A series of tornado weather processes that occurred in northern Liaoning province on June 1, 2023 were diagnosed using the data from intensive automatic stations, FY-3G low-orbit meteorological satellites, Doppler radar and historical data. The results show that the northeastern cold vortex provides dry and cold conditions in the upper levels, and the southerly air flow in the front of the surface cyclone transports warm and humid air to the north, forming a strong convective instability in northern Liaoning province. In the lower levels, there is a nearly vertically distributed frontal zone. The high positive vorticity at the 950~750 hPa in front of the frontal zone is well matched with the vertical velocity, and the dynamic uplift effect is enhanced, which is conducive to the development of the convective system. The dry lines, large temperature gradient zones and cold pools associated with the mesoscale frontal areas lead to the formation of linear convective cloud belts in western Liaoning province. Local small-scale vortices may be the dynamic trigger system for the tornado in Fuxin. The density flow generated between the strong cold pool in northern Liaoning province and the strong warm ridge in the southeast caused the local westerly wind to increase on the east side of the cold pool. The superposition effect of the wind speed and direction convergence along with the strong cold pool may be the reason for the triggering of the tornado in Kaiyuan. Under the background of the northeastern cold vortex and the special coastal and mountainous terrain, northern Liaoning province has the convective potential conditions for tornadoes. Fuxin is located in the special terrain of an inverted "U" shape with high altitude in the surrounding area and low in the middle. It is easier to form small-scale cyclonic closed circulation, which may be one of the reasons why Fuxin is the center of the Liaoning province tornado.

During a large-scale heavy pollution weather process in Lianyungang area from December 11 to 13, 2020, this paper used the aerosol sampling device and laser particle counter carried by the Unmanned Aerial Vehicle (UAV) to vertically detect the near-ground atmospheric particulate matter, combined with wind profile radar, ground-based microwave radiometer data, ground meteorological observation data, and reanalysis data, and analyzed the atmospheric particulate matter concentration profile and the vertical distribution of meteorological elements. The source and diffusion of pollutants were analyzed through the backward trajectory tracking mode, and the causes of the polluted weather were explored. The results show that the high-altitude wind field observed by the wind profile radar and the temperature and humidity characteristics measured by microwave radiometer have a good correspondence with the vertical characteristics of particulate matter. From the vertical distribution of pollutants, during the serious pollution period, the pollutants are mainly concentrated at the height below 200 m, the pollution layer is shallow, showing an obvious bimodal structure. In the pollution diffusion stage, the pollutants spread upward, the concentration of pollutants on the ground decreases significantly, and the pollution layer is relatively deep with the weakening and rising of the inversion layer. The exogenous transport of pollutants and the hygroscopic growth of pollutants under favorable meteorological conditions in Lianyungang area are the main reasons for this polluted weather process. The main sources of upstream pollutant transport are Xuzhou and the central region of Shandong Peninsula, and after leaving Lianyungang, pollutants mainly spread along southeast to east to sea. The change of wind direction probably a key meteorological condition for predicting the beginning of pollutant dissipation, while wind speed is the key to the pollutant dissipation speed.

In order to better understand the concentration distribution of O₃ in the lower troposphere and the influence of meteorological factors on O₃ pollution, this paper used UAV (Unmanned Aerial Vehicle) to observe O₃ concentration vertically, air temperature and relative humidity in Meishan city from June 27 to 30 and July 6 to 12, 2022, and combines the weather situation, ground meteorological elements and HYSPLIT's 48 h backward trajectory to compare and analyze the two processes from the near surface and vertical direction, respectively. The results showed that the daily variation of O₃ concentration was obvious, the valley value near surface appeared at 06:00-08:00, and began to increase significantly around 10:00, then reaching a peak during 12:00-20:00. While the average concentration of O₃ in the vertical direction was lowest at 10:00 and rapidly enhanced at 12:00, fell slightly at 16:00 and peaked at 20:00. When the concentration of O₃ near surface is high, the corresponding average vertical concentration is also higher; while the concentration of O₃ near surface is higher than that in the vertical direction, and the concentration of O₃ in the 0~1000 m increases slightly and then decreases, and the peak mostly appears in the height of 0~300 m above the ground. The correlation between O₃ concentration and meteorological elements was significantly higher during the day than that during the night, positively correlated with temperature, negatively correlated with relative humidity and pressure, and positively correlated with wind speed, while the correlation between O₃ concentration and relative humidity in the vertical direction was significantly different at different observation times.

Based on the hourly monitoring data of near-surface PM_2.5 mass concentration and the meteorological data of the same period in Kunming area from 2016 to 2020, the correlation between the variation characteristics of PM_2.5 mass concentration and meteorological factors was analyzed, and the seasonal characteristics of PM_2.5 transport path and pollution track and the distribution of potential source areas were studied by using the backward trajectory model. The results showed that the mass concentration of PM_2.5 increased first and then decreased and fluctuated peak-valley throughout the year. The overall concentration was 57.4% higher in spring and winter than in autumn and summer, and relatively stable at night, while the variation range was large in daytime, and the concentration at night was significantly higher than that in daytime (22.6%). The mass concentration of PM_2.5 was negatively correlated with air temperature and wind speed, the correlation coefficients were -0.60 and -0.13, respectively, and was positively correlated with air pressure (R=0.44, P < 0.01). There were seasonal differences in the source direction of airflow trajectories in Kunming area. The sources of spring and winter trajectories were concentrated, mainly from the southwest, and scattered in summer and autumn centered on the south. Except for all the airflows arriving in Kunming area in summer and autumn are clean airflow, the transmission path in spring is far and the contribution source area is large. The central part of Yunnan province (Yuxi city), the southwest Yunnan province (Chuxiong prefecture, Puer city) and parts of northwestern Bangladesh and northern Myanmar are the most important contributors to PM_2.5 quality concentrations in Kunming area, and the coverage and contribution degree of high values in winter are smaller than those in spring, and are only distributed in dots in south-central Yunnan province (Yuxi city, Honghe prefecture).

Based on the air quality monitoring data in Liaoning province from 2015 to 2022, the Air Quality Composite Index (AQCI) was used as the characterization index to analyze the temporal variation of air quality, and the ArcGIS spatial autocorrelation tool was used to explore the spatial distribution characteristics of AQCI at different time scales. The results show that AQCI in Liaoning province shows a downtrend over the past 8 years, decreasing from 5.64 in 2015 to 3.55 in 2022. The excellent air quality rate steadily increased, with a 24.7% improvement in 2022 compared to 2015. The proportion of O₃, PM_2.5, and PM₁₀ as primary pollutants increased from 93.1% to 99.0%. The seasonal mean value of AQCI follows the distribution pattern of summer < autumn < spring < winter. The excellent air quality rate is highest in summer or autumn, followed by spring, and winter is lowest. The primary pollutants show seasonal fluctuations, with PM_2.5 and PM₁₀ being the main pollutants in spring, autumn, and winter, while O₃ pollution mainly occurs in summer. The monthly variation in AQCI is significant, which varies largely in different cities, with the highest values in January and the lowest values in July or August. The excellent air quality rate is lowest in January and highest in August. PM_2.5, PM₁₀, and O₃ account for more than 90% of the primary pollutants each month. The spatial aggregation characteristics of AQCI in Liaoning province are evident, with variations over time. The high-value aggregation characteristics of the central urban agglomeration are particularly significant.

The hourly data from environmental monitoring stations in Shandong province during 2013-2021 was used to characterize six pollutants, to explore the temporal and spatial distributions of particulate matter (PM) and ozone (O₃), and to analyze the meteorological conditions and sources of pollution. The results show that the air quality in Shandong province has improved year by year from 2013 to 2021. The annual mean mass concentration of PM_2.5 in 2021 decreased by 60.2% compared to that in 2013. The annual mean concentration of O₃ shows a rising trend year by year during 2013-2021, while the concentrations of the other five pollutants decrease. PM and O₃ are the most important pollutants affecting the air quality in Shandong province. CO, NO₂, PM₁₀, PM_2.5, and SO₂ concentrations exhibit a unimodal pattern characterized by highs in winter and lows in summer; O₃ concentrations show a unimodal pattern feature with the highest in summer and the lowest in winter. The spatial distribution is characterized by high pollutant concentrations in the central and western part of Shandong province and low concentrations in the eastern part of Shandong province. Environmental meteorological index and atmospheric self-purification capacity index characteristics show that the combination of favorable meteorological conditions and emission reduction during 2013-2021 led to a 59 μg·m^-3 decrease in the annual average concentration of PM_2.5 in 2021 compared to that in 2013, with emission reduction as the main reason. Source apportionment shows that the proportion of PM_2.5 from local emissions is high in most areas of Shandong province. The share of local emissions decreased in 2021 compared to that in 2019. An area is closer to Hebei and He'nan provinces, the share of exogenous transmission is the larger. Local emissions in the seven corridor cities accounted for 31%~54% in 2019, and 19%~34% in 2021, with northwest Shandong province being the most affected by external transport.

By utilizing the ground meteorological observation data from 12 national meteorological stations in Xianyang city from 1962 to 2021, alongside the ambient air quality monitoring data from 2015 to 2021, the daily atmospheric self-purification capacity from 1962 to 2021 was calculated. The mutation tests were employed using Mann-Kendall (M-K)and sliding t-test (MT) methods, and the spatial characteristics were studied. The results show that the air self-purification capacity in Xianyang city exhibits distinct annual, monthly, and seasonal patterns, with a decreasing trend observed in decadal variation. The climate tendency rate of the air self-purification capacity is -1.22×10⁴ km²·a^-1·(10 a)^-1. From 1962 to 2021, the overall trend has shown an initial increase, followed by a decrease, and then another increase, with the lowest year (2002) recording a 70% decrease compared to the highest year (1969). The sequence of atmospheric self-purification capacity from high to low is summer, spring, fall, winter. The correlation coefficient between the annual average wind speed and the annual average atmospheric self-purification capacity is 0.98. The correlation coefficient between the number of light wind days and the atmospheric self-purification capacity of the atmosphere is -0.91, with the self-purification capacity of non-light wind days being 6.2 times that of light wind days. The correlation coefficients between the number of precipitation days, daily precipitation, monthly precipitation, and atmospheric self-purification capacity were 0.23, 0.02, and 0.07, respectively. In terms of spatial distribution, the annual and seasonal average atmospheric self-purification capacity is generally higher in the east and lower in the west, as well as higher in the north and lower in the south. Regions such as Wugong, Xingping, Qindu, and Weicheng exhibit lower atmospheric self-purification capacity. Additionally, the Air Quality Index (AQI) and PM_2.5 concentration are inversely correlated with atmospheric self-purification capacity. The abrupt change year of annual atmospheric self-purification capacity was identified through two abrupt change tests in 2010, with spring and summer experiencing an abrupt change in 2011, and autumn and winter in 2010.

Using precipitation observation data from China Meteorological Administration ground stations from 2019 to 2020, six quantitative evaluation indicators including correlation coefficients (CCs), mean bias errors (MBEs), root mean square errors (RMSEs), probability of detections (PODs), false alarm rates (FARs), and critical success indices (CSIs) were used to analyze the accuracy and regional applicability of the IMERG multi-satellite remote sensing retrieval of the Global New Generation Global Precipitation Measurement Program (GPM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L) precipitation products in Sichuan region, China. The results show that the two products have a strong precipitation zone on the eastern side of the western Sichuan Plateau, roughly distributed in a quasi north-south direction, and that ERA5L can capture heavy precipitation centers more accurately compared to IMERG. The consistency between station observations within the basin is better than in the surrounding mountainous areas. The precipitation of IMERG and ERA5L was 15.2% and 33.3%, respectively, higher than that observed by the station, respectively. The two products have a relatively high ability to capture precipitation events in the western Sichuan Plateau, with low FAR. From the perspective of time test statistical indicators, the trends and magnitude changes observed by the two products and stations are basically consistent, with relatively high relative station observations being the main trend. Compared to IMERG, ERA5L has a high CC with site observations, a high POD, a high CSI, and a low deviation, but a relatively high FAR. Overall, both IMERG and ERA5L precipitation products have shown certain potential for application in Sichuan province, but there are significant differences in accuracy under different precipitation intensities and terrain conditions, requiring product correction.

In order to further understand the influence of Yuanbao Mountain on rainfall in northeast of Guangxi Zhuang Autonomous Region, the spatial and temporal distribution characteristics of precipitation in Yuanbao Mountain and its surrounding areas were analyzed based on the hourly observation data from 2021 to 2022 of the Guangxi Yuanbao Mountain gradient meteorological observation system, which consists of 21 regional automatic meteorological stations. The results indicate that the average annual precipitation in Yuanbao Mountain is nearly 2700 mm, which is comparable to that in the coastal areas of Guangxi. It is distributed from high in the east to low in the west, with large precipitation values concentrated on the windward slope. The annual precipitation on the windward and leeward slopes differs by nearly 900 mm. The number of precipitation days increases with altitude. Monthly precipitation has a single peak, with most precipitation occurring from May to July. In the southern regions, the maximum monthly precipitation occurs in June on windward and leeward slopes of Yuanbao Mountain, accounting for more than 28% of the annual precipitation, and the maximum monthly precipitation in the northern regions occurs in May. The peak daily rainfall mainly occurs at 04-05 am, with little rainfall activity during the daytime, reaching a minimum at 16-17 pm. In addition, the number of annual heavy rain days in Yuanbao Mountain ranges from 9.5 to 18.5 days, mainly occurring from May to July. The maximum number of heavy rain days occurs in June in all regions. The number of heavy rain days in high altitude regions is higher than that in low altitude regions. The intensity of heavy rain on the windward slope and southern slopes of the mountain is significantly greater than that in other regions. Moreover, the frequency of short-term heavy rain in Yuanbao Mountain is 10~32 times per year. There are significant differences in the starting and ending months and the duration of short-term heavy rain in different regions. Short-term heavy rain mainly occurs in the early morning, but it is slightly delayed with increasing altitude. Short-term heavy rain occurs most frequent on the windward slope, and the intensity of short-term heavy rain is similar in different regions.

According to the "Regulations for Monitoring and Evaluation of Regional Important Processes (Rainstorm)" issued by China Meteorological Administration, the localization recognition threshold of regional rainstorm processes in Yunnan province was determined, which is proved to be objective and effective by disaster data. Furthermore, the temporal and spatial distribution characteristics of regional rainstorm processes in Yunnan province from 1961 to 2022 were analysed by using the methods of climate trend analysis, Manner-Kendall test, wavelet, EOF and REOF. The results show that the comprehensive intensity of the regional rainstorm processes in Yunnan province fluctuates greatly, and ist annual mean value shows an insignificant decreasing trend, and the monthly mean value fluctuates and the peak appears in January, and the ten-day mean value in rainy season changes little. The regional rainstorm processes in Yunnan province occur four times a year on average, which shows a decreasing trend after 1977, but it is not significant, and there is no obvious abrupt change point from 1961 to 2022. The frequency of regional rainstorm processes in Yunnan province shows an unstable period of 6~8 years from 2008 to 2022. The regional rainstorm processes in Yunnan province are most frequent in summer and the least frequent in winter, accounting for nearly 70% of the annual rainstorm from mid-June to late August. The regional rainstorm processes in Yunnan province are more in the south and less in the north, although the frequency is less in the northern margin, the process precipitation intensity is larger. The regional rainstorm processes in Yunnan province are characterized by five major climatic patterns, such as most of the same pattern, southwest and northeast reverse pattern and north and south reverse pattern etc., and it can be further divided into five typical regions, namely, southern region, western region, central and eastern region, northern region and northwestern region. Over the past 62 years, the southern region, western region and central and eastern region fluctuate greatly, and the trend of change was not obvious. The northern region is stable and change little. The northwest region has mainly decreased in the early period and increased significantly in the later period.

Based on the monthly precipitation data from 71 observation stations in Hebei Province from 1961 to 2021 and the NCEP/NCAR reanalysis data, the temporal and spatial variability characteristics and associated factors of precipitation relative variability were analyzed for different climatic subregions of Hebei province through regression analysis and trend analysis. The results indicate that there are significant differences in precipitation relative variabilities across different timescales, with smaller timescales exhibiting larger relative variability. That is, the seasonal precipitation relative variability is greater than that of annual precipitation, which is lower than that of monthly precipitation. The annual precipitation relative variability in Hebei province ranges from 13.5% to 29.8%, with an average of 14.9%. Among the different subregions, the plain areas exhibit greater annual precipitation relative variability, while the Bashang region shows relatively smaller variability. Besides, except for the Bashang region, it shows decreasing trends in the 5 years changes of annual precipitation relative variability over the northern mountainous region, western mountainous region, coastal region, and plain region. Factors such as altitude, urbanization, and atmospheric circulation have significant impacts on the precipitation stability in Hebei province. When the low altitude and low urbanization are accompanied by a westerly wind anomaly at 200 hPa near 40°N and a zonal distribution of cyclonic-anticyclonic-cyclonic systems at 850 hPa, the strong ascending or descending motion circulations may lead to irregular precipitation variability in Hebei province.

Utilizing the daily temperature and precipitation data of 175 meteorological stations in Beijing-Tianjin-Hebei region from 1961 to 2020, the spatiotemporal variations of extreme temperature and precipitation events were analyzed using 9 typical extreme indices and methods of linear trend and Sen's mutation. An impact evaluation index system was constructed based on these extreme indices to quantify the local extreme climate impacts for the period of 2003-2019. The results indicate that the Beijing-Tianjin-Hebei region experienced a significant increase in extreme warm events and a significant decrease in extreme cold events from 1961 to 2020. Since the late 1990s, the warming rate has accelerated, with the decrease in cold events occurring earlier than the increase in warm events. Compared to Tianjin and Hebei, Beijing shows greater increase in extreme high temperatures and the warm event frequency, as well as smaller increase in extreme low temperatures and decrease in cold events. It implies the status of increasingly stronger warm events and less weak cold events in Beijing. The extreme precipitation indices in Beijing-Tianjin-Hebei region show a slight decreasing trend with notable decadal variability. Beijing and Hebei have experienced an increase in the precipitation maxima, suggesting an enhancement in extremely strong precipitation events in these areas. The year of 2020 shows slightly higher-than-normal values on the annual maximum temperature (TXx), warm days (TX90p), and warm spell duration index (WSDI), while lower-than-normal values for the annual minimum temperature (TNn), cold nights (TN10p), cold spell duration index (CSDI), and precipitation indices. Several stations in Tianjin have recorded the lowest CSDI and precipitation indices since 1961. It indicates that the extreme temperatures in 2020 are close to normal, while extreme precipitation in Tianjin is at a minimum state. Besides, the overall impacts of extreme events have increased since 2003. Although Beijing experiences lower climate disaster damage intensity compared to Hebei, it shows higher intensity of extreme weather events.

Based on the meteorological data of 224 stations in the Yellow River Basin from 1961 to 2020 and the MODIS NDVI vegetation data from 2000 to 2020, the spatiotemporal distribution patterns of aridity were analyzed, as well as its impacts on the vegetation NDVI. Results show that the tendency rate of annual averaged aridity in the Yellow River Basin is -0.03 per decade from 1961 to 2020, with a multi-year average value of 2.56. In terms of the regional variability, the northern stations are mainly characterized by a decreasing trend (52.2%), while the central and southern stations primarily exhibit an increasing trend (41.5%). As for the aridity spatial distributions, it generally decreases from northwest to southeast, with the highest values in the upper reaches (3.74), followed by the middle reaches (1.99) and lower reaches (1.74). The upper reaches include arid, semi-arid, and semi-humid subregions, while the middle and lower reaches are mostly semi-humid subregions. Stepwise regression analysis indicates that aridity is primarily impacted by the local precipitation. The reduction in annual average aridity in most areas could be attributed to decreased total solar radiation, increased precipitation, and lower temperatures. The averaged NDVI is 0.30 in the Yellow River Basin from 2000 to 2020, with a distribution of higher values in the southeast (lower reaches) and lower values in the northwest (upper reaches). The NDVI is a significantly negative correlation with the aridity (r=-0.52, P < 0.01, n=224), especially in upper and middle reaches of the Yellow River Basin.

The wind fields measured from the TWP3-type wind profile radar in Shapingba and the TWP8-L(Ⅱ)-type radar in Youyang of Chongqing, were assessed and analyzed towards the ERA5 reanalysis data. The dense radiosonde observations at 13:00 from June to August of 2014-2016, which have not been assimilated by the reanalysis, are firstly used to verify the representativeness of wind data from ERA5 reanalysis in Chongqing. It shows little deviation between them two, indicating the general reliability of the ERA5 reanalysis, which could further be employed to validate the accuracy of horizontal winds from Chongqing wind profile radars. The data from wind profile radar and ERA5 reanalysis were compared and analyzed. The results indicate that the correlation coefficient of horizontal winds at lower layers is lower than that at the middle and upper layers. However, the deviations mainly range from -1 to 1 m·s^-1 at altitudes below 5000 m. With the increasing altitudes, the u-component deviations tend to increase negatively, while the v-component deviations increase positively. In the summer season with hotness and dryness, the wind field measured by wind profiles is more precise to that in winter. The accuracy of wind measurements from wind profile radars is affected by precipitation. At the lower layers, the stronger deviations of u-component from wind profiles tend to be slightly larger during precipitation processes than during non-precipitation periods. At the middle and upper layers, the accuracy of the v-component wind is significantly affected by precipitation, with its weaker deviations being slightly larger during precipitation processes than during non-precipitation periods. During squall line weather, the wind speeds detected by both datasets are relatively close, while the wind profile data show slightly larger u and v wind components at the lower layers compared to reanalysis data.

Based on the daily temperature, precipitation, wind speed, relative humidity, and negative oxygen ion concentration observation data in Bifeng Gorge scenic area (BGSA) and the daily sunshine observation data of Yaan meteorological observatory station closest to BGSA, this paper constructed a tourism meteorological comprehensive index (TMCI) using temperature-humidity index (I), wind efficiency index (K), human body comfort index (HBCI), and air freshness index (negative oxygen ion concentration, N). The monthly and seasonal characteristics of the main tourism climate resources and tourism indices in BGSA were explored. The impacts of extreme weather and meteorological geological disasters on summer tourism in BGSA were also analyzed. The results show that the meteorological elements such as temperature, precipitation, humidity, wind speed, sunshine, and ultraviolet intensity in BGSA are suitable for summer tourism. The temperature-humidity index, wind efficiency index, HBCI, and TMCI from May to September in BGSA are suitable or very suitable for summer tourism. The negative oxygen ion concentration level in BGSA is above level I for 92.4% of the year, making it very suitable for tourism. The risk of extreme weather and meteorological geological disasters in BGSA is not significant throughout the year, and the impact of natural disasters on BGSA summer tourism is relatively small. In conclusion, BGSA is a great place for summer tourism.

Using MODIS remote sensing cloud cover data from 2019 to 2021, based on the negative correlation between surface meteorological observation sunshine percentage and satellite remote sensing cloud cover, an inversion calculation model was established for different months and regions. The monthly sunshine percentage and sunshine hours at a spatial resolution of 1 km were calculated, and representative surface meteorological station observation data were selected to verify the calculation results of sunshine hours. The results show that there is a highly significant correlation between the monthly average remote sensing cloud cover and the percentage of sunshine in different climatic regions of Hubei province. The relative error in calculating annual (monthly) sunshine hours at each representative station is less than 2% (10 hours), indicating that the constructed sunshine hours inversion model has high calculation accuracy. The calculated annual sunshine hours in Hubei range from 854.8 hours to 1952.8 hours. The southwestern western regions of Hubei province have the lowest sunshine hours, with annual sunshine hours below 1100 hours. The northeastern northern and northwestern southern regions of Hubei province have the highest sunshine hours, with annual sunshine hours exceeding 1850 hours. From the inversion results of sunshine hours, it is also found that due to the thermal conditions differences between water and land, the cloud cover over lakes in the Hubei plain areas are less than that of surrounding land, and the sunshine hours are larger than that of surrounding land.

Using the precipitation data from 2001 to 2021 and the data of atmospheric pollutants such as PM_2.5, sulfate and nitrate, we chose Lazi as the study area and use the machine learning (ML) method to decipher the complex relationship between precipitation and its influencing factors. After that, the contribution of each input variable to the precipitation in was is quantified by this ML method. The results indicate that the dew point temperature is the most crucial elements affecting the precipitation in Lazi, contributing 74% to precipitation in rainy season and 66% to precipitation in non-rainy season. Among aerosol components, nitrate shows the greatest influence on precipitation, accounting for 61% and 71% to the precipitation in rainy and non-rainy seasons, respectively. This result means that nitrate aerosols play an important role in precipitation.