The study utilizes SA Doppler weather radar data from Dalian and Dandong, combined with surface observations and ERA5 reanalysis data, to conduct a comparative analysis of the synoptic background and mechanisms of two mild-wet downburst events over the Liaodong Peninsula: the first on September 11, 2020 (Event Ⅰ), and the second on September 10, 2021 (Event Ⅱ).Results indicate that both events occur under the influence of a Northeast Cold Vortex.Event Ⅰ is associated with a cold vertex transformed from a northward typhoon, while Event Ⅱ involves a cut-off low.Both events are characterized by unstable atmospheric stratifications of "upper-wet and lower-dry", with skew-T diagrams showing an upward-opening "bell-shaped" structure.Near-surface temperature lapse rates are nearly dry-adiabatic.The precipitable water content exceeds 30 mm.The lifting condensation level is low, with CAPE and DCAPE values being relatively small.The 0-6 km wind shear intensity is significantly stronger in Event Ⅱ than in Event Ⅰ.The thunderstorm gale events in both cases are induced by wet microbursts, with Event Ⅰ involving an isolated storm and Event Ⅱ embedded within a squall line system.In both cases, the Doppler radar reflectivity notches exceeding 45 dBz appear earlier at higher elevation angles than at lower elevation angles prior to the wet microbursts.

Using surface and upper-air observations as well as the ERA5 hourly reanalysis data (0.25° × 0.25°), the study analyzes the causes of the first heavy snowfall event over the Loess Plateau on November 20, 2020, and investigates the lower-level cooling mechanisms responsible for the rain-to-snow transition during the initial phase of precipitation.Results show that the heavy snowfall event is supported by two moisture transport pathways, i.e., one from the southwest and the other from the southeast.The eastward merging and deepening of the westerly trough and plateau trough guide the eastward movement of the shear line, resulting in moisture convergence and uplift in northern Shaanxi province.The divergence of an upper-level jet stream further enhances the upward motion.Conditional symmetric instability characterizes the mid-atmosphere, and the cold front triggers the release of unstable energy, forming a slantwise secondary circulation along the front.Northern Shaanxi is located in the ascending branch of this circulation, with local maximum snowfall intensity exceeding 4 mm·h^-1.The rapid temperature drop near the surface during the initial precipitation phase is the key factor in the rain-to-snow transition, while the temperature forecast biases are identified as the main cause of errors in model-based rain-snow phase predictions.Lower-level abnormal cooling due to evaporative cooling facilitates the rain-to-snow transition, which deserves emphasis in associated predictions.

Using datasets of the hourly surface observation, upper-air observation, and ERA5 reanalysis, the study analyzes the precipitation phase characteristics and the causes of a freezing rain event in southern Heilongjiang province during the rain-snow weather process on November 8, 2021.Results show that the atmospheric circulation background features an eastward-moving and intensifying upper-level low vortex.The southeastern jet stream transporting warm and moist air provides moisture and energy for the precipitation, forming a warm layer that causes the freezing rain.The stratification over the freezing rain area exhibits a "cold-warm-cold" structure.Ice crystal growth produces snow in the upper cold layer, which melts into rain upon entering the warm layer.As the rain descends through the cold layer near the surface, it remained in a supercooled liquid state, forming freezing rain i.e.a process consistent with the "ice-phase melting" mechanism.The warm advection-induced warming in the mid-to-lower atmosphere is indicative of freezing rain, while the northeasterly in the lower layer contributes to cold advection.Sustained and strong northeasterly tends to enhance cold advection, causing a temperature drop in the cold layer and increasing the freezing rain intensity.Further comparison with a typical freezing rain event in Jilin province reveals that a strong warm layer and pronounced inversion layer significantly contribute to the intensified freezing rain.

Based on surface meteorological observation data, air pollutant monitoring data from 2014 to 2021 in Tai′an city, and ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), this study analyzes the characteristics of PM_2.5 and O₃ compound pollution, the meteorological causes, and the interactions between PM_2.5 and O₃ during compound pollution periods in this region.The results show that compound pollution by PM_2.5 and O₃ is a significant issue in Tai′an city, with monthly exceedance hours showing a unimodal distribution and significant seasonal variation characteristics.The compound pollution is more likely to occur under conditions of high temperature, high relative humidity, and low wind speed.Using Principal Component Analysis (PCA) and K-means clustering methods, the weather patterns associated with compound pollution in Tai′an city are classified into five types: rear of high pressure type, uniform pressure field type, saddle field type, front of weak low pressure type, and bottom of high pressure type, with the rear of high pressure type showing the highest frequency of compound pollution occurrence.Analysis of the interactions between PM_2.5 and O₃ in Tai′an city indicates significant correlations in both winter and summer.In summer, atmospheric oxidation is dominated by O₃ oxidation, and the contribution rate of atmospheric PM_2.5 concentration under various photochemical intensity levels shows that primary emissions surpass secondary formation.In winter, with weak atmospheric oxidation, increasing PM_2.5 concentrations, lead to reduced direct solar radiation and decreased O₃ peak concentrations, indicating that PM_2.5 has an inhibitory effect on O₃ formation.

Using CN05.1 precipitation observation data and ERA5 reanalysis data from 1961 to 2020, this study analyzes the interdecadal variations of precipitation during the crop growing season (May to September) in the Songhua-Liaohe River Basin and its relationship with moisture budget, and explores the link between total moisture budget and moisture transport in the basin.The results indicate that precipitation and the moisture budget during the crop growing season of the Songhua-Liaohe River Basin exhibit similar decadal variation patterns.Five moisture channels influence precipitation in the basin, with westerlies and the East Asian summer monsoon serving as the primary moisture sources.During the 1960s to 1970s, the moisture transport in the crop growing season was primarily dominated by the East Asian summer monsoon.In the 1980s to 1990s, both westerlies and the summer monsoon jointly influenced moisture transport.Since the 21st century, westerlies have become the dominant driver of moisture transport.

The study analyzes the spatiotemporal variation characteristics of annual, seasonal, and monthly precipitation of different intensity levels using daily precipitation data from 33 national ground meteorological observation stations in the Three Gorges region of the Yangtze River from 1961 to 2020.The results indicate that the Three Gorges region is abundant in precipitation, with an average annual precipitation of 1200 mm and an annual precipitation day of 155 d.The monthly variations of precipitation amount and intensity in this region are unimodal, with the peaking pattern in July; while the monthly variations of precipitation days are bimodal, with peaks in spring and secondary peaks in autumn.Over the past 60 years, there has been a significant decreasing trend in the annual precipitation days in the Three Gorges region, while annual precipitation intensity has significantly increased.However, there is no significant trend in annual precipitation amount.The number of precipitation days in spring, autumn, and winter has declined significantly, with a larger reduction in light rain days from October to December.Meanwhile, precipitation intensity has markedly increased in autumn and winter, with notable increases occurring in January, February, and November.The spatial distribution of annual precipitation days in the Three Gorges region shows a consistent decreasing trend across the entire region, while precipitation intensity generally shows an increasing trend; the distribution of precipitation amount changes is uneven, with a decrease in the central region and an increase in the eastern and western regions.

This study uses daily precipitation data from 61 national meteorological stations in Liaoning province from 1961 to 2021.A range of methods, including the percentile method, standardization processing method, correlation coefficient method, and weighted comprehensive index method were employed.Five assessment indicators for regional heavy rainfall events in Liaoning province were selected: maximum event precipitation, maximum daily precipitation, maximum hourly precipitation, heavy rain coverage, and duration.A quantitative assessment model for regional heavy rainfall events was established to analyze the spatiotemporal variation characteristics of the comprehensive intensity of regional heavy rainfall in Liaoning province.The results indicate that regional heavy rainfall events in Liaoning province occur from April to November, with a general duration of 1 to 4 days and a coverage of 4 to 30 stations.The frequency and comprehensive intensity of heavy rainfall are highest in summer, followed by autumn, and lowest in spring.Heavy precipitation levels above the heavy rain threshold are most frequent in the southeastern part of Liaoning, with strong precipitation more common in the southern part during spring, and in the southeastern part during summer and autumn.There is a significant interdecadal variation in the frequency and intensity of regional heavy rainfall events in Liaoning province.In the 1970s, heavy rainfall events were less frequent, whereas from the early 1980s to the present, they have been more frequent.The comprehensive intensity of regional heavy rain was stronger from the mid-1970s to the early 21st century, and has been weaker since the start of the 21st century.

Three types of soil textures (loam, sandy loam, and clay) from corn fields in western Liaoning province in 2021 were selected, along with different precipitation levels (light rain, moderate rain, and heavy rain) for continuous 7 days and every other day for 7 days after that.The relative soil moisture data for 0~50 cm depth was collected.The characteristics of soil moisture recession for different precipitation levels, soil textures, and soil depth recession coefficients (K) during the corn growing season in this region were analyzed, and a soil moisture prediction model was constructed.The results show that the soil moisture recession process in corn fields of western Liaoning can be divided into three stages: rapid recession, slow recession, and stagnant recession.The average recession rate during the rapid recession stage for the three soil textures was 7.21 mm·d^-1, with clay soil having a 9%-12% higher rapid recession rate than other soil textures.There were no significant differences in the slow recession and stagnant recession rates among different soil textures.The K value of the shallow layer (10~20 cm) was significantly smaller than other soil layers and had the largest dispersion.The fluctuation range of K values in the 50 cm soil layer increased significantly under heavy rainfall conditions.There was a significant negative correlation between K values and precipitation (R=-0.92).Under the same rainfall level, the K value of clay soil was 12%-20% lower than that of loam and sandy loam.The monthly variation of K values showed the highest in March, lowest in late August, and rebounded to a relatively high value in late November.Based on the soil moisture balance equation, a soil moisture recession model was constructed.The simulation accuracy was best for light rain levels, and the simulation results for shallow soil layers were better than those for deep layers.

The present study selected MODIS data and meteorological observation data from Inner Mongolia Autonomous Region between 2001 and 2021, and analyzed the seasonal variations of the Wetness Index in this region, as well as the response characteristics of the net primary productivity (NPP), gross primary productivity (GPP), and carbon use efficiency (NPP/GPP) of the forest ecosystem in eastern Inner Mongolia to drought.The results showed that the average NPP and GPP of the forests in eastern Inner Mongolia were 418.79 g C·m^-2·a^-1 and 703.12 g C·m^-2·a^-1, respectively, and the NPP/GPP ratio was 0.59.The spatial distributions of NPP and GPP were higher in the north and lower in the south, while the NPP/GPP ratio decreased from northwest to southeast.From 2001 to 2021, the NPP and GPP of the forests in eastern Inner Mongolia shifted from a stable pattern to a fluctuating growth pattern, with the NPP/GPP ratio showing a weak increasing trend, indicating an enhanced carbon sequestration capacity.In the eastern part of the Greater Khingan Range, the NPP and GPP showed an increasing trend, while the northern region showed a decreasing trend, and the overall change in NPP/GPP was relatively small.Summer drought had a stronger inhibitory effect on the increase of NPP and GPP, while autumn drought had a certain degree of influence on the NPP/GPP.

The effects of meteorological conditions on rice yield and nutritional quality were analyzed by using Duncan′s multiple extreme deviation test, Friedman′s rank test, correlation analysis, and yield and quality composite index in 2018-2019 using the 2018-2019 rice staged transplantation observations, meteorological data, and grain quality testing data in the Sanjiang Plain.The results showed that meteorological conditions had a significant effect on yield, which was highly significantly and positively correlated with ∑T_{≥ 10 ℃} at the heading-milk ripening stage, and this value of 684.8 ℃ was an indicator of the cumulative temperature for obtaining ideal yield in rice.Meteorological conditions significantly affected the amino acid and fatty acid contents of grains, and the correlation between various types of amino acid and fatty acid contents and meteorological conditions was unique.Overall, amino acid content was highly significant and significantly negatively correlated with ΔT at the heading-milk ripening stage and T_max at the transplanting-maturity stage, respectively; fatty acid content was highly significant and negatively correlated with T and T_max at the transplanting-maturity stage, and ΔT at the panicle initiation-maturity stage, respectively.The ΔT of 5.1 ℃ at the heading-milk ripening stage and the ΔT of 7.0 ℃ at the panicle initiation-maturity stage were the temperature indexes for obtaining ideal amino acid and fatty acid contents, respectively.Meteorological conditions had no significant effect on grain protein content, but on grain fat, starch, and straight-chain amylose content; fat and straight-chain amylose content decreased and starch content increased with delayed transplanting period.Overall, rice yield and nutritional quality were significantly and positively correlated with the T_min of the heading-milk ripening stage.In the Sanjiang Plain, the suitable transplanting period for rice is when the temperature is stable over 13.0 ℃, and the date of transplanting is around May 19, and then the rice cultivation can obtain high yield, high quality and high efficiency.It can be seen that rational control of crop sowing or transplanting period is an effective method to improve crop yield and quality in agricultural production, which can promote agricultural production to increase yield, quality and efficiency.

Multi-temporal MODIS image data of Heilongjiang province from 2017 to 2021 were selected, and based on Google Earth Engine (GEE) geospatial analysis cloud platform, the spectral reflectance of various types of features as well as the difference characteristics of indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were compared and analyzed.A classification decision tree was established to identify soybean planting areas in Heilongjiang province, estimate the area, and compare with other classification methods.The results show that the total accuracy of estimating the area of soybean planting areas in Heilongjiang province in 2018 identified based on the decision tree classification method is 97.09%, and the Kappa coefficient is 0.77, which is higher than the random forest and support vector machine methods in terms of classification accuracy.By adjusting and optimizing the decision tree model for soybean planting area identification and area estimation in the year without sample, the soybean distribution change in Heilongjiang province from 2017 to 2021 was obtained with a total accuracy of more than 90%, a Kappa coefficient of more than 0.60, and an accuracy of more than 95% for the area estimation results.

Observations from 10 national meteorological stations in Shanghai from 1961 to 2020 were used to calculate the changes in heat loads under different meteorological parameters for outdoor air-conditioning in winter.Combined with the carbon emission factor method, the impacts of changes in meteorological conditions on air-conditioning heat loads and carbon emissions in winter in Shanghai were quantified and analyzed based on the 2020 data The results show that the calculated outdoor air temperature of air conditioning in winter in Shanghai increases significantly, and the change of calculated relative humidity mainly decreases, with the largest variation of calculated air temperature and relative humidity in the central urban area, and the smallest in Chongming District in the northeastern part of Shanghai.Comprehensive air conditioning outdoor calculated temperature and relative humidity changes, the total air conditioning heat load in Shanghai in winter is a decreasing trend.Compared with 1961-1990, the total heat load decreases by 5.39% to 8.42% from 1991 to 2020.The carbon emission from air conditioning in winter in Shanghai can be reduced by about 55-4553 t by climate change.

Taking the main bridge project of HongKong-Zhuhai-Macao Bridge (HZMB), the longest cross-sea bridge in the world (divided into two sections, A and B), as the research object, based on the vehicle skid model to estimate the safe speed thresholds without side-slip under sunny and rainy days, wind force of 1 to 17, and a variety of wind angles, we have analyzed the trend of the change of the safe speeds with the wind angle, the wind force, and the sunny and rainy conditions.The results show that the most unfavorable wind angle for safe driving is about 75°, and the most unfavorable wind angles for section A of the HZMB are 120°, 150°, 300°, and 330°, while the most unfavorable wind angles for section B are 15°, 165°, 195°, and 345°.Comprehensive analysis of the wind direction and wind speed data from the representative stations for meteorological observation of the Bridge shows that the high risk season for vehicle sideslip on section A of the HZMB is summer, with August being the high-risk month; the high-risk season for section B is summer, with July being the high-risk month, and the risk indicator for section B is greater than that for section A.