Applying Fuzzy Clustering to a Multimodel Ensemble for U.S. East Coast Winter Storms: Scenario Identification and Forecast Verification
Bibliographic record
Abstract
Abstract This article introduces a method for objectively separating and validating forecast scenarios within a large multimodel ensemble for the medium-range (3–7 day) forecasts of extratropical cyclones impacting the U.S. East Coast. The method applies fuzzy clustering to the principal components (PCs) of empirical orthogonal function (EOF) analysis on mean sea level pressure (MSLP) from a 90-member combination of the global ensembles from the National Centers for Environmental Prediction, the Canadian Meteorological Center, and the European Centre for Medium-Range Weather Forecasts. Two representative cases are presented to illustrate the applications of this method. Application to the 26–28 January 2015 event demonstrates that the forecast scenarios determined by the fuzzy clustering method are well separated and consistent in different state variables (i.e., MSLP, 500-hPa geopotential height, and total precipitation). The fuzzy clustering method and an existing ensemble sensitivity method are applied to the 26–28 December 2010 event to investigate forecast uncertainty, which demonstrates that these two methods are complementary to each other and can be used in the operations together to track the evolution of forecast uncertainty. For past cases one can define a cluster close to the analysis based on the projection of the analysis onto the PC base of clustering. This analysis group is validated using conventional validation metrics for both cases examined, and this analysis group has fewer errors than the other groups as well as the multimodel ensemble mean and individual model means.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".