In order to improve the fine defense ability of late frost disaster in the vineyards in the eastern foothills region of the Helan Mountain, the minimum temperature observation data of the vineyards from April to May during 2020-2023 were used to analyze the variation characteristics of minimum temperature, the occurrence frequency and regional distribution characteristics of late frost in the vineyards. Based on the European Centre for Medium-Range Weather Forecasts (ECMWF) model forecast products and the actual temperature of grid points in Ningxia, the radial basis function (RBF) neural network algorithm was used to construct the minimum temperature and frost prediction model in the vineyards in the eastern foot of the Helan Mountain. The results show that the light frost was the most common in the vineyards in the eastern foothills region of the Helan Mountain, followed by the medium frost. April was the main month for frost occurrence. The frost in D.F. Yuxing winery appeared most frequently, and the frost in Guanlan winery was the least. The verification results of the minimum temperature and frost forecast show that compared with the ECMWF model, the RBF model has improved the accuracy of the minimum temperature forecast in the Helan, Yongning and Hongsipu production areas, with the highest increase of 33.8%, and the average absolute error reduced by 0.20-1.50 °C. For the single station frost forecast, the RBF model has obvious advantages, the accuracy rate generally increased by 1.0%-14.0%, and the average absolute error reduced by 0.04-0.37 °C. For the average of the production areas, the RBF model has the highest accuracy of frost prediction in the Hongsipu production area, up to 13.0%. In the case analysis of frost, the RBF model has better prediction effect, especially for moderate frost prediction. Compared with the ECMWF model, the accuracy rate increased by 25.0%-50.0%, and the average absolute error reduced by 1.80-2.10 °C.

The compound drought and heat wave has been paid more and more attention and the combined effect is far more than ordinary drought events. The characteristic of the events was explored in Central Asia from 1981 to 2020, based on hourly temperature data and monthly potential evapotranspiration data of the Land Component of the Fifth Generation of European Reanalysis (ERA5-Land) from the European Centre for Medium-Range Weather Forecasts and daily multi-source weighted-ensemble precipitation data. Standardized Precipitation Evapotranspiration Index (SPEI) and daily maximum temperature were calculated to identify the events. The following conclusions were drawn: (1) The spatial distribution of compound drought and heat wave event in Central Asia was not concentrated. The regions with high frequency of the events in the 1980s, 1990s, 2000s and 2010s were mainly distributed in the southeast, north, northwest and west, respectively. (2) In terms of temporal distribution, the general trend of the events had changed from large fluctuation to small fluctuation, and likely to remain stable after 2020. (3) Studying the four typical years of 1984, 1993, 2010 and 2020, it is concluded that the compound drought and heat wave event in Central Asia were mainly concentrated in the southeast in 1984, sporadic occurrences in a few scattered regions in 1993，in the north in 2010 and in the west in 2020.

Lanzhou is a typical valley city in Northwest China. Based on the ground conventional meteorological observation data, air quality index at Lanzhou and Baiyin stations and NCEP-DOE reanalysis Ⅱ data, the weather situations during the severe and extreme air pollution processes in Lanzhou from 2015 to 2017 were classified, and the similarities and differences of air pollution between Lanzhou and its satellite city Baiyin under different weather situations were analyzed. And on this basis the influence of meteorological elements on air pollution under different weather situations were discussed by numerical simulation of 21 typical cases. The results are as follows: (1) The weather situations during the severe and extreme air pollution in Lanzhou were mainly trough-ridge pattern and westerly wind pattern from 2015 to 2017. (2) The valley wind circulation was weaker under trough-ridge pattern weather situation. When the degree of air pollution was the same in two cities, the near ground wind field converged over Lanzhou, while the convergence and divergence of wind field wasn’t obvious over Baiyin, and the downward movement over Baiyin was more significant than that over Lanzhou in the afternoon. When the degree of air pollution was different in two cities, the wind field appeared convergence near ground of Lanzhou, and there was a weak downward movement at night, while the wind field appeared divergence near ground of Baiyin, the downward movement wasn’t obvious. (3) The valley wind circulation was stronger under westerly wind pattern weather situation. When the degree of air pollution was the same in two cities, the convergence and divergence of wind field wasn’t obvious over Lanzhou and Baiyin, but there was downward motion near ground of Lanzhou at night. When the degree of air pollution was different, the wind field appeared convergence near ground of Lanzhou, and the downward airflow was dominant in the daytime, while the convergence and divergence wasn’t obvious over Baiyin, and the updraft was dominant near ground during the daytime and nighttime. (4) Under two patterns weather conditions, the wind speed near ground of Lanzhou was usually smaller than that over Baiyin on polluted day, the inversion gradient over Lanzhou was usually larger than that over Baiyin, and the height of boundary layer over Lanzhou was smaller than or close to that over Baiyin.

When applying wavelet transformation method in the retrieval of the boundary layer height by using lidar backscatter signals， the different selection of wavelet generating function may get different results．Therefore，the idealized lidar signal profiles during the daytime and nighttime have been built to explore which wavelet generating function will get the best performance．In this study，Haar wavelet covariance transformation has been used for the profiles of lidar backscatter signal，and Morlet and Mexican Hat wavelet transformation have been utilized for the gradient profiles of lidar backscatter signal，respectively．The results showed that the Haar function and the Mexican Hat function should be used as wavelet generating functions:the Haar function is more accurate and the Mexican Hat function is more stable． Furthermore，the changed wavelet dilation of the wavelet generating function also has been researched in order to test the sensitivity of the three different wavelet transformation methods to the wavelet dilation． The result show that whatever the idealized profile or the profile in which disturbance was added，the larger wavelet dilation can get a more stable and accurate diurnal boundary layer height and nocturnal mixing layer height．

 

The simulanted radar data are produced by using high-order Lengendre polynomialst of it the real radar observed wind. Three interpolation schemes are tested with the simulated data.In first scheme（CVI），the radar data are first interpolated to horizontal Cartesian grids but left on the iroriginal constant elevation surfaces in the vertical，then the data are further interpolated to the Cartesian grids with a linear interpolation in the vertical. The second schemeis the three-dimension Barnes interpolation technique（3DBarnes）.The third scheme is three-dimension variational analysis（VAR）.The results show that the3D-Barnes scheme obviously excels the others，especially at middle level；the error of the CVI scheme is slightly larger than the former，and its smooth nessispoor， and it has biggish missing region at lower and upper level.The VAR scheme is not perfect.