Variational Data Assimilatin with the NMC Spectral Model

用 NMC 谱模型进行变分数据同化

• 批准号: 9102851
• 负责人: Ionel Navon
• 金额: $ 31.57万
• 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: 1991
• 资助国家: 美国
• 起止时间: 1991-06-01 至 1994-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9102851&HistoricalAwards=false
• 关键词: Variational; Data; Assimilatin; NMC; Spectral

One tactic for potential improvement of Numerical Weather Forecasting (NWF) is the timely incorporation of large bodies of new observational data. These will become available from networks of Doppler radars and other automated instrumental systems that are to come on line throughout the remainder of this decade. The oppor- tunity will be enhanced with the concurrent growth of computer data-handling power. Also, and just as important, will be recent developments in applied mathematics, particularly in the theory of optimal control. By the mathematical analysis of the equations (or computational algorithms) governing the behaviour of evolving systems, it is possible to identify key values whose adjustment will minimize undesirable properties of the system. However, the analysis becomes extremely difficult as the governing equations become complex, and even if it is successfully carried out, there remains the problem of implementing its results quickly and cheaply enough to be useful in practice. In the case of NWF, the procedure can be applied to the problem of adjusting the initial meteorologi- cal fields that start a forecast so that the differences between the predictions of a numerical model and measurements coming in early in the forecast period are minimized. This is done by making repeated trial predictions for a short period, while simulteneously and repeatedly fine tuning the initial fields until the desired minimum error is found. The adjusted initial field is then used to carry out the prediction over the full forecast period. The model of concern to the PIs is the workhorse operational weather prediction model of the US National Meteorological Center (NMC). In a grant just ending under the NSF-NMC Joint Program in NWF they have successfuly constructed a set of algorithms (the "adjoint model") that complement the NMC computer program in such a way that by alternately using the adjoint and prediction models during the trial prediction period the number of iterations can be significantly reduced. This has been characterized by the Director of Development of NMC as a "most significant achievement". The proposed work is to exploit the breakthrough by further analysis of the properties of the two models in order to achieve successful operational implementation of the theoretical findings. This could enhance the accuracy of the weather forecasts to levels equal to or exceeding those achieved at the European Center for Medium Range Weather Forecasting, and increase the return on the national investment in the next-generation weather observing network.

数值天气预报(NWF)潜在改进的一个策略是及时纳入大量新的观测数据。这些将通过多普勒雷达网络和其他自动化仪器系统提供，这些系统将在本十年的剩余时间内投入使用。随着计算机数据处理能力的同时增长，机会将会增加。此外，同样重要的是应用数学的最新发展，特别是最优控制理论。通过对控制演化系统行为的方程(或计算算法)的数学分析，可以确定其调整将使系统的不良特性最小化的关键值。然而，随着控制方程变得复杂，分析变得极其困难，即使成功地进行了分析，也存在着快速和廉价地实施其结果以在实践中有用的问题。在数值预报的情况下，该方法可应用于调整开始预报的初始气象场的问题，从而使数值模式的预报与预报初期的实测值之间的差异最小化。这是通过在短时间内进行重复尝试预测来实现的，同时反复微调初始场，直到找到所需的最小误差。然后使用调整后的初始场在整个预测期内进行预测。PIS关注的模式是美国国家气象中心(NMC)的主要业务天气预报模式。在NSF-NMC联合计划下刚刚结束的一项拨款中，他们成功地构建了一套算法(“伴随模型”)，以这样一种方式补充NMC计算机程序，即通过在试验预测期间交替使用伴随模型和预测模型，可以显著减少迭代次数。NMC的发展主任称这是一项“最重大的成就”。建议的工作是通过进一步分析这两种模型的特性来挖掘突破口，以实现理论研究成果的成功实施。这可以将天气预报的准确性提高到等于或超过欧洲中期天气预报中心的水平，并增加国家对下一代天气观测网络的投资回报。