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CCEP-I: Making the Global Local - Unusual Weather Events as Climate Change Educational Opportunities

CCEP-I: Making the Global Local - Unusual Weather Events as Climate Change Educational Opportunities
CCEP-I:将全球异常天气事件作为气候变化教育机会
批准号:
1043235
负责人:
Edward Maibach
金额:
$99.89万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-15 至 2013-08-31

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中文摘要
翻译
乔治梅森大学被授予与美国气象学会、国家海洋和大气管理局、美国地球物理联合会、美国国家气候学家协会和气候中心合作建立第一阶段气候变化教育伙伴关系(CCEP)的奖项。CCEP第一阶段项目的总体目标是建立一个以区域或主题为基础的合作伙伴关系协调的国家网络,致力于增加与气候变化科学及其影响相关的有效,高质量的教育计划和资源的采用。该项目将重点建立一个由广播气象学家、气候科学家、大学研究项目和主要气候和天气科学组织组成的全国网络,以吸引、培训和授权当地广播气象学家教育和告知美国公众有关气候的信息。具体目标包括:(1)确定资源、培训和其他形式的支持,使天气预报员能够将有关气候变化的信息纳入其广播;(2)在那些对人为气候变化的真实性仍未确定的天气预报员中,确定(a)他们犹豫不决的性质,以及(b)帮助他们得出符合科学共识的结论的机会;(3)在拒绝科学共识的预报员和接受科学共识的预报员之间建立一个冲突分析和解决过程的原型,以便(a)了解他们之间的差异和相互作用的模式,(b)制定调解他们关切的框架;(4)与现有的气候和天气相关的公民科学项目建立联系,使天气播报员能够让观众参与到气候和天气科学中来;(5)确定气象本科学生和证书考生的气候科学教学课程和课程开发需求;(6)设计一个五年实施计划,该计划(a)将目标1-5的成就转化为一项能够实现我们目标的举措,(b)在财务上是可持续的。有关该项目的更多信息,请访问http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503465或联系PI。
英文摘要
An award has been made to George Mason University to establish a Phase I Climate Change Education Partnership (CCEP) in collaboration with the American Meteorological Society, National Oceanic & Atmospheric Administration, American Geophysical Union, American Association of State Climatologists, and Climate Central. The overall goal of the CCEP Phase I project is to establish a coordinated national network of regionally- or thematically-based partnerships devoted to increasing the adoption of effective, high quality educational programs and resources related to the science of climate change and its impacts. This project will focus on establishing a national network of on-air broadcast meteorologists, climate scientists, university research programs, and key climate and weather science organizations, to engage, train, and empower local broadcast meteorologists to educate and inform the American public about climate. The specific goals include (1) identifying the resources, training and other forms of support that will enable weathercasters to incorporate relevant information about climate change into their broadcasts; (2) identifying, among those weathercasters still undecided about the reality of anthropogenic climate change, (a) the nature of their indecisions, and (b) opportunities to help them reach a conclusion consistent with scientific consensus; (3) developing a prototype conflict analysis and resolution process between weathercasters who reject the scientific consensus and those who accept it so as to (a) understand their differences and the patterns of interactions and (b) develop frameworks that mediate their concerns; (4) developing linkages with existing climate- and weather-related citizen science programs to enable weathercasters to involve their viewers in climate and weather science; (5) identifying curriculum and curriculum development needs for teaching climate science to undergraduate meteorology students and certificate candidates; and (6) designing a five-year implementation plan that (a) leverages the accomplishments of objectives 1-5 into an initiative that can achieve our goal and (b) is financially sustainable. More information on this project is available by visiting http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503465, or contacting the PI.
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Weather and Climate in the Newsroom: Expanding the Climate Matters Network and Its Science Communication Research
  • 批准号:
    1713450
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $290.19万
  • 财政年份:
    2017
  • 负责人:
    Edward Maibach
  • 依托单位:
TV Weathercasters and Climate Education: Expanding the Reach of Climate Matters
  • 批准号:
    1422431
  • 项目类别:
    Standard Grant
  • 资助金额:
    $299.82万
  • 财政年份:
    2014
  • 负责人:
    Edward Maibach
  • 依托单位:
Enabling TV Meteorologists to Provide Viewers with Climate Change-related Science Education Based on ISE 'Best Practices'
  • 批准号:
    0917566
  • 项目类别:
    Standard Grant
  • 资助金额:
    $106.04万
  • 财政年份:
    2009
  • 负责人:
    Edward Maibach
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis