Prediction of Western North‐Pacific Tropical Cyclone Activity in Earth System Models
Bibliographic record
Abstract
Abstract Subseasonal prediction of tropical cyclones (TCs) has many potential applications but remains a challenge due to biases in both model‐based large‐scale conditions and TCs in coupled global models. Model forecasts of environmental parameters can be linked to TC activity and then be used to extend the horizon of useful skill through statistical‐dynamical models. The aim of this work is to assess the utility of incorporating model forecasted environmental fields in a statistical model compared with skill coming from model forecasted Madden Julian Oscillation (MJO) state in predicting TC activity over the Western North Pacific (WNP). In this study, we evaluate the European Center for Medium‐Range Weather Forecasts (ECMWF) from the Subseasonal‐to‐Seasonal (S2S) database and the Navy Earth System Prediction Capability (ESPC) as part of the Subseasonal Experiment on their ability to predict WNP TC activity using environmental fields. To isolate the environmental signals associated with subseasonal variability of TC activity, we examine events of anomalous accumulated cyclone energy, genesis, and TC days. These events are used to create composites of ERA5 reanalysis fields of environmental conditions related to WNP TC activity, which are used to select predictors for statistical dynamical hybrid models. The ECMWF statistical‐dynamical scheme exhibits an improvement in skill by using a tailored outgoing longwave radiation (OLR) predictor compared with the MJO predictors. The Navy‐ESPC generally performs worse than the ECMWF and has OLR biases that impede it from improving skill in the statistical‐dynamical schemes. Using shear and humidity fields as predictors did not improve predictability in either model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".