为充分利用新一代多普勒天气雷达基数据,结合新一代多普勒天气雷达基数据标准格式,文章研究了使用Python语言读取和解析基数据的方法,将解析后的数据根据不同的产品、不同的扫描仰角分别存储在多个Excel文件中.将解析后的数据在Python中重绘生成,与业务用pup软件生成的产品图像对比,验证了基数据解析的准确性.该软件的业务应用可为基于雷达基数据资料的分析与研究提供数据基础.

The consistency of the dual-channel radar plays a crucial role in the performance of the dual-polarization radar. In theory, the performance of the two channels is required to be completely consistent, but it cannot be completely consistent due to the influence of hardware errors, temperature and noise in practical applications. Therefore, it is necessary to test the consistency of horizontal and vertical channels of radar regularly in business applications. Aiming at this problem, the receiving system of Jinan S-band dual polarization radar is tested by off-line manual testing and online automatic testing. The offline measurement uses two signal sources inside and outside the machine to test separately, it is found that the output power difference between the two channels is too large. After the connection lines of the two channels and the two-channel power divider are exchanged, the output power of the two channels is basically the same. The test results of noise coefficient and echo intensity of the two channels are good and meet the requirement of consistency. CW signal source and TS signal source are used for online automatic test. The amplitude and phase standard deviation of the CW signal and the TS signal meet the requirements of the index. However, TS signal is used to calibrate the received full link, which increases the loss of azimuth rotation joint, so its amplitude and phase standard difference are higher than CW signal. Therefore, it is necessary to test and correct the deviation caused by the rotation joint regularly after running for a long time for the dual polarization radar. The two measurement methods in this paper can effectively detect the dual-channel consistency of radar.

Four aerosol mass concentration observation stations in Dezhou, Taishan, Weifang and Weihai, which are distributed in different regions of Shandong Province, are selected to analyze and compare their PM2.5 daily, monthly and annual changes of data: In the daily variation, Dezhou, Weifang and Weihai stations all have a bimodal structure, and the PM data of Dezhou and Weifang have the same trend with time. In the monthly variation, the PM2.5 of each station mass concentration values show obvious seasonal variation characteristics, with higher average concentration in winter and spring and lower average concentration in summer and autumn. The lowest monthly mean of the four stations appears in August, the maximum monthly mean of Dezhou, Weifang and Weihai appears in spring, and the maximum monthly mean of Taishan appears in October. In the annual variation, except for the obvious seasonal variation characteristics, the four stations are found to have the trend that the monthly average maximum value and minimum value are decreasing year by year, which is related to the importance of environmental protection at the national level and the active adoption of relevant environmental treatment measures by local governments. From the overall PM2.5 in terms of mass concentration value, PM2.5 in Dezhou and Weifang is higher than that in Taishan and Weihai, so PM value in inland areas is generally higher than that in coastal and high-altitude areas.

利用逐小时的气溶胶、探空、ERA5气象数据,分析了2020年年底济南地区3次严重霾污染天气过程,结果表明:2020年年底的3次霾污染过程中AQI的变化趋势与PM2.5、PM10和NO2基本一致,说明PM和NO2是造成济南地区冬季霾污染天气的主要污染物,而CO、SO2和O3不是济南地区霾污染天气的主要污染物,其中PM与O3呈明显的错峰趋势;1000 hPa风场特征:a过程,在12月8日前后,济南地区受高压反气旋影响,风力微弱以晴好天气为主,不利于污染物的沉降和扩散,11日开始受西北冷空气影响,雾霾过程结束.b过程,12月26日前后济南地区受东南暖湿气流影响,到27日达到最强,然后减弱,使得这次短暂的霾污染过程减弱.c过程,1月20日前后济南地区受东南暖湿气流影响,23日开始减弱,使得AQI下降,然后在27日受东北风影响,此次长达8 d的霾污染天气结束.济南地区几次霾污染过程均出现逆温层,贴地逆温和脱地逆温均影响了霾的形成.