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In this paper, the features and possible causes of extreme precipitation in the winter of 2019/2020 in China are investigated based on the observational station daily dataset provided by the China Meteorological Administration and the ECMWF ERA5 reanalysis dataset. Record-breaking precipitations are found in most areas of China in the winter of 2019/2020, particularly for the north of China. The magnitude of precipitation in south (north) of China in 2019/2020 increase 0.39 mm/day (0.15 mm/day), about 39.0% (105.5%) compared with the climatological mean. Moisture budget analysis shows that contributions of atmospheric circulation anomaly on horizontal moisture advection and total moisture flux convergence are primarily responsible for the positive anomalies of precipitation in China. Further analysis suggests that the weakened East Asian Trough and the anti-cyclone circulation anomaly over the Sea of Japan favor the transportation of low-level warm and humid airflow from the Northwest Pacific and the East China Sea to the north of China. Meanwhile, the westward movement of the Subtropical High and the Southern Branch Trough anomalies transport low-level warm and humid air from the Bay of Bengal and the South China Sea to south of China. The warm moist air flows northeastward from the East China Sea, Bay of Bengal and the South China Sea while the climatological cold wind flows southeastward, which causes the increase of local precipitation in the winter of 2019/2020. In consideration of the fact that stratospheric polar vortex anomaly has a process of downward propagation to the troposphere, it is of great significance to study the changes of the stratosphere and the relationships between stratosphere and troposphere.

The behavior of stabilizing piles with different embedded lengths in bedrock with a hard upper layer and a weak lower layer is examined using physical model tests. Based on the geological model of the landslide with bedrock with a hard upper layer and a weak lower layer, the pile–landslide physical model is developed and includes the data collection system, the pile–landslide model and the loading system. The real-time deformation and moment of the stabilizing pile is obtained in the process of loading at the trailing edge of the landslide for different percentages of hard bedrock. The test results indicate that the percentage of hard bedrock has a strong influence on the deformation and moment of the stabilizing pile embedded in bedrock with a hard upper layer and a weak lower layer. A higher percentage of hard bedrock can result in a greater maximum bending moment of the stabilizing pile and a smaller displacement at the stabilizing pile head, and vice versa. The results also show that 40% of hard bedrock can be considered the critical condition in bedrock with a hard upper layer and a weak lower layer. The percentage of hard bedrock has a limited influence on the deformation of the stabilizing pile when the percentage is greater than 40%. Moreover, four tests with stabilizing piles with different embedded lengths are conducted to investigate the most suitable embedded length in the bedrock with a hard upper layer and a weak lower layer. The results indicate that 28.5 cm is a reasonable embedded length for the stabilizing pile in the two-layered bedrock with a hard rock percentage of 20%.

Because multiple wind farms are connected to the grid at the same time and the total amount of energy in the same wind zone is limited, there is a strong correlation between wind farms with similar geographical locations. Neglecting this correlation can lead to a large difference between wind power analysis and actual operation, which in turn leads to a series of adverse consequences. In this paper, we use nuclear density estimation to establish the edge distribution of wind power output, compare and analyze various Copula functions based on correlation parameters and entropy weight optimization theory. The simulation analysis results show that the Clayton-Copula function is the best correlation function, which can describe the tail part of the random time series more accurately.