Characteristics of Target Areas Selected by the Ensemble Transform Kalman Filter for Medium-Range Forecasts of High-Impact Winter Weather
Bibliographic record
Abstract
Abstract The characteristics of “target” locations of tropospheric wind and temperature identified by a modified version of the ensemble transform Kalman filter (ETKF), in order to reduce 0–7-day forecast errors over North America, are explored from the perspective of a field program planner. Twenty cases of potential high-impact weather over the continent were investigated, using a 145-member ensemble comprising perturbations from NCEP, ECMWF, and the Canadian Meteorological Centre (CMC). Multiple targets were found to exist in the midlatitude storm track. In half of the cases, distinctive targets could be traced upstream near Japan at lead times of 4–7 days. In these cases, the flow was predominantly zonal and a coherent Rossby wave packet was present over the northern Pacific Ocean. The targets at the longest lead times were often located within propagating areas of baroclinic energy conversion far upstream. As the lead time was reduced, these targets were found to diminish in importance, with downstream targets corresponding to a separate synoptic system gaining in prominence. This shift in optimal targets is sometimes consistent with the radiation of ageostrophic geopotential fluxes and transfer of eddy kinetic energy downstream, associated with downstream baroclinic development. Concurrently, multiple targets arise due to spurious long-distance correlations in the ETKF. The targets were least coherent in blocked flows, in which the ETKF is known to be least reliable. The effectiveness of targeting in the medium range requires evaluation, using data such as those collected during the winter phase of The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Field Campaign (T-PARC) in 2009.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".