全球数值天气预报谱模式随分辨率的提高，计算复杂性大幅增长，同时通信开销也大幅增长，特别是随处理器的不断增加，通信开销的影响越来越大，导致无法在大规模并行计算机上进行高效执行，对数值天气预报的实时性要求形成严峻挑战，从而成为当前国际上的研究热点之一。本项目从存储访问、通信隐藏、算法改进等三个层面，以现有全球数值天气预报谱模式为依托，在国际上全球谱模式已有高性能并行计算技术的基础上，重点研究谱变换的高效可扩展单边通信、计算与通信的高效重叠优化、半Lagrange方案并行计算的自适应通信、水平导数从谱空间到格点空间的迁移等技术，以最大程度减少通信开销的影响，提高全球谱模式的计算效率与可扩展性，为我国未来在E级计算机系统上采用超高分辨率谱模式进行数值天气预报，从而进一步提高预报准确性奠定并行计算技术基础。

With the improvement of the resolution, the computation complexity of the global spectral numerical weather prediction model increases rapidly. At the same time, the communication overhead increases rapidly too. Especially, with more and more processors used, the influence of the communication overhead is more and more serious, which prohibits the highly efficient execution on large-scale parallel computers and becomes a serious challenge to real-time requirement. Thus, it has become one of the hot researches in the world.. In this project, we will investigate from three levels, including memory access, communication hiding, and algorithm improvement, based on the available global spectral model and the known advanced high-performance computational techniques in this area over the world. Four topics will be focused on, that is, the highly scalable one-sided communication for spectral transforms, the highly efficient overlapping of computation and communication and its optimization, the self-adaptive communication in the parallel computation of semi-Lagrange scheme, and the migration of horizontal derivatives from spectral space to grid point space. The objective is to reduce the influence of the communication overhead as largely as possible and improve the computation efficiency and the scalability of the global spectral model, which is the basis to run spectral numerical weather prediction with very high resolution on future exascale computer systems, and to improve the accuracy of the global numerical weather prediction.