The Twelfth (12th) Workshop on Meteorological Sensitivity Analysis and Data Assimilation; Lake George, New York; May 19-24, 2024

第十二届（十二届）气象敏感性分析与资料同化研讨会；

• 批准号: 2330336
• 负责人: Isaac Moradi
• 金额: $ 1.6万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330336&HistoricalAwards=false
• 关键词: Twelfth; 12th; Workshop; Meteorological; Sensitivity

Data assimilation plays a crucial role in weather forecasting by combining observations of the atmosphere with numerical model data to determine the current state of the atmosphere. This is important because the current state of the atmosphere is used as a starting point for the weather forecasts, and the more accurate the starting point is, the better the weather forecast will be. A significant improvement in the accuracy of weather forecasts over the past few decades can be attributed to the enhanced precision of current weather estimates obtained through data assimilation. The Workshop will enable collaboration and discussion between experts in data assimilation, including topics such as using machine learning to enhance the performance of data assimilation, how observations can be optimally used to improve weather forecasts, and how data assimilation methods can be used for the ocean as well as for the atmosphere. The workshop's main goal is to advance fundamental understanding of data assimilation and sensitivity analysis, and their applications to weather forecasting and climate estimation for societal benefit. Travel support will be provided for graduate students to attend the Workshop to learn directly from the scientific community and to present their own research. The Workshop participants will present, review, discuss, and evaluate works pertaining to atmospheric sensitivity analysis and data assimilation, but will include representation from oceanography, coupled atmosphere-ocean systems, and other fields that employ similar techniques. Topics for presentation and discussion will include development of computationally efficient techniques for sensitivity analysis, including machine learning, diagnostic tools, and consideration of statistical issues affecting sensitivity analysis or data assimilation. Applications of data assimilation to synoptic and dynamic analysis of weather events, to reanalyses, and to estimation of observation impacts will be evaluated. Presentations will include overview talks on fundamentals and recent paths of development, and posters or short seminars on new works.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据同化通过将大气观测与数值模型数据相结合来确定大气的当前状态，在天气预报中发挥着至关重要的作用。这很重要，因为当前的大气状态被用作天气预报的起点，起点越准确，天气预报就越好。过去几十年天气预报准确性的显着提高可归因于通过数据同化获得的当前天气估计精度的提高。该研讨会将使数据同化专家之间进行协作和讨论，包括利用机器学习提高数据同化性能、如何优化利用观测来改进天气预报以及如何将数据同化方法用于海洋等主题以及气氛。该研讨会的主要目标是增进对数据同化和敏感性分析的基本理解，及其在天气预报和气候估计中的应用，以实现社会效益。将为参加研讨会的研究生提供差旅支持，以便直接向科学界学习并展示自己的研究成果。研讨会参与者将介绍、回顾、讨论和评估与大气敏感性分析和数据同化有关的工作，但将包括海洋学、大气-海洋耦合系统以及采用类似技术的其他领域的代表。演示和讨论的主题将包括开发用于敏感性分析的计算有效技术，包括机器学习、诊断工具以及影响敏感性分析或数据同化的统计问题的考虑。将评估数据同化在天气事件的天气和动态分析、再分析和观测影响估计中的应用。演讲内容将包括关于基础知识和最新发展路径的概述演讲，以及关于新作品的海报或简短研讨会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。