Center for Analysis and Prediction of Storms (CAPS)

俄克拉荷马大学(OU)与美国国家海洋和大气管理局(NOAA)合作提出的这项提案要求提供资金，以建立一个风暴分析和预测科学技术中心。该中心主任将由道格拉斯·K·礼来教授担任。必须开发新一代模型来解决风暴规模的预测问题，目前美国任何地方都没有足够的科学家来开展这项研究。该中心将填补这一空白，并将成为对这一领域感兴趣的其他研究人员的中心。国家大气研究中心(NCAR)和其他大学正在进行补充建模工作，但这些相关研究工作中使用的模型不能为OU中心所需的多尺度任务提供足够的框架。该中心计划加强他们与NCAR和其他大学研究人员的工作关系，特别是通过交流教职员工和学生。初步研究将从三个方向进行：模式开发；可预测性研究和敏感性测试；以及数据同化和初始化研究。要解决的问题范围包括基础理论、实验和操作测试。这类模型的成功必然需要使用以前没有应用于大气模型的先进数值技术；许多技术将迫使现有模型的框架发生彻底改变。此外，同化/初始化问题将需要一个不同于迄今使用的理论框架。最后，通过开发一种新的模型，可以充分利用快速发展的计算机体系结构，特别是大内存和高度并行的超级计算机。这项研究结合了俄亥俄大学的多学科专业知识。必须对许多现象的主导物理，例如云动力学和表面能量平衡进行研究和模拟。然后，必须使用准确模拟所涉及的流动的数值程序，将该模式并入一般的中尺度预报能力。有了所设想的方法，计算机就像一个“专家系统”一样，可以改变其数值算法及其空间和时间分辨率，以响应领域中可观察到的变化。所有这些都必须使用实际数据的输入进行测试，而这些数据的初始化和同化是该过程的关键，从而使模拟的预测能力能够对照测量的大气过程进行测试和评估。

This proposal from the University of Oklahoma (OU) in collaboration with the National Oceanic and Atmospheric Administration (NOAA) requests funds to establish a Science and Technology Center for Analysis and Prediction of Storms. The Director of the Center will be Professor Douglas K. Lilly. A new generation of models must be developed to address the storm-scale prediction problem and there is currently no critical mass of scientists anywhere in the U.S. to carry out this research. The Center will fill this gap and will serve as a focal point for other researchers interested in this area. Complementary modelling work is being carried out at the National Center for Atmospheric Research (NCAR) and other universities, but the models used in these related research efforts do not provide adequate frameworks for the multi-scale tasks required in the OU Center. The Center plans to enhance their working relationships with NCAR and other university researchers, especially through exchange of faculty, staff, and students. The initial research will proceed in three directions: model development; predictability research and sensitivity testing; and data assimilation and initialization research. The scope of the problems being addressed spans basic theory, experimentation, and operational testing. The success of such models will necessarily require the use of advanced numerical techniques not previously applied to atmospheric modelling; many will force a complete change in the framework of existing models. In addition, the assimilation/initialization problem will require a different theoretical framework than has been heretofore used. Finally, by developing a new model, full advantage can be taken of rapidly evolving computer architectures, particularly the large memory and highly parallel supercomputers. The research couples the multidisciplinary expertise at OU. The governing physics of many phenomena, e.g., cloud dynamics and surface energy balance, must be investigated and modelled. The model must then be incorporated into a general mesoscale predictive capability, using numerical procedures which accurately simulate the flows involved. With the methods envisioned, the computer is allowed as an "expert system" to alter its numerical algorithm and its spatial and temporal resolution, in response to observable changes in the fields. All of these must be tested using the inputs from real data, and the initialization and assimilation of these data are the linchpins of the process, allowing the predictive capability of the simulation to be tested and evaluated against measured atmospheric processes.