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

数据同化在天气预报中起着至关重要的作用，它将大气观测与数值模式数据相结合，以确定大气的当前状态。这一点很重要，因为大气的当前状态被用作天气预报的起点，起点越准确，天气预报就越好。过去几十年来，天气预报的准确性有了显著提高，这可归功于通过数据同化获得的当前天气估计的准确性有所提高。该讲习班将使数据同化专家之间能够进行合作和讨论，包括使用机器学习提高数据同化性能，如何最佳地利用观测来改善天气预报，以及如何将数据同化方法用于海洋和大气等主题。研讨会的主要目标是促进对数据同化和敏感性分析的基本理解，以及它们在天气预报和气候估计中的应用，以造福社会。将为研究生参加讲习班提供旅费支助，以便直接向科学界学习并介绍自己的研究成果。讲习班与会者将介绍、审查、讨论和评价与大气敏感性分析和数据同化有关的工作，但也将包括海洋学、大气-海洋耦合系统和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灵敏度分析的计算高效技术，包括机器学习、诊断工具以及考虑影响灵敏度分析或数据同化的统计问题。将评估数据同化在天气事件的天气学和动力学分析、再分析和估计观测影响方面的应用。演讲将包括关于基本原理和最近发展道路的概述讲座，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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.