Under the background of vigorously promoting the construction of ecological civilization, it is of great reference value to study the applicability of vegetation indices in vegetation monitoring in Hainan Island. Based on the NDVI (Normalized Differnce Vegetation Index), EVI (Enhanced Vegetation Index), DVI (Difference Vegetation Index) data extracted from MODIS (Moderate-Resolution Imaging Spectroradiometer), monthly mean temperature and precipitation data of 18 national meteorological observation stations from 2001 to 2020 and land cover data in 2015 and 2017 of Hainan Island, the applicability of three vegetation indices NDVI, EVI and DVI in vegetation monitoring of Hainan Island was studied by using the univariate linear fitting, root mean square error analysis and correlation analysis method. The results are as follows: (1) Among the three vegetation indices, EVI has the best fitting effect on the vegetation coverage area extracted from the land cover data of Hainan Island, the root mean square error accounts for 9.57% of the actual mean. (2) EVI can best reflect the seasonal variation characteristics of vegetation in Hainan Island, it began to increase slowly in February, slow decline after reaching its peak in August, and fell to the lowest in February next year; EVI has the widest range, which can best reflect the vegetation coverage with different thicknesses, and can better overcome the NDVI saturation problem in high vegetation area; EVI has the greatest correlation with climate factors and can best reflect the characteristics of vegetation's response to climate. (3) The fitting effect of three vegetation indices on the vegetation coverage area of Hainan Island was comprehensively evaluated, and the ability to characterize the seasonal variation and density of vegetation was evaluated. The EVI was determined to be the most suitable vegetation index for characterizing the vegetation characteristics of Hainan Island.

It is important to study the characteristics of precipitation anomaly in the Beijing-Tianjin-Hebei region to improve the understanding and prediction ability of precipitation in the autumn extended period. In recent years, the phenomenon of “summer rain in autumn” has occurred frequently in the Beijing-Tianjin-Hebei region, showing the characteristics of precipitation intensity increase and more extreme precipitation in autumn. The autumn precipitation in the Beijing-Tianjin-Hebei region in 2021 was the most since 1981, and the precipitation at many stations in October of 2021 broke the historical extreme values. Based on daily precipitation data in autumn and NCEP/NCAR reanalysis data in Beijing-Tianjin-Hebei region, the Morlet wavelet analysis and Lanczos filtering methods were used to analyze the low-frequency oscillation period of autumn precipitation and the evolution of atmospheric low-frequency circulation characteristics in the Beijing-Tianjin-Hebei region in 2021 in order to explore its abnormal characteristics. The results show that the main low-frequency oscillation period of autumn precipitation in the Beijing-Tianjin-Hebei region in 2021 is 10-20 days, and the variance of low-frequency oscillation is 44%. The low frequency circulation at 500 hPa during the low frequency precipitation activity period shows that there is convergence movement before the low-pressure anomaly, which is conducive to the strengthening of the low-level anomaly cyclone and upward movement. During the low frequency precipitation activity period, an abnormal cyclone moves northward from the South China Sea to the Beijing-Tianjin-Hebei region, which was conducive to the transport of warm and humid air from the south to the Beijing-Tianjin-Hebei region. The intensity of water vapor transport will affect the intensity of precipitation process. The stronger the intensity of water vapor transport is, the greater the intensity of precipitation is.

It is difficult to forecast heavy precipitation under complex terrain in mountainous areas， which formation mechanism is complicated， and often brings serious geological disasters. Based on conventional observation data， European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data， FY-4A satellite cloud imagery， Doppler radar data and forecast products from various models， the factors contributing and model forecasting performance of local short-time heavy precipitation process in the Hanjiang Basin of southern Shaanxi from the night on 3 to the early morning on 4 June 2022 were examined and analyzed. The results are as follows： （1） This process is a short-time heavy precipitation triggered by the front in the Hanjiang Basin of southern Shaanxi. Due to shallow convection instability and weak vertical wind shear， the heavy precipitation exhibited localized characteristics with significant intensity. The accumulated precipitation in 12 hours exceeds 50 mm in many stations， with a maximum of 104.8 mm. （2） The two ends of the front are blocked by the topography and move slowly and are difficult to cross the high mountains. Consequently， convection is continuously triggered within the basin， generating heavy precipitation， and the secondary circulation formed in the surface layer of the basin can enhance convective activity. （3） A cold pool formed in the front of front continuously triggers the backward propagation of new convective cells within the basin to form a train effect. Meanwhile， the intense radar reflectivity factor， exceeding 50 dBZ， is located below the 0 ℃ isotherm level， which has high precipitation efficiency and prolonged duration， thus bringing a short-time heavy precipitation with a maximum of 62.6 mm·h^-1. （4） Global models displayed limited capability in forecasting this process， while mesoscale regional models can reflect the characteristics of frontal convection and precipitation， especially CMA-TRAM and CMA-GD models can reflect the triggering and development trend of local strong convection well. However， the intensity and organization of the convective system induced by the frontal cold pool of the front still have substantial forecast deviations.

Convective turbulent dust emission (CTDE) is a new dust emission mechanism that thermal convective turbulent directly entrains dust particles into the atmosphere. Due to frequent occurrence, the long-term contribution of CTDE cannot be ignored. In the light of the previous research, the mechanism and influencing factors of CTDE are summarized, the parameterization schemes of CTDE are introduced, and the similarities and differences between CTDE and saltation-bombardment and/or aggregation-disintegration dust emission as well as dust devil are compared in terms of occurrence condition and dust emission flux. Finally this paper provides a reference for studying on CTDE and gives suggestions on field observation and improvement of parameterization for CTDE, including measuring CTDE events in potentially active areas, comparing the characteristics of CTDE between different dust sources, and building further correction functions of soil moisture and vegetation cover to improve the model performance.

Based on the Ji'nan S-band dual-polarization Doppler weather radar (CINRAD/SA-D) data, combined with automatic weather station data and conventional observation data, comparative analysis of the environmental conditions are made, and emphasis is laid on the analysis of dual-polarization signatures for the Wangzhuangji storm and Da'an storm, which are short for the two extreme rainfall storms separately occurring at Wangzhuangji county of Shenxian and Da'an county of Yanzhou, Shandong on 5 and 6 August 2020. The results show that: the two extreme heavy rain occurred in the conditions of high K index, large convective available potential energy (CAPE), deep wet layer, and moderately weak vertical wind shear. In contrast, the storm relative helicity (SRH) is evidently larger for the extreme rainfall event on 6 in August. The flow structures of the storms are significantly different: the Wangzhuangji storm tilts upward and intensively diverges at high-level inducing higher storm top and specific differential phase K_DP column, while Da'an storm performs as cyclonic rotation with weaker high-level divergence. The microphysical structure varies at different levels: for the both two storms, there are high concentration of solid (liquid) particles separately above (below) -10 ℃ layer. But Wangzhuangji storm has more abundant graupels above -10 ℃ layer, a few element of liquid particles from -20 ℃ to -10 ℃ layer, and a certain amount of ice particles below -10 ℃ layer. The two storms exhibit comparable dual-polarization characteristics with moderate differential reflectivity Z_DR, bigger K_DP and correlation coefficient (CC) at low-level, which indicate that the rainfall storms constructed with abundant moderate particle size liquid raindrops are rich of water favorable for extreme rainfall.

Based on winter mean temperature observation data in Xinjiang and Arctic Oscillation (AO) index and atmospheric circulation data, the relation and variation between mean temperature in Xinjiang and AO in winter were studied, the conceptual model of winter mean temperature prediction in Xinjiang due to AO influence under the climate warming background was established. The results show that in the process of climate warming, the relation between mean temperature in Xinjiang and AO in winter not only came from global warming, but also depended on AO change in the same period. Overall, the positive (negative) anomaly of temperature in Xinjiang in winter corresponded to AO positive (negative) anomaly. Since the global climate warming, the impact of AO on mean temperature in Xinjiang in winter was asymmetric. When the winter AO index was in positive phase, the corresponding air temperature in Xinjiang was higher than normal, the anomalous change of temperature in Xinjiang matched to AO anomaly in winter. However, when the winter AO index was in negative phase, the positive and negative anomaly of winter air temperature in Xinjiang depended on the intensity of the Polar vortex in northeastern Hemisphere and geopotential height anomaly in the east of 70°E longitude and middle latitude.

Based on the daily precipitation data of 66 meteorological stations in Fujian Province, NCEP/NCAR reanalysis data and NOAA daily OLR data, the large-scale circulation characteristics and it’s variation of drought events in Fujian Province from March to June 2018 were analyzed. The results show that less precipitation in every month, less rainstorm days and weakness of persistent rainstorm process led to severe meteorological drought in Fujian Province from March to June 2018. Affected by the La Niña event, Fujian was in the center of downdraft. The blocking highs were weaker, the West Pacific subtropical high was weaker and moving to the south and the east than usual, Fujian Province was in the anomalous center of water vapor flux divergence, which was the circulation background of drought. The variation of circulation showed that the blocking highs, the subtropical high and the northward propagation of tropical convection were not active in the earlier period, leading to less rainstorm days and drought development. In the later period, the circulation presented the characteristics of periodic oscillation, and the effective cooperation of the north and south system provided favorable circulation conditions for occurrence of persistent rainstorm, which alleviated the drought.

 Based on the wind lidar observation data and 12 wind shear events reported by pilots at Lanzhou Zhongchuan airport from July to November 2020, the performance and effectivity of two lidarbased lowlevel wind shear alerting algorithms were investigated. One algorithm is the regional divergence algorithm (RDA), and another is the combinational algorithm of single ramp and double ramps detection. The IRIS algorithm, the default algorithm in the lidar, was used as a reference. The conclusions are as follows: (1) RDA and ramp detection algorithm had better ability in alerting wind shear events under the convection weather or downward momentum weather conditions. However, they had a poor performance on identification of wind shear events under turbulent conditions. (2) Compared with ramp detection algorithm, RDA uses multiradial directions to reconstruct headwind profile, and consequently it can provide some temporal and spatial evolution information of radial velocities along the runway and its extension line, which is conducive to identification and alerting of wind shear events ahead of time. (3) The alerting results on 12 wind shear events indicated that RDA was superior to ramp detection algorithm and IRIS algorithm.

The precipitation forecast performance of GRAPES_Meso model with 3 km spatial resolution and its cloud analysis system was investigated and compared through numerical experiments for 13 heavy rainfall cases in summer and batch forecast in July 2018 in Northwest China. The results are as follows: (1) GRAPES_Meso model with 3 km spatial resolution had a good forecasting skill and stable performance for precipitation forecast in Northwest China, it could provide favourable outputs for short-term forecasting and nowcasting business. The average threat score of light rain and above for 13 heavy rainfall cases was between 0.5 and 0.6, while the threat score of batch experiments in July was slightly worse than that of 13 heavy rainfall cases. (2) By introducing observed data of radar reflectivity, satellite TBB and CTA, the cloud analysis system could reasonably adjust cloud water, rain water and other hydrometeor content, consequently reduce spin-up time in the mesoscale model and improve the forecast ability of precipitation with different magnitudes. However, the forecast results of cyclic assimilations in cloud analysis system weren’t stable. (3) The predicted coverage of radar echo by GRAESP_Meso model and cloud analysis system was consistent with observations, while the intensity of composite reflectivity was higher than the observations.

Based on the monitoring data of air pollutants from Xingtai Ecological and Environment Bureau and the corresponding meteorological observation data, this article analyzed an ozone pollution process in Xingtai of Hebei Province during 10-24 June 2018. The results are as follows: (1) In the process of pollution, the trends of ozone concentration were the same at four monitoring points in Xingtai. The ozone mass concentration over Xingtai Teacher’s College was the highest, and it was the lowest in Environmental Protection Agency. The diurnal variation of ozone mass concentration showed a single peak, the lowest mass concentration appeared from 05:00 to 06:00 and the highest mass concentration appeared at 15:00. The diurnal variation of ozone mass concentration was the largest over Xingtai Teacher’s College, and it was the smallest in Environmental Protection Agency. (2) The hourly variations of ozone mass concentrations were approximate under different weather conditions. The diurnal variation presented “single peak”. The ozone mass concentration changed dramatically on sunny days, and the change was flat on rainy days. (3) Ozone mass concentration was positively correlated with average temperature, maximum temperature, minimum temperature, solar radiation and the average of wind speed. The highest correlation coefficient was between ozone mass concentration and maximum temperature. The negative correlation was between ozone and NO2, PM10, CO, PM2.5. (4) After a intense radiation, if the highest temperature was equal to or more than 29 ℃ and the relative humidity was between 30% and 60% with a southerly wind, the hourly average mass concentrations of ozone would exceed the standard easily from 12:00 to 19:00.

Evaluating the accuracy and uncertainty of regional meteorological data sets were very important for the simulation of land surface process and analysis of climate change. Based on the sites observation data, the simulation accuracy of air temperature and precipitation of the China meteorological forcing dataset (CMFD), the atmospheric forcing data from 2000 to 2015 in the Heihe River Basin (WRFOUT) and the simulated forcing dataset of 3 km/6-hour in the Heihe River Basin (SFD) under different underlying surface types were evaluated, and uncertainty of  three reanalysis data sets were evaulated through three cornered hat (TCH) method. The results were as follows:(1) For temperature data, the WRFOUT data set had the highest accuracy in grassland, shrub land, desert bare land and wet ground surface, while the accuracy of CMFD data set was relatively high under the cropland, and among the five underlying surfaces, the temperature values of the three sets of reanalysis data had the highest accuracy in the shrub. For precipitation data, the accuracy of the CMFD data set under the five underlying surface types was higher, SFD datasets were overestimated in grassland, shrub land and desert bare land, while WRFOUT datasets were overestimated in grassland, farmland and wetland. (2) Through the TCH method, the uncertainty of temperature data in CMFD and WRFOUT data sets was lower, while the uncertainty of SFD data set was relatively large. The uncertainty of precipitation data of the three data sets was larger, and it was higher in the area with complex vegetation types and elevations.

Based on the daily mumps cases，meteorological data from 2005 to 2011 in Yinchuan，the epidemic characteristics of the mumps cases and relations with the meteorological factors were analyzed by using the methods of climatic inclination rate and the correlation analysis． The results show that the number of mumps cases presented increasing trend，the crowd from the age of 1 to 20 with mumps cases accounted for 93． 7% of the total mumps cases in Yinchuan，and they were the main easy infected crowd，while the crowd from the age of 6 to 7 was the key crowd of prevention and control． The monthly variation of mumps cases presented increase trend from February to May and September to December，while decline trend in summer and winter．The number of mumps cases accounted for 44． 4% and 5． 0% of the total number in Xingqing and Lingwu，respectively． The correlations were significant between the number of mumps cases and the weekly mean pressure，weekly mean temperature，weekly maxium temperature difference，mean relative humidity，weekly minimum relative humidity and weekly sunshine hours． The number of mumps cases was obviously affected by the meteorological factors of the first one to fourth weeks，so mumps cases could be forecasted by using the preceding meteorological factors．