Determination of the Adjoint Model of the NMC Global and NGMModels and Their Application to 4-D Data Assimilations

NMC Global和NGM模型伴随模型的确定及其在4维数据同化中的应用

• 批准号: 8806553
• 负责人: Ionel Navon
• 金额: $ 35.15万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-06-01 至 1991-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8806553&HistoricalAwards=false
• 关键词: Determination; Adjoint; Model; NMC; Global

"Determination of the Adjoint Models for NMC's Global and Nested Grid Models and Their Application to Four Dimensional Data Assimilation" A numerical weather prediction model requires a complete and accurate specification, or analysis, of the state of the atmosphere at the initial time. The process of introducing observations which are distributed in time into a forecast model is known as four dimensional data assimilation. Observations cannot perfectly describe the state of the atmosphere at a given time. Ideally, an assimilation should produce the best "fit" of the data observations while satisfying the dynamical properties of the governing equations as embodied in the model. Under this award, the PI and his colleagues will employ an assimilation method which uses optimal control theory to minimize the differences between the evolution of the observations and the dynamical constraints of the model. This technique requires the determination of the adjoint of the models employed, in this case, models used at the National Meteorological Center (NMC). Determination of the adjoint model of a particular forecast model is a complex numerical problem. The PI has developed a staged approach, working on models of increasing complexity. At each stage, the adjoint method for four dimensional assimilation will be tested in cooperation with NMC scientists. In the course of the research, the PIs will have to develop new methods for dealing with problems introduced by the use of more complex models. For example, determination of the adjoint models requires that quantities be differentiable, but certain physical processes, such as convection, are not. This research is being supported as part of an NSF-NMC cooperative effort to support basic research in numerical weather prediction.

“NMC的全局和嵌套的伴随模型的确定 网格模型及其在四维数据中的应用 同化” 数值天气预报模式需要一个完整的， 准确的规格，或分析，状态的 大气层在最初的时间。 化进程 在时间上分布到预测模型中的观测值 被称为四维数据同化。 意见 不能完美地描述在给定的大气状态 时间 理想情况下，同化应该产生最佳的“适应”， 在满足动力学性质的前提下， 模型中的控制方程。 根据本 奖，PI和他的同事将采用同化 方法，使用最优控制理论，以尽量减少 观察结果的演变与 模型的动态约束。 这种技术需要 确定所采用的模型的伴随，在此 案例，国家气象中心（NMC）使用的模型。 特定预报的伴随模式的确定 模型是一个复杂的数值问题。 PI开发了一个 分阶段的方法，在日益复杂的模型上工作。 在 每一阶段，四维同化的伴随方法 将与NMC科学家合作进行测试。 过程中 在研究中，PI将不得不开发新的方法， 处理由使用更复杂的 模型 例如，伴随模型的确定 要求量是可微的，但某些物理量 而对流等过程则不然。 这项研究是作为NSF-NMC的一部分得到支持的 合作支持数值天气基础研究 预测.