Investigating the Difference Between Members in the High-resolution Rapid Refresh Ensemble (HRRRE) During the February 23rd, 2022 Winter Storm
Bibliographic record
Abstract
Probabilistic forecasting is one tool that is being used to help create more accurate and understandable forecasts. Using percentages and probabilities allows for more depth to a forecast and allows forecasters to be able to convey a clearer message of what exactly they are expecting. Ensembles are a set of forecast models that have either different starting conditions, boundary conditions or parameter settings. They are one way of creating probabilistic forecasts and can help in the understanding of the likelihood of a specific outcome. Forecasters use ensembles to attempt to analyze the range of possible outcomes and the likelihood of those outcomes that a weather system can present. However, each weather event is unique in the confidence and agreement between different weather models and their respective ensembles. The High-Resolution Rapid Refresh Ensemble (HRRRE) is an experimental ensemble product with a goal of having real world observations fall within the spread of the ensembles. There is an increased emphasis on the uncertainty of precipitation type (p-type) in mixed precipitation events. This study is to investigate the differences in key variables and p-type between the warmest and coldest HRRRE members. The forecast for both the camps of the ensemble will be compared to ground observations, specifically; 2-meter temperature, precipitation type, precipitation amount, and wind from the; New York State Mesonet, the Automated Surface Observation System, the meteorological Phenomena Identification Near Ground, from the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). During the WINTRE-MIX field campaign soundings were also launched at 4 different sites around the St. Lawrence River Valley that helped provide a vertical profile of the storm. The event that is going to be researched occurred from February 22nd-23rd, 2022 in northern New York and Southern Quebec. This event produced widespread icing from Northern New York through the St. Lawrence River Valley. Leading up to the event, differences in 925mb heights and meridional wind were observed between the warm and cold camps. Stronger meridional winds just above the boundary layer led to increased mixing in the warmer members throughout the boundary layer, leading to the surface inversion behind mixed out faster. Research supports the fact that the colder members were closer to reality at the surface within the WINTRE-MIX region due to this difference.
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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 | 0.000 |
| 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 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".