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，它将对大气的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度的显著提高可以归因于通过数据同化获得的当前天气估计的精度提高。讲习班将促进数据同化方面的专家之间的协作和讨论，包括利用机器学习提高数据同化的性能、如何以最佳方式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和大气。讲习班的主要目标是增进对数据同化和敏感性分析及其在天气预报和气候估计中的应用的基本了解，以造福于社会。将为研究生参加讲习班、直接向科学界学习并介绍他们自己的研究成果提供旅行支助。讲习班参加者将介绍、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。发言和讨论的主题将包括开发计算效率高的敏感性分析技术，包括机器学习、诊断工具，以及审议影响敏感性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和动力分析、再分析和观测影响估计中的应用。演讲将包括关于基本原理和最新发展道路的概述演讲，以及关于新工作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优势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.