Variational Data Assimilatin with the NMC Spectral Model

一种可能改善数值天气的策略 预测（NWF）是及时将大量的 新的观测数据。这些将通过网络提供 多普勒雷达和其他自动化仪器系统， 在这十年的剩余时间里上线。机会- 随着计算机的同时发展， 数据处理能力同样重要的是， 应用数学的发展，特别是在理论上， 最佳控制。通过对方程的数学分析（或 计算算法）控制进化的行为 系统，可以确定其调整的关键值 将最小化系统的不期望的特性。但 由于控制方程 变得复杂，即使成功地进行， 如何快速而廉价地实施其成果仍然是一个问题 足以在实践中发挥作用。就NWF而言， 可以应用于初始气象条件的调整问题。 计算开始预测的字段， 数值模型的预测和测量结果 在预测期内，最大限度地减少。这是通过使 在短时间内重复试验预测，同时 并反复微调初始场，直到所需的 找到最小误差。调整后的初始字段然后用于 在整个预测期内进行预测。 PI关注的模型是主力操作 美国国家气象中心的天气预报模式 （NMC）。在刚刚结束的NSF-NMC联合计划下， NWF他们已经成功地构建了一套算法（ “伴随模型”），其在这种情况下补充NMC计算机程序。 通过交替使用伴随模型和预测模型 在试验预测周期期间，迭代次数可以是 大幅减少。主任指出， 国家气象中心的发展是“最重要的成就”。的 建议的工作是通过进一步分析 这两种模型的性质，以实现成功的 理论研究成果的可操作性。这可能 提高天气预报的准确性， 超过了欧洲中程导弹中心所取得的成就， 天气预报，提高对国家的回报 下一代天气观测网络的投资。

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.