Investigating the Difference Between Members in the High-resolution Rapid Refresh Ensemble (HRRRE) During the February 23rd, 2022 Winter Storm
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,011 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».