U.S.-France Cooperative Research: Variational Data Assimi- lation Using Optimal Control Methods

该奖项将支持佛罗里达州立大学的I.M.Navon博士和James O‘Brien博士与两名法国研究人员之间的合作研究：布莱斯-帕斯卡大学数学系的F.X.LeDimet教授，Clermont-Ferrand教授，以及巴黎诺梅尔高等学院动态气象学实验室的Olivier TALAGRAND教授。该项目的目标是研究气象学和海洋学数据同化的最优控制变分方法，目的是提高大气和海洋模式的精度和可预测性。在全球预报模式中，数值预报误差的产生主要是由于初始条件的误差以及模式的动力学和物理上的缺陷。在不久的将来，由于更好的物理参数化或全球观测系统精度的数据改进，预计数值天气预报(NWP)的潜在改进将会发生。伴随模式作为最优控制理论在资料同化中的应用，在最近几年得到了蓬勃的发展。美国和法国的调查人员在过去的合作中开发了NMC光谱模型的伴随项。在这个项目中，他们将专注于算法优化、效率和复杂问题的可伸缩性领域的相关理论问题。此外，他们还将研究在伴随数值预报中包含物理大气处理器的变分数据同化，如辐射和水汽热力学。该项目将受益于研究人员在最优控制理论和气象学方面的互补专业知识。这项研究的结果将对提高中期数值天气预报模式的可预测性和利用即将推出的各种卫星数据具有重要意义。

This award will support collaborative research between Drs. I.M. Navon and James O'Brien, Florida State University, and two French researchers: Professor F.X. LeDimet, Department of Mathematics, Blaise-Pascal University, Clermont-Ferrand, and Professor Olivier Talagrand, Laboratory for Dynamic Meteoro- logy, Ecole Normale Superieure, Paris. The objective of the project is to investigate optimal control variational methods for data assimilation in meteorology and oceanography with the aim of improving accuracy and predictability of atmospheric and oceanic models. In global forecasting models, errors arise in the numerical forecast mainly due to errors in the initial conditions and deficiencies in the dynamics and physics of the models. Potential improvements in numerical weather prediction (NWP) in the near future are expected to occur as a result of either better physical parameterizations or from data improvement in the accuracy of global observing systems. The use of adjoint models as an application of the theory of optimal control for data assimilation has seen a vigorous development in the last few years. The US and French investigators in past collaboration have developed the adjoint of the NMC spectral model. In this project, they will focus on related theoretical questions in the area of optimization of algorithms, efficiency and scaling of complex problems. In addition, they will study the inclusion in the adjoint NWP of variational data assimilation of physical atmospheric processors, such as radiation and moisture ther- modynamics. This project will benefit from the complementary expertise of the investigators in optimal control theory and meteorology. The results of this research will be important for enhancing the medium-range numerical weather forecasting model's predictability and for taking advantage of various satellite data which will shortly become available.