【作者】 朱玉祥；

【导师】 苗春生；

【作者基本信息】 南京气象学院 ， 气象学， 2004， 硕士

【摘要】 数值天气预报是现代天气预报的重要手段，中尺度模式MM5在很多气象台得到了广泛应用，随着我国多普勒天气雷达的普及，如何在数值预报模式中使用雷达资料以提高对中小尺度天气现象的预报准确率是摆在广大气象工作者面前的一个重要课题。四维变分同化方法作为一种提高数值天气预报的有效方法受到国内外专家的广泛关注，其重要特点是一个时次的数据可以影响到它以前时间的分析结果，因此不但可以为数值预报模式提供初始场，而且可以对观测资料不足的地区加以弥补。本文的工作就是多普勒雷达资料在MM5模式中的四维变分同化试验。文中首先在利用模拟风场比较各种雷达资料反演方法的优缺点后，选择变分分析法从雷达资料中反演风场，并根据雷达回波强度得到湿度场。接着介绍了气象资料同化的发展历史及主要方法，并重点强调了四维变分同化理论。然后详细阐述了本文同化试验所用的MM5四维变分同化系统中构造伴随模式的基本方法—伴随码技术以及伴随模式中目标函数的构造，及其权重、尺度因子、下降算法的选取。最后把雷达风场和雷达湿度场同化到MM5四维变分同化系统中。同化试验结果表明：同化空间分辨率很高的雷达风场后，能改善中小尺度降水的预报效果，并且能够得到常规观测资料所不能得到的中小尺度信息，对分析中小尺度系统结构具有重要意义；同化雷达湿度场效果不明显，可能与降水类型和同化时刻有关；而直接在初始时刻加入雷达湿度场，补充了常规资料在反映中小尺度系统方面的不足，增强了初始场中的水汽，有利于降水量的增加；同时同化雷达风场和雷达湿度场的试验表明，水汽的输送和局地的水汽辐合对于产生特大暴雨的贡献远大于仅有高湿中心的贡献。本文的试验结果还表明：要想提高客观定量的模式降水预报，仅有一个成熟的中尺度模式是不够的，资料的时空精度至关重要，而多普勒天气雷达资料在中尺度模式中的四维变分同化是解决资料时空精度问题的有效手段。更多还原

【Abstract】 Numerical weather prediction is an important means in present-day weather prediction. Mesoscale model MM5 has been used widely in many observatories. Along with a large number of Doppler radar stations have been built in our country, it is a crucial problem for the meteorologists how to use Doppler radar data in numerical weather prediction model. As an efficient approach to improve the accuracy of numerical weather prediction, Four-dimensional Variational Assimilation is widely focused on by the domestic and overseas experts. One of its important characteristics is that the data in one time can influence previousanalysis results, which can not only provide the optimized initial condition but also make up observation datum’s absence in some areas . This paper does the experiments of Doppler radar’s Four-dimensional Variational Assimilation in MM5 model. Firstly after comparing advantages and disadvantages of several methods by using the data of simulative wind, the paper chooses variational analysis method to retrive three-dimensional wind field. Also, the humility field is obtained from radar echo intensity. Secondly meteorologic datum assimilation history and major ways are introduced. Especially four-dimensional Variational Assimilation is emphasized. Thirdly we expound the basic methods how to construct the adjoint model including adjoint code technique, objective function formation, and how to adopt the weighting coefficient, scaling factor, descent arithmetic. Lastly radar wind field and radar humility field are assimilated in the MM5 4D Variational Assimilation System. The assimilation experimentation results indicate that after assimilating radar wind field of small spatial scale, mesoscale and small-scale precipitation prediction can be improved and mesoscale and small-scale information which can’tappear by tradition datum can be gained, which is valuable to analyze the mesoscale and small-scale system structure ; the effect assimilating radar humidity field isn’t obvious, which is perhaps correlation with precipitation types and assimilating time. The results also show that adding radar humidity field to initial condition at initial time can supply the gap of the regular data in reflecting the mesoscale and small-scale systems, strengthen the humidity in the initial field, and eventually help to improve precipitation. The experiment of assimilating radar wind field and radar humility field at the same time shows that vapor transportation and local vapor divergence play more significant role in causing excessively heavy rain than only high wet center. The paper’s experiments also reveal that it is far beyond the need of improving objective and quantitative precipitation prediction to have only a mature mesoscale model, in effect, the temporal and spatial accuracy of data is crucial, and that radar data’s 4D variational assimilation in mesoscale model is an efficient way to solve the problem of the temporal and spatial accuracy of data.更多还原