Science of Preparedness. Berg, J. 357(6356):1073.
Science of Preparedness [link]Paper  doi  abstract   bibtex   
Our hearts go out to those affected by hurricanes Harvey and Irma and by earlier monsoons across South Asia. These events are compelling reminders of the important role that science must play in preparing for disasters. But preparation is challenging, as reflected in the many facets of the ” science of preparedness.” Certainly, modeling and forecasting storms are critical, but so are analyses of how agencies, communities, and individuals interact to understand and implement preparedness initiatives. [Excerpt] [...] Long-range estimates of the number and expected severity of storms in an upcoming hurricane season are also challenging because they are driven in part by changes in Earth's climate. These estimates depend on empirical data regarding long-term trends from previous seasons as well as computational models. Physical principles suggest that increases in storm intensity is a very likely consequence of global warming (see www.gfdl.noaa.gov/global-warming-and-hurricanes). These long-range predictions may be useful for urban planning and forward-looking budgeting to prepare, not for a particular event, but rather, for the likely occurrence of storms over time in a given location. [] The science of preparedness also relates to the development and evaluation of disaster management plans. Work in this area involves analysis of previous events to discern whether responses were effective in mitigating potential adverse outcomes or may have, in some cases, made them worse. Coordination between different agencies, the timeliness and clarity of decisions made by officials, and prior identification and mobilization of resources have all been identified as key factors of management plans. [...]
@article{bergSciencePreparedness2017,
  title = {Science of Preparedness},
  author = {Berg, Jeremy},
  date = {2017-09},
  journaltitle = {Science},
  volume = {357},
  pages = {1073},
  issn = {0036-8075},
  doi = {10.1126/science.aap9025},
  url = {http://mfkp.org/INRMM/article/14430813},
  abstract = {Our hearts go out to those affected by hurricanes Harvey and Irma and by earlier monsoons across South Asia. These events are compelling reminders of the important role that science must play in preparing for disasters. But preparation is challenging, as reflected in the many facets of the ” science of preparedness.” Certainly, modeling and forecasting storms are critical, but so are analyses of how agencies, communities, and individuals interact to understand and implement preparedness initiatives.

[Excerpt] [...] Long-range estimates of the number and expected severity of storms in an upcoming hurricane season are also challenging because they are driven in part by changes in Earth's climate. These estimates depend on empirical data regarding long-term trends from previous seasons as well as computational models. Physical principles suggest that increases in storm intensity is a very likely consequence of global warming (see www.gfdl.noaa.gov/global-warming-and-hurricanes). These long-range predictions may be useful for urban planning and forward-looking budgeting to prepare, not for a particular event, but rather, for the likely occurrence of storms over time in a given location.

[] The science of preparedness also relates to the development and evaluation of disaster management plans. Work in this area involves analysis of previous events to discern whether responses were effective in mitigating potential adverse outcomes or may have, in some cases, made them worse. Coordination between different agencies, the timeliness and clarity of decisions made by officials, and prior identification and mobilization of resources have all been identified as key factors of management plans. [...]},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14430813,~to-add-doi-URL,computational-science,cooperation,decision-making,environmental-modelling,long-term,modelling,networks,planning,preparedness,science-based-decision-making,short-term-vs-long-term},
  number = {6356}
}
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