国际标准期刊号: 2157-7617

地球科学与气候变化杂志

开放获取

我们集团组织了 3000 多个全球系列会议 每年在美国、欧洲和美国举办的活动亚洲得到 1000 多个科学协会的支持 并出版了 700+ 开放获取期刊包含超过50000名知名人士、知名科学家担任编委会成员。

开放获取期刊获得更多读者和引用
700 种期刊 15,000,000 名读者 每份期刊 获得 25,000 多名读者

索引于
  • CAS 来源索引 (CASSI)
  • 哥白尼索引
  • 谷歌学术
  • 夏尔巴·罗密欧
  • 在线访问环境研究 (OARE)
  • 打开 J 门
  • Genamics 期刊搜索
  • 期刊目录
  • 乌尔里希的期刊目录
  • 访问全球在线农业研究 (AGORA)
  • 国际农业与生物科学中心 (CABI)
  • 参考搜索
  • 哈姆达大学
  • 亚利桑那州EBSCO
  • OCLC-世界猫
  • 普罗奎斯特传票
  • SWB 在线目录
  • 普布隆斯
  • 欧洲酒吧
  • ICMJE
分享此页面

抽象的

Climate Change Impact on Probability Analysis of Hurricanes

Keshtpoor M*, Mark Osler

Coastal flood risk due to cyclonic storms is a significant topic of concern for coastal communities. Planning and engineering efforts within these communities often require estimates of water surface elevations associated with specific return periods. In order to generate the surge elevations for prospective return periods, Joint Probability Method (JPM) techniques are often used [1,2]. Within a JPM approach, statistical representations of cyclonic storm characteristics (i.e., storm frequency, intensity, and radius to maximum wind) are parameterized, along with an associated probability distribution for each parameter. The probability distributions for each of the major hurricane characteristics are based on local historical climatology. A key assumption in the development of the probability distribution for the storm parameters is that each is statistically stationary. Global climate models suggest that characteristics of cyclonic storms may be impacted by climate change. Such changes would challenge the assumption of statistical stationary within the traditional JPM approach. Here, a straightforward windowing approach is proposed to account for possible variation of characteristics. This approach results in more recent storm events having a larger impact on the probability distribution of storm parameters, should such an adjustment be judged necessary by the JPM practitioner. The application of the proposed approach is demonstrated by applying on sample data sets of hurricanes at the mid-Atlantic region and South Florida.

免责声明: 此摘要通过人工智能工具翻译,尚未经过审核或验证。