Hurricane Irene (2011): Lessons for Achieving a Weather-Ready Nation
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".