The distribution line is exposed to the natural environment for a long time, which is easy to be affected by strong convection weather. In the afternoon of April 19, 2022, under the influence of high impact weather such as lightning and thunderstorms and gales, 13 distribution lines in Longnan City broke down successively, causing adverse effects such as power outage and power load loss for users. The extreme wind speed and lightning positioning data of from the automatic meteorological observation stations in Longnan City, Fengyun 4A (FY-4A) infrared cloud images, sounding data and Doppler weather radar data were used to analyze the severe convection weather process and the influence of power grid. The results are as follows: (1) The severe convective weather was dominated by lightning and thunderstorms and gales, and a large area of power outage and power load loss had occurred in Xihe, Lixian, Wudu, Kangxian and other counties. (2) The development of severe convection weather was mainly influenced by the plateau trough and shear line. Under the unstable atmospheric junction of "cold above and warm below", the surface convergence line triggered strong thunderstorm and gale; Satellite cloud images and radar echoes also showed that the development of convective clouds coincided with surface thunderstorms and gales. (3) The fault range distribution of distribution lines was basically consistent with the occurrence time and transit path of severe convective weather. Using 10-minute extreme wind speed and lightning location data, it was discussed that when the extreme wind speed exceeded 15.0 m·s^-1 or the positive lightning current exceeded 43 kA or the negative lightning current exceeded 26 kA, the possibility of distribution line faults was higher.

In order to better carry out the road icing prediction and early warning services, the hourly observation data of traffic meteorological stations in the high incidence area of road icing in Gansu Province (the east of Wuwei, Gansu) were used to analyze the spatial and temporal distribution characteristics of road icing, explore the correlation between road icing and meteorological factors, and construct the road icing warning model by using Logistic regression method and neural network algorithm. The results showed that road icing in Gansu Province occurred mainly in winter (from December to February of the following year), and the frequency of road icing was higher from 00:00 to 10:00 and from 22:00 to 23:00. Logistic regression model and neural network model had high prediction accuracy for non-icing events, with 91.9% and 96.2%, respectively. For the occurrence of icing events, the prediction accuracy of Logistic regression model was low, at 31.6%, while that of neural network model could reach 44.6%, indicating that the two models had certain indicative significance for road icing warning, and the prediction effect of neural network model was better than that of Logistic regression model.

Based on observation data of CO₂, CH₄ mole fraction and temperature, relative humidity, wind speed and wind direction at Linfen station of Shanxi from 2013 to 2018, and ERA-5 PBL (planet boundary layer) reanalysis data from the European Center for Mediumrange Weather Forecasts (ECMWF) and GDAS (global data assimilation system) reanalysis data from the National Centers for Environmental Prediction (NCEP), the spatio-temperal distribution characteristics of two greenhouse gases concentration and their influence factors were analyzed in Linfen with high carbon emission. The results show that the annual average CO₂ and CH₄ mole fractions were 441.7×10^-6 and 2359.5×10^-9 at Linfen station, respectively, they were higher than that at background stations of globe and Waliguan of Qinghai Province and other city stations such as Pudong of Shanghai. There are very significantly positive correlations between CO₂ and CH₄ concentrations at Linfen in spring, autumn and winter, which indicates that the anthropogenic emissions dominate to carbon cycle of Linfen. The monthly change of CO₂ and CH₄ mole fraction with single peak and single valley pattern was obvious at Linfen, and the CO₂ mole fraction was the highest in winter and the lowest in summer, while the CH₄ mole fraction was the highest in winter and the lowest in spring. The CO₂ and CH₄ mole fraction were higher from 06:00 BST to 09:00 BST, while those were lower from 15:00 BST to 17:00 BST at Linfen, and their diurnal change ranges were the smallest in spring, while that of CO₂ and CH₄ mole fraction was the greatest in summer and winter, respectively. Apart from carbon emission source, the influence of meteorological conditions on CO₂ and CH₄ concentration is obvious in Linfen. The influence of temperature and humidity was more in summer, while that in other seasons was less. The photosynthesis and photochemical reactions enhance in summer, which lead to the decrease of CO₂ and CH₄ concentration, therefore the high temperature and low humidity are beneficial to the decrease of concentration. The average wind speed has significantly negative correlation with two greenhouse gases mole fraction, and the low wind speed is beneficial to the increase of concentration. In addition, the northeast and southeast winds are likely to transport industrial and other emission gases to the observation site and surrounding, which lead to the increase of two gases concentration at the site. Due to the influence of anthropogenic emission sources is most, the spatial distribution characteristic of CO₂ concentration is better similar to CH₄ concentration in Linfen in winter. In addition, the CH₄ concentration in eastern Linfen is higher in the whole year, which may be attributed to the Qinshui coal field with the most yields in China.

Based on the hourly precipitation from automatic weather stations, radar data at Xifeng station and radiosonde data at Pingliang station from 2008 to 2018, the temporal and spatial distribution, physical quantities and radar echo characteristics of short-term heavy rainfall in the Loess Plateau of Qingyang were analyzed statistically. The results are as follows: (1) The short-term heavy rainfall was easily to occur in the north and southeast of the Loess Plateau of Qingyang, and the business forecast should focus in Huan county and Zhengning county. (2) The earliest and latest short-term heavy rainfall occurred in mid-April and mid-October in the Loess Plateau of Qingyang from 2008 to 2018, respectively, and it occurred mostly in July and August, the peaks were in mid-July and mid-August. The diurnal variation of short-term heavy rainfall appeared typical double peaks pattern, the major and minor peaks occurred at about 17:00 BST and 09:00 BST, respectively, and the frequency of short-term heavy rainfall with different levels was higher from 16:00 BST to 18:00 BST. (3) The early warning indicators of water vapor condition of short-term heavy rainfall were 700 hPa specific humidity equal to or more than 7 g·kg-1 and temperature dew-point difference equal to or less than 4 ℃ in the Loess Plateau of Qingyang, as to energy condition, CAPE was equal to or more than 100 J·kg-1 and CIN was equal to or less than 120 J·kg-1, and for instability condition, △T7-5 was equal to or more than 15 ℃ and SI was equal to or less than 1 ℃. In addition, the early warning indicators of -20 ℃ and 0 ℃ layers height averaged 8.6 km and 5.1 km, and the thickness of frozen layer averaged 3.4 km. (4) The radar echo intensity of short-term heavy rainfall exceeded 38 dBZ, the height of echo top and the strongest echo center were more than 7.0 km and 3.6 km, respectively, and the VIL was more than 20 kg·m-2, which could be used for early warning indicators of short-term heavy rainfall. Upon inspection, these early warning indicators could provide some useful references for potential forecast and nowcasting of short-term heavy rainfall in the Loess Plateau of Qingyang.