Based on the conventional meteorological observations, the ERA5 (0.25°×0.25°) reanalysis data and FY-4A satellite cloud top brightness temperature data, the snow storm in the western Tibet from October 18 to 19, 2021 was analyzed, and the contribution of the low vortexes in northern India to the heavy snowfall was further studied. The results show that the heavy snowfall occurred under the background of the South Branch trough moving eastward and the abnormal activity of the Indian vortex, the high-level jet in front of the South Branch trough and the east-west double vortexes in northern India provided favorable circulation background for the strong snowfall in the west of the plateau. During this snowfall process, from northern India to the south of the Himalayas, the southeast low-level jet burst, establishing a water vapor transport channel from the bay of Bengal to the west, so that the water vapor in the bay of Bengal can be transported to the west. The low vortex system that generated in northwest India, on the one hand, made the water vapor from the bay of Bengal gather in the east of the low vortex and blocked its continuous transport westward. On the other hand, it enhanced the forcing effect between the southerly wind in the east of the low vortex and the plateau topography, so a large amount of water vapor can be continuously transported from the lower troposphere along the steep terrain on the southern slope of the plateau to the plateau, which provided sufficient water for the strong snowfall. The invasion of high-level potential vorticity is the main reason for the formation and development of the low vortex system in northwest India. In general, the low-level vortex system in the lower troposphere in northern India played a key role in the heavy snowfall process. In snow forecast in the plateau areas, it is necessary to strengthen tracking and monitoring of low vortex system in the lower troposphere in the low latitude.
The Fourier Merlin transformation, multi-scale optical flow method and Weibull distribution were used to carry out multi-scale prediction experiments of radar echo on four precipitation cases from June to July 2020 in Hubei Province, and the phase and intensity correction of model products were realized. On this basis, the corrected radar echo prediction of numerical model and radar echo extrapolation prediction were blended by using hyperbolic tangent function. Finally, the prediction effect of blending method with different prediction time, scales and thresholds of echo intensity was quantitatively analyzed by means of prediction skill score, mean absolute error (MAE) and probability of detection (POD). The results are as follows: (1) Compared with model prediction and radar extrapolation, the 0-3 hours precipitation echo predicted by the blending method improved obviously in range and location, the advantage of blending prediction was obvious, especially to strong echo, and it had a positive influence on prediction of convection. The 0-1 hour prediction effect of precipitation echo with 0.01°× 0.01° spatial scale was obviously better than those with other scales and prediction time. (2) MAE of Wuhan RUC model prediction was the largest with a range of 6.1-8.2 dBZ, while for blending forecast it was the smallest with a range of 4.7-6.5 dBZ. POD of blending prediction for 0.01°× 0.01° scale decreased with increase of echo threshold and prediction time, while the average POD was the maximum and MAE was the minimum for precipitation echo with 20 dBZ threshold at other scales, the average POD (MAE) of blending prediction was higher (lower) than other two kinds of prediction. On the whole, the blending prediction was obviously superior to single prediction, and it can provide reference for improvement of 0-3 hours quantitative precipitation forecast.
Based on the air pollution monitoring data, meteorological observation data, NOAA monthly mean Arctic oscillation (AO) index and ERA5 reanalysis data in January from 2013 to 2020, the meteorological causes of more haze days in Hohhot of Inner Mongolia in January 2020 were analyzed. The results are as follows: (1) The weak winter monsoon circulation, the atmosphere with high humidity, low boundary layer and inversion structure in January 2020 were the main reasons of more haze days. (2) There was a significantly positive correlation between daily average relative humidity, accumulated precipitation, the number of days with 2 min average wind speed per hour equal to or less than 1.5 m·s-1 and haze days in Hohhot in January from 2013 to 2020, among which the 2 min average wind speed per hour equal to or less than 1.0 m·s-1 was more conducive to the occurrence of fog and haze. (3) Surface snow cover had a great influence on the continuous haze event. The deeper and longer the snow cover lasted, the more haze days were in January in Hohhot. (4) When the haze weather continued in Hohhot in January 2020, the average height of the boundary layer was about 430-550 m, with the lowest of 210 m. The lower boundary layer contributed to the accumulation of pollutants near surface layer, leading to the deterioration of visibility. The lower the boundary layer height was, the heavier the pollution would be.
Based on the lightning monitoring data of 33 lightning location monitoring stations and real-time lightning disaster data of 48 counties in Qinghai Province from 2010 to 2019, the spatial distribution and risk zoning of lightning disasters in Qinghai Province were analyzed by using mathematical statistics and ArcGIS spatial analysis method. The results show that the regions with more lightning frequency and strong positive and negative lightning current intensity were mainly distributed in the central and eastern part of Qinghai Province, while the areas with high value of thunderstorm days were mainly distributed in the Qilian Mountain and the southern part of Qinghai Province. The lightning disaster risk presented obvious regional differentiation in Qinghai Province. The high-risk regions were mainly located in Kunlun Mountains, Qilian Mountains, Nyainqentanglha Mountains, Bayan Har Mountains and Anyemaqen Snowy Mountains, as well as part of the southern grazing area of Qinghai Province. The northwest of Qaidam Basin, the southeast pastoral area of Qinghai Province and some areas around Qinghai Lake were medium-risk areas. The risk level in most of the eastern agricultural area, part of Qaidam Basin, Wudaoliang and Tuotuo River area was relatively lower.
