The Retrieval Of Temperature Advection And Numerical Model Assimilation Based On The Wind Profile Radar Data

Temperature advection is the basic physical quantities in the weather prediction. Temperature advection, which directly leads to the change of atmospheric thermal structure and further brings about the alteration with atmospheric physics field, can reflect the development of the weather systems better. In recent years, the technology of wind profile radar has been fully developed, whose data has both such high precision and spatial and temporal resolution. It can also continuously provide the distribution of horizontal wind along with height over time. On the basis of the concept of temperature advection and the principle of thermal wind, retrieving the temperature advection with high precision and spatial and temporal resolution is feasible by the algorithms of the retrieval of temperature advection with the wind profile radar data.The temperature advection has been retrieved with the data of the wind profile radar in Beijing, Yanqing Station. A cold air invasion on November 15,2014 in the night has been analyzed in detail and six cold air invasions from September to November,2015 has been studied statistically, whose results are compared to temperature advection prediction products of T639 model. After the quality control, the horizontal wind data with the height from 1000 to 4000 m per hour has been assimilated using GRAPES_ Meso model which is from six wind profile radars in the region of Beijing-Tianjin. Results show that:(1) The advantages of the temperature advection retrieved through the wind profile radar observations are real-time, continuous and high precision. The quality of retrieving temperature advection depends on the quality of horizontal wind profile data. The order of magnitude is consistent and the value is approaching between the retrieval of temperature advection by the data of the wind profile radar and the initial data of T639 model within a certain amount of prediction time.(2) Prediction time shows a good conformity between temperature advection retrieving data of the wind profile radar and temperature advection prediction products of T639 model in 6～12 hours, which depends on temporal scale weather system. The deviation between the prediction products of T639 model and the real-time products retrieved of the wind profile radar forms gradually as the prediction time lengthens, even the reverse prediction advection attribute may appear. Thus, the precision of numerical forecast can be improved with the wind profile radar data assimilated in the future.(3) With the integral of model, assimilating data of wind profile radar can expand the reach of prediction products to a larger height range. In general, assimilating data of wind profile radar influences on prediction products of GRAPES_Meso model observably within 6 hours. Fluctuations of differential peak show that height range of data of wind profile radar assimilated is positively correlated to the influence on prediction products’results. And there are also other factors influencing on prediction products’results, including layout of sites and geographical environment (hilly land, plain).(4) Assimilating the data of wind profile radar has an effect on the temperature advection prediction products of GRAPES_Meso model. It depends not only on the height range of data assimilated but also on the weather condition and the terrain.