Hurricane Irene (2011): Lessons for Achieving a Weather-Ready Nation
Notice bibliographique
Résumé
Hurricane Irene left a devastating imprint on the Caribbean and U.S. East Coast in late August 2011. The storm took the lives of more than 40 people and caused an estimated $6.5 billion in property damages. The effects of Irene were felt from the U.S. Virgin Islands and Puerto Rico to the Canadian Maritime Provinces and as far west as the Catskill Mountains. The storm produced widespread, devastating flooding in Vermont, New Hampshire, New York, and New Jersey and damaging storm surge along the coasts of North Carolina and Connecticut. It also downed trees and power lines, resulted in massive evacuations, and several rescue efforts. Hurricane Irene tested the technical, human, and psychological resilience of citizens, emergency response organizations, decision makers, and the personnel of the National Oceanic and Atmospheric Administration (NOAA). As part of NOAA’s National Weather Service (NWS) mission to safeguard life and property through continuous improvement, NOAA formed a service assessment team to evaluate the strengths and weaknesses of NWS performance during this storm. The service assessment summarizes the event, documents operational best practices, and provides recommendations for improved services and support in order to achieve NWS’ goal of a Weather-Ready Nation. The report of the team was released in late September 2012. Several of these findings and recommendations were also made by the NWS Service Assessment Team for Hurricane/Post-Tropical Cyclone Sandy (2012).The presentation will highlight the major findings and recommendations of the assessment and what changes have been implemented. Presenter Bio Dr. Kelley is a meteorologist with NOAA/National Ocean Service's Marine Modeling and Analysis Programs of the Coast Survey Development Lab. He is located at the NOAA-UNH Joint Hydrographic Center/Center for Coastal and Ocean Mapping. John is involved with the development, evaluation, and implementation of NOS' operational numerical ocean forecast modeling systems for estuaries, coastal waters, and the Great Lakes. These real-time forecast systems provide short-range forecasts of water levels, currents, salinity, and water temperature for the marine navigation community. In addition, he is the project manager NOS' nowCOAST GIS web mapping portal which provides maps of real-time observations, analyses, and forecasts for the coastal U.S. via an interactive map viewer and web map services. He received his undergraduate degree in Geography/Atmospheric Sciences from The University of Rhode Island, a M.S. in Meteorology and a Master in Public Administration from The Pennsylvania State University and a Ph.D. in Atmospheric Science from The Ohio State University. Following his Ph.D., he was a visiting postdoctoral scientist at the NWS' Environmental Modeling Center's Ocean Modeling Branch in Maryland. From September 2011 to September 2012, he was a member of the NWS Service Assessment Team for Hurricane Irene which documented and evaluated the NWS’ performance and effectiveness during Irene and made recommendations for improving its products and services.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».