Collaborative Research: Changes in Characteristics of Global Precipitation Since 1900 from Observations, Analyses, and Models: Mechanisms and Uncertainties

合作研究：1900年以来全球降水特征的观测、分析和模型变化：机制和不确定性

• 批准号: 1015926
• 负责人: Samuel Shen
• 金额: $ 27.39万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1015926&HistoricalAwards=false
• 关键词: Collaborative; Research; Changes; Characteristics; Global

This collaborative effort aims at conducting comprehensive diagnostics on precipitation variability, elucidating differences between observed and modeled precipitation data. Dynamic mechanisms and uncertainties associated with these differences will be explored and uncertainties will be quantified for both reconstructed and modeled data. The deliverables include (i) error estimation for global precipitation fields; (ii) precipitation dynamics, data analyses and corresponding uncertainty estimates; (ii) benchmark datasets of the reconstructed precipitation data on daily, monthly, seasonal, and annual scales over the globe and specific regions; (iii) feedback and guidance on reduction of systematic biases in model precipitation; and (iv) professional training of postdoctoral researchers, graduate students, undergraduate students, and high-school students with special attention to minority and underrepresented groups. This research effort has significant broader impacts: in addition to the training and outreach to underrepresented minority students, this activity has the potential of enabling stakeholders interested in precipitation optimize decisions and mitigate impacts due to droughts and floods.

这项合作努力的目的是对降水变异性进行全面诊断，阐明观测和模拟降水数据之间的差异。动态机制和不确定性与这些差异将被探讨和不确定性将被量化重建和建模数据。可交付成果包括：㈠全球降水场误差估计; ㈡降水动力学、数据分析和相应的不确定性估计; ㈡地球仪和特定区域每日、每月、季节和年度尺度重建降水数据的基准数据集; ㈢关于减少模式降水系统偏差的反馈和指导;以及（iv）对博士后研究人员、研究生、本科生和高中生进行专业培训，特别关注少数民族和代表性不足的群体。 这项研究工作具有广泛的影响：除了对代表性不足的少数民族学生进行培训和宣传外，这项活动还可能使对降水感兴趣的利益攸关方能够优化决策，减轻干旱和洪水造成的影响。