Doppler Radar Data's Retrieving And Its Four-dimensional Assimilation Experiments In Mesoscale Model

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.