Application of Nonlocal Mixing Concepts to Model Initializa-tion and Forecasting

为了使大气数值模式提供准确的预报，许多因素都是重要的。其中之一就是必须充分说明模式初始时刻的大气状态。一个常见而困难的问题是如何解释在初始模式时间存在的天气现象（例如中尺度模式中的单个雷暴）对热量、动量和水分的再分配。如果没有充分说明这些条件，数值模式在其预报期的早期阶段往往是不准确的。这就是所谓的“自旋上升”问题。在区域（中尺度）模式的初始化过程中，首席研究员将使用一种被称为非局部混合的新技术来继续和扩展早期的研究。理论和数值研究都将用于利用现有的高分辨率研究数据集开发和验证适当的模型技术。如果成功，本研究结果将对缓解6 ~ 12小时中尺度模式的“自旋上升”问题大有裨益。

Many factors are important in order for numerical models of the atmosphere to provide accurate forecasts. One of these is the fact that the state of the atmosphere at the initial time of the model must be adequately specified. A common and difficult problem is how to account for the redistribution of heat, momentum and moisture by weather phenomena that exist at initial model time (e.g. individual thunderstorms in mesoscale models). When such conditions are not specified adequately, numerical models tend to be inaccurate in the early stages of their forecast period. This is known as the "spin-up" problem. The Principal Investigator will pursue and extend earlier investigations using a new technique, known as non-local mixing, in the initialization process for regional (mesoscale) models. Both theoretical and numerical research will be used in the development and verification of appropriate model techniques using existing high resolution research data sets. If successful, the results from this study will be of great benefit for alleviating the "spin-up" problem in mesoscale models in the six to twelve hour time range.