The Extreme Precipitation Forecast Table: improving situational awareness when heavy rain is a threat
Bibliographic record
Abstract
A collaborative team of Science and Operations Officers from the National Weather Service (NWS) Weather Forecast Offices (WFOs), hydrologists from the Lower Mississippi River Forecast Center (LMRFC), and management from the Weather Prediction Center (WPC) worked together to develop and transition a tool into NWS operations called the Extreme Precipitation Forecast Table (EPFT). The EPFT was designed to help NWS forecasters improve their situational awareness (SA) when heavy rainfall threatens their county warning area. The EPFT compares Quantitative Precipitation Forecasts (QPF) to Average Recurrence Intervals (ARIs) from the NOAA Atlas-14 to alert forecasters to the potential for climatologically significant and extreme rainfall. A counterpart to the EPFT, called the Extreme Precipitation Assessment Table (EPAT), compares observed precipitation (i.e., Quantitative Precipitation Estimates [QPE]) to inform forecasters as to the climatological significance of impactful rain events. This paper presents cases demonstrating the usefulness of the EPFT and EPAT in helping forecasters improve their SA in real-time operational settings when heavy rain was a threat.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".