Workshop on Meteorological Sensitivity Analysis and Data Assimilation; Roanoke, West Virginia; June 1-5, 2015

该基金仅用于支持参加气象敏感性分析和数据同化讲习班的学生。预计75名与会者将介绍，审查，讨论和评估与大气敏感性分析和数据同化有关的工作，但将包括海洋学和其他采用类似技术的领域的代表。该基金将为至少8名学生或博士后提供部分支持。研讨会将在一个相当偏远的独立设施中举行，所有与会者将在那里共享膳食和夜晚，为与专家和专家之间的对话提供了许多机会，以最大限度地利用我们的时间。 演讲将包括基础教程，最近发展道路的概述，新作品的海报或简短研讨会以及一些非正式辩论。学生们也将有机会展示自己的作品。智力价值：这项工作的智力价值在于扩展了模型调整的概念，使其与空间验证的基本原则保持一致。该框架将通过允许用户调整模型参数以具有理想的空间特征来改进预测。 PI的空间技术的发展，以及敏感性分析方法适合这个问题。更广泛的影响：更广泛的影响的研究是多管齐下的。参与该项目的学生将成为数值预报模型中一些最复杂主题的专家：空间验证，灵敏度分析和模型调整。这个高度跨学科的框架本身具有普遍适用性，因为它可以用来改善任何数值模型的预测，涉及不精确的物理过程参数化或不精确的物理常数，包括天气，气候，海洋等知识，鉴于空间验证正在出现的验证方法的选择，为未来的预测，该方法将有广泛的用途。该方法将以期刊文章和计算机代码的形式传播，这些代码将包含在国家大气研究中心（NCAR）制作的验证R包中。

This NSF fund is solely for support of student participants at the Workshop on Meteorological Sensitivity Analysis and Data Assimilation. The 75 expected participants will present, review, discuss, and evaluate works pertaining to atmospheric sensitivity analysis and data assimilation, but will include representation from oceanography and other fields that employ similar techniques. The fund will provide partial support for at least 8 students or postdocs who will attend. The workshop will be held at a fairly remote, self-contained facility at which all participants will share meals and evenings, providing many opportunities for dialogs with and between experts to make maximum use of our time. Presentations will include a mix of tutorials on fundamentals, overview talks on recent paths of developments, posters or short seminars on new works, and some informal debates. The students will also have an opportunity to present their own works.Intellectual Merit: The intellectual merit of the work is in extending the notion of model tuning to align it with the basic tenets of spatial verification. The framework will improve forecasts by allowing a user to tune the model parameters to have desirable spatial features. The PIs have contributed to the development of the spatial techniques as well as to the sensitivity analysis methods suitable for this problem.Broader Impacts: The broader impacts of the research are multi-pronged. The student involved in this project will become an expert in some of the most sophisticated topics in numerical prediction models: spatial verification, sensitivity analysis, and model tuning. This highly interdisciplinary framework itself has general applicability in that it can be employed to improve predictions from any numerical model involving inexact parameterizations of physical processes or inexact knowledge of physical constants, including weather, climate, ocean, etc. Given that spatial verification is emerging as the verification method of choice for future forecasts, the method will have wide-spread use. The method will be disseminated in the form of journal articles, and computer code to be included in a verification R package produced by National Center for Atmospheric Research (NCAR).