A Climatological Study of Atlantic Basin Tropical Storms to Provide Satellite Data Assimilation Guidance for Numerical Models

大西洋盆地热带风暴气候学研究为数值模型提供卫星资料同化指导

• 批准号: 0543330
• 负责人: Zafer Boybeyi
• 金额: $ 38.09万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0543330&HistoricalAwards=false
• 关键词: Climatological; Study; Atlantic; Basin; Tropical

The threat to life and property from Tropical Cyclones/Hurricanes (referred to hereafter as TCs) is well recognized by the scientific community and the general public. To better understand and predict a hurricane's track and its intensity, it is important to consider climate factors that influence those characteristics. While the TC forecasting has received significant attention both on the scales of weather and climate prediction, it has yet to be comprehensively studied on both scales when coupled together. The PI will conduct a climatological study for Atlantic basin TCs using the past 100-year TC record (1900-2004) to provide satellite data assimilation guidance (i.e., targeted data assimilation strategy) for numerical weather prediction (NWP) models. Existing climatological TC records will be analyzed to create a database that serves as a basis for satellite data assimilation guidance. Then the developed data assimilation guidance will be implemented and validated in an NWP model. Finally, TC intensity forecasting with the aid of NWP models that use unstructured adaptive and nested grid techniques coupled with the data assimilation methodology will be studied in detail. Broader Impacts: The research will improve TC and medium-range weather forecasting that is useful to the academic and operational modeling communities.

热带气旋/飓风（以下简称TC）对生命和财产的威胁已得到科学界和公众的充分认可。为了更好地了解和预测飓风的路径和强度，重要的是要考虑影响这些特征的气候因素。虽然TC预报在天气和气候预报两个尺度上都受到了极大的关注，但当两者结合在一起时，它还没有得到全面的研究。PI将使用过去100年TC记录（1900-2004）对大西洋盆地TC进行气候学研究，以提供卫星数据同化指导（即，有针对性的数据同化策略）的数值天气预报（NWP）模式。将分析现有的气候TC记录，以建立一个数据库，作为卫星数据同化指导的基础。然后，开发的数据同化指导将实施和验证数值预报模式。最后，将详细研究在数值预报模式的帮助下进行TC强度预报，该模式使用非结构化自适应和嵌套网格技术，并结合数据同化方法。 更广泛的影响：这项研究将改善TC和中期天气预报，这对学术和业务建模界都很有用。