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.

数据同化在天气预测中起着至关重要的作用，通过将大气的观测与数值模型数据相结合以确定大气的当前状态。这很重要，因为大气的当前状态被用作天气预报的起点，而起点越准确，天气预报的越好。在过去几十年中，天气预报的准确性有了显着提高，这可以归因于通过数据同化获得的当前天气估计值的提高。研讨会将使数据同化专家之间的合作和讨论，包括使用机器学习来增强数据同化的性能，如何最佳地使用观察来改善天气预测以及如何将数据同化方法用于海洋以及气氛。研讨会的主要目标是提高对数据同化和敏感性分析的基本了解，以及它们在天气预报和气候估计中的应用对社会利益的估计。将为研究生提供旅行支持，以便参加研讨会，直接向科学界学习并展示自己的研究。研讨会参与者将介绍，审查，讨论和评估与大气灵敏度分析和数据同化有关的作品，但包括海洋学，耦合大气 - 海洋系统以及其他采用类似技术的领域的代表。介绍和讨论的主题将包括开发用于敏感性分析的计算高效技术，包括机器学习，诊断工具以及对影响灵敏度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概要和动态分析，重新分析以及估计观察影响的应用。演讲将包括有关基本面和最新发展途径的概述，以及有关新工作的海报或简短的研讨会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来通过评估来支持的。