U.S.-Germany Cooperative Research on Optimal Control MethodsApplied to the Study of Mesoscale Meteorological Model Sensitivity and Data Assimilation

美德合作研究最优控制方法应用于中尺度气象模型灵敏度和资料同化研究

• 批准号: 9022892
• 负责人: Douglas Lilly
• 金额: $ 0.97万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-08-15 至 1993-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9022892&HistoricalAwards=false
• 关键词: U.S.; Germany; Cooperative; Research; Optimal

This award supports Professors Douglas K. Lilly and John M. Lewis of the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS), University of Oklahoma, to collaborate with Dr. Hartmut Kapitza and others of the GKSS National Research Center, in Geesthacht, Germany. The objective of their collaboration is to apply the principles of optimal control to improve the modelling of mesoscale phenomena and to improve the manner in which models and data are matched. In particular, they will work on extending a promising new technique called the "adjoint" method, that was originally developed by Dr. Karpita. He and his colleagues at GKSS have special expertise in modeling that is well matched by the U.S. group's expertise in the use of data from new observational systems being introduced in the U.S. that include sophisticated ground-based remote sensing techniques. The U.S. group will be able to help scientists at the GKSS to introduce realistic mesoscale data into the model, while research in data assimilation at CIMMS will benefit greatly from the opportunity to use the model and its adjoint on a variety of mesoscale meteorological problems. Deterministic models used in mesoscale meteorology are among the most complicated used in science today. The physical scales that must be included in the models range from the larger scale ageostrophic circulation around fronts to the microphysics of precipitation processes. This is further complicated by the need to interface with large scale models and to obtain boundary conditions. The observational demands of such systems are substantial, and the compilation and analysis challenge is extraordinary. The University of Oklahoma now has an NSF-funded Science and Technology Center, called "Center for the Analysis and Prediction of Storms" (CAPS.) The kind of data assimilation work being advanced by Dr. Kapitza and the German center is now one of the principal foci of the CAPS program plan.

该奖项支持俄克拉荷马大学中尺度合作研究所气象研究所（CIMMS）的道格拉斯·K·莉莉和约翰·刘易斯教授与Hartmut Kapitza博士和其他GKSS国家研究中心，德国Geesthacht的其他国家研究中心合作。 他们协作的目的是应用最佳控制原则来改善中尺度现象的建模并改善与模型和数据相匹配的方式。 特别是，他们将致力于扩展一种有希望的新技术，称为“伴随”方法，该方法最初由Karpita博士开发。 他和他在GKSS的同事在建模方面具有特殊的专业知识，该专业知识与美国集团在美国引入的新观察系统的数据中的专业知识非常匹配，其中包括精致的基于地面的遥感技术。 美国小组将能够帮助GKSS的科学家将逼真的中尺度数据引入模型，而CIMMS数据同化的研究将从机会及其在各种中尺度气象问题上使用模型及其伴随的机会受益匪浅。 中尺度气象学中使用的确定性模型是当今科学中最复杂的模型。 模型中必须包含的物理尺度范围从围绕前部的较大年龄循环到降水过程的微物理学。 与大规模模型接口并获得边界条件的需要更加复杂。 此类系统的观察需求是巨大的，并且汇编和分析挑战是非凡的。 俄克拉荷马大学现在拥有一个由NSF资助的科学技术中心，称为“暴风雨分析和预测中心”（CAPS。）KAPITZA博士和德国中心正在提出的数据同化工作类型，现在是CAPS计划计划的主要重点之一。