An introduction to NCEP SREF aviation project
Bibliographic record
Abstract
NCEP’s Short Range (1-3-day) Ensemble Forecast (SREF) system provides mesoscale probabilistic forecast information, and has undergone several stages of development at the NCEP Environmental Modeling Center (EMC) since 1996 (Tracton et al 1998, Du and Tracton 2001, Du et al 2004). It has been well understood that the forecast skill of deterministic, computer-generated weather forecasts is limited by the chaotic processes in the atmosphere which bring either errors in the initial/boundary conditions or uncertainties in the model (Lorenz 1963). Generally, a single deterministic model will produce one possible solution to a weather system but may miss the actual situation. One approach called an Ensemble Forecast (EF) was proposed to try to capture a range of possible solutions and take into account the uncertainties in both the initial/boundary conditions and the models (Epstein 1969, Leith 1974, Palmer et al 1990). Since then, ensemble forecasts were launched and achieved significant progress at several weather centers in the world (NCEP, European Centre for Medium Range Weather Forecasts, US Navy, Japan Meteorological Agency, Canadian Meteorological Centre, etc). NCEP EMC is one of the pioneers in researching and developing EF systems, either on the global scale or mesoscale. The history of the NCEP Ensemble Prediction System (EPS) can be traced back to the early 1990s, when NCEP introduced and then implemented operationally its medium range global EPS (Tracton and Kalnay 1993, Toth and Kalnay, 1993, 1997). Motivated by the success of its global EPS, NCEP initiated the ensemble forecasting system for short range applications, using the Eta and the Regional Spectral Model (RSM) in the middle of 90s (Brooks, et al 1995, Tracton et al 1998). The Short Range Ensemble Forecast (SREF) system has undergone testing and forecast evaluation, and has shown promise in improving forecast skill for short-range forecasts (Hamill et al 1997, Du et al 1997, Stensrud, et al 2003). The SREF system was implemented operationally by NCEP in 2001, and is still being improved (Du and Tracton 2001, Du, et al 2004). In 2002, under FAA sponsorship, NCEP began its SREF Aviation Project in an effort to bring ensemble techniques to aviation forecasting by further postprocessing SREF generated output to create aviation-based forecast products for icing, turbulence and visibility, etc. This work is also applicable to the NCEP Aviation Weather Center (AWC) and NOAA Aviation Service Branch missions. This paper will present an overview of the SREF aviation project, including its configuration, post processing procedure, and product generation. Some concepts of verification and evaluation for the ensemble forecast are briefly discussed. Finally, the future plans for the SREF aviation project are presented.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".