We analyzed a thunderstorm gale event triggered by squall line over the central Hebei province on September 21, 2017, using conventional sounding data, observational data at surface automatic weather stations, National Centers for Environmental Prediction (NCEP) 1°×1° reanalysis data, satellite cloud images and data from Doppler weather radar.The results show that the thunderstorm gale event occurs at the bottom of a high-level cold vortex and is directly triggered by a squall line, under favorable conditions of cold air behind a short-wave trough overlying low-level warm advection.Both water vapor content and thermodynamic conditions reach the mean level triggering strong thunderstorm gales in North China.Compared with conditions in summer, vertical temperature lapse rate and vertical wind shear in autumn are more suitable for developing a thunderstorm gale, although energy is not as sufficient as in summer.The intensity and shape of the squall line are similar to those of the radar-echo characteristics during summer thunderstorm gales.However, a higher threshold of low-level radial velocity is required for the early warning of squall line gale in autumn according to the area of low-level radial velocity exceeding the threshold.The squall line in autumn is accompanied by mesoscale systems at the surface, such as a convergence line of the wind field, and thunderstorm high pressure.The effect of density flow in a cold pool favors the formation of strong winds near the surface.

Based on optimal track data of tropical cyclone from China Meteorological Administration-Shanghai Typhoon Institute (CMA-STI), conventional observational data from Meteorological Information Combine Analysis and Process System (MICAPS), National Centers for Environmental Prediction (NCEP) 1°×1° reanalysis data, and temperature of black body (TBB) data retrieved from the FY-2G satellite, we analyzed the causes of rainstorms occurring in the central part of Huanghuai area from 02:00 on August 17 to 14:00 on August 19, 2018, influenced by a landing tropical cyclone (TC) "Rumbia".The results showed that short-time heavy precipitation occurs at most meteorological stations, which can be triggered by the front or rear borders of convective clouds, the train effect of convective cells, the central area of deep convective clouds, and the emergence of convective cells, as well as non-convective clouds.On August 17, rainstorms occur in the central and southern Huanghuai areas, at the right side of the pathway of "Rumbia".A middle-level inverted trough is located westward to a low-level inverted trough, which is favorable for the rainstorm formation in the central Huanghuai area.On August 18, several meso-scale systems are influencing the rainstorm, including the middle northern side of "Rumbia", a low-level inverted trough, the easterly jets, the TC circulation, and weak cold air.The enhanced precipitation in the central Huanghuai area is resulted primarily from the increase in convergence of water vapor at 925 hPa and divergence of water vapor at 400 hPa, and the increase of middle- and low-level equivalent potential temperature.The increase of vertical wind shear is a good indicator of strong precipitation.After 20:00 on August 18, upper-level southwesterly jets begin to form, and southerly winds along the east of low-level inverted trough enhance and move northward.With the coupling of upper- and low-level circulation, rainstorm in northern Shandong province develops, and rainstorm in the central Huanghuai area becomes weak obviously.

To reveal the characteristics and causes of abnormal precipitation in the Sichuan Basin in July of 2018, we analyzed the characteristics of precipitation anomalies and atmospheric circulation, and the effect of water vapour, low system, and cold/warm air masses on precipitation based on statistical method and synoptic diagnostic methods and using precipitation data at meteorological stations and National Centers for Environmental Prediction (NCEP)/National Centers for Atmospheric Research (NCAR) reanalysis data from 1961 to 2018.The results show that rainstorms occur frequently over the Sichuan Basin in July of 2018, which results in anomalously higher precipitation compared with the climate mean state.The abnormal precipitation is characterized by more precipitation days and higher precipitation amounts, especially concentrating in the western basin.Meanwhile, the maximum daily precipitation and continuous precipitation day numbers both increase markedly, as well as the amount and day numbers of over heavy precipitation and rainstorm.Compared with the mean state of atmospheric circulation, the South Asia High is stronger and warmer and moves eastward, and the Subtropical High moves westward and northward in July of 2018, which are beneficial for the divergence of upper-level air over the Tibetan Plateau and its eastern areas.This results in the maintaining of precipitation weather systems and the transport of abundant water vapor to the basin.More water vapor is concentrated in July of 2018 compared with the mean state, coming from the South China Sea and the West Pacific Ocean.Water vapor is transported along the southern side of the Subtropical High, which is closely related to the anomalous northward and westward movement of Subtropical High and tropical cyclones.The period of high precipitable water vapor and high water vapor flux corresponds well with the precipitation process.Low system occurs frequently during precipitation events in July of 2018, with low-level warm and moist flows and middle-level (weak) cold airs, which favors triggering precipitation in the Sichuan Basin.

Rapid urbanization significantly modifies the urban morphology, leading to unfavorable wind environment at pedestrian level in neighborhoods.This study utilized a method based on exceeding probability and computational fluid dynamics (CFD) modeling to establish a general way and process on quantitative assessment of wind environment in neighborhoods.A case study was carried out in Chongqing Longfo neighborhoods on aspects of both frequent events and occasional events.The results show that the wind environment exhibits a dramatic regional difference due to complex underlying surfaces.The winds at open spaces, streets parallel to background wind directions and certain regions around tall buildings tend to be stronger than that in densely-built regions and the inner spaces of closed residential estate.The quality of wind environment of the former is graded as level 1b, and the quality of the latter is graded as level 1a.In general, the urban surface can act as frictional force which reduces the overall wind speed in the neighborhoods and further results in a favorable wind environment.These results could aid to promote fine service of urban weather and climate forecasting at neighborhood's scale, and enhance the ability of disaster prevention and reduction on urban meteorology.

We analyzed the characteristics of typhoon precipitation affecting Hainan Island and atmospheric circulation, and discussed the channels and sources of water vapor supply during process of the precipitation, using tropical cyclone track database from China Meteorological Administration and precipitation data at Hainan regional station from 1986 to 2016 as well as the Lagrangian airflow trajectory model.The results show that the effect of typhoon on Hainan Island and the typhoon precipitation mainly occur from June to October.In years with higher (lower) precipitation, the effect of cold air in the south of Yangtze River and the subtropical high is both relatively weak (strong), but that of the south trough is opposite.Meanwhile, the low level vapor flux field exhibits an anomalous cyclonic (anticyclonic) circulation.In years with higher precipitation, anomalous northeasterly flows and anomalously strong southwesterly flows usually affect the Hainan Island, which mostly come from the Northwest Pacific Ocean, the Indian Ocean, and the Bay of Bengal.However, in years with lower precipitation, moisture is mainly transported by the easterly flows from the Western Pacific Ocean and weak southwesterly flows from the South China Sea.The four major water vapor sources are the Western Pacific Ocean, the Bay of Bengal, the South China Sea and the Indian Ocean.The Western Pacific Ocean and the Bay of Bengal contribute most of water vapor in years with higher typhoon rainfall, accounting for 33% and 30%, respectively.There is sufficient water vapor supply from the east and west oceans.While in years with lower typhoon rainfall, Western Pacific Ocean provides most of water vapor, accounting for 38%, and the contribution of other water vapor sources is less than 30%.The main water vapor channel is located in the east of 110°E.

Based on the observation data from 65 stations in Liaoning province, the accuracy of the prediction products in different seasons was evaluated using the 2-meter temperature products within 12-36 hours from 2016 to 2018 predicted by the EC (ECMWF) model.The effect of the fixed error correction and optimal sliding period correction methods on improving the accuracy was analyzed.The results show that the accuracy of temperature in Liaoning province predicted by the EC model is as follows:that of the maximum temperature is the best in winter with the accuracy in the urban sites of 81.5%, and that of the minimum temperature is the best in summer with the accuracy in the urban sites of 84.3%.After the adoption of the optimal sliding cycle correction, the accuracy of the maximum and minimum temperature in Liaoning province from 2016 to 2018 is improved 19.7% and 20.5%, respectively, compared with the EC model.The prediction accuracy in the minimum temperature is higher than that in the maximum temperature.In whole spatial distribution, the prediction accuracy of the EC model for the maximum or minimum temperature in the central plain of Liaoning province is higher than that in the east and west parts, especially in the northeast, southwest, and southeast of Liaoning province.The accuracy in the areas affected by Changbai Mountain is significantly lower than that in other regions.At the same time, the correction prediction ability of the maximum and minimum temperature in summer is better than that in other seasons.Under the rainy and sunshine weather conditions, the correction test in temperature forecasted in Liaoning province is carried out.It is concluded that the test results have some supplementary effect on the correction of the maximum and minimum temperature in Liaoning province, especially when precipitation occurs in winter, the supplementary correction effect for the maximum temperature forecast is most significant.

To reveal the variation characteristics in temperature over the Zhongyue mountain region in the troposphere near the bottom of the free atmosphere in recent 60 years, based on homogenizing the monthly mean air temperature data at Dengfeng meteorological station, the simulated models of the monthly mean air temperature at Songshan meteorological station were constructed using the monthly mean air temperature homogeneous data of Dengfeng meteorological station.The missing data from 1990 to 2002 at Songshan meteorological station were interpolated using the monthly models to get the monthly mean air temperature data from 1956 to 2017.The trend of temperature change was analyzed using a linear regression method.The results show that an obvious correction effect is obtained using the homogeneous treatment to deal with the inhomogeneous effect of the monthly mean air temperature caused by the unnatural factors of station migration.After homogenization, the significant rising rate of the annual average air temperature at Dengfeng station from 1969 to 2017 increases from 0.218℃ per decade to 0.310℃ per decade.The increasing rate is 42%.The verification of the model with independent data shows that, on the whole, the linear correlation coefficient and slope between calculated model values and measured values are 0.999 and 0.989, respectively (n=204, p < 0.01).From January to December, the mean correlation coefficient of model verification and test parameters is 0.958, root mean square error is 11.7%, the average absolute deviation is 0.3℃, the average deviation is 0.1℃, the fitting index is 0.973, and simulation efficiency is 0.900.The model has a good simulation effect.From 1956 to 2017, the annual mean air temperature of Songshan station increases significantly, with a rate of 0.223℃ per decade.Among the four seasons, the temperature increasing rate in spring is the highest, with 0.350℃ per decade, followed by winter and autumn.The mean air temperature in summer does not increase obviously.The warming rate in February is the highest, at 0.445℃ per decade.

Based on the daily temperature in winter from 56 meteorological stations from 1961 to 2020 in Liaoning province, the temporal and spatial change in the average temperature and the climatic characteristics of warm winter events at a single station and the region scale were analyzed using climate tendency rate, IDW, sliding t test mutation analysis and wavelet analysis methods.According to the national standards of warm winter, the climate value in 1990-2020 was selected as the reference one.The results show that the average temperature in winter in Liaoning province increases at a rate of 0.3℃ per decade in recent 60 years.The warming trend is most significant in the central and eastern Liaoning province.In the first 30 years of the 1990s, it is a relatively cold period, but its warming effect is stronger than that in the last 30 years.The average temperature in winter shows an obvious abrupt change from cold to warm in around 1987.There is a relatively weak change from cold to warm in 1971.After 1988, the occurrence of warm winter events at a single station increases significantly compared with that in 1961-1987.The high frequency of warming events mainly happens at the central and southeast parts of Liaoning province, with 16-20 warming winter events.There are sixteen times of regional warming winter events in the recent 60 years.In the first 20 years after the 21st century, there are nine times of regional warm winter events, accounting for 60% of the total events.Regional warm winter events oscillate in periods of quasi-22 years and 2-3 years.

Based on the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) daily reanalysis data and the temperature data collected at conventional observation stations from the National Meteorological Information center, the characteristics of the large-scale extreme cold events in China were analyzed.The national extreme low temperature events were defined based on intensity, range and time, and thirty-eight large-scale extreme cold temperature events (LECES) were obtained, which were divided into five types according to different affected regions:national, Eastern, South and North China, Northwest and South China, and Northeast China.The circulation backgrounds of different styles of the LECES were also analyzed.The results show that there are precursor signals about 15 days before the occurrence of the nationwide LECES.An obvious blocking situation appears in the Ural Mountains.The upper-level jet stream at 300 hPa shows zonal direction in positive and negative, which strengthens the circulation system of East Asian monsoon and Siberian high, and the cold air accumulates in northern Asia.An extraordinarily strong anticyclone covers Siberia at 850 hPa, and most of China is controlled by northerly winds.Such a spatial configuration and evolution of circulation system in the middle and lower troposphere affects China and causes extreme cold temperature events.Besides, compared with the nationwide LECES, the other types of LECES have the precursor signal about five days before the event, with short duration and weak intensity.And the anomaly area of 500 hPa height field and sea level pressure field determines the type of LECES.

Based on comparative analysis of the monitoring data of PM_2.5 and PM₁₀ of three cities from Hainan province (Yongxing Island, Sanya and Haikou) from December of 2016 to May of 2017, the pollution characteristics in Yongxing Island were analyzed.The results show that, compared with Haikou and Sanya, Yongxing Island has the best air quality, the lightest fine particle pollution and the most stable daily variation of PM_2.5 and PM₁₀ mass concentrations.The main reason is that human production activities have little impact on air quality.Through further analysis of PM_2.5 mass concentrations of the three stations and near-surface meteorological elements (relative humidity, total monthly precipitation and visibility), it is found that the PM_2.5 mass concentration of Yongxing Island is negatively correlated with visibility as a whole, and the PM_2.5 mass concentration of Yongxing Island is the lowest at different wind speeds and winds, followed by Sanya and Haikou.The large PM_2.5 concentration area of Yongxing Island mainly appears in the upward direction of the northeast wind, while the air flow in the other directions is relatively clean.Moreover, the initial PM_2.5 mass concentration of Yongxing Island is relatively low under the conditions of calm wind or light wind.By analyzing the 72-hour backward trajectory six hours a day, it is found that during the winter and summer monsoon, Yongxing Island is respectively affected by the Marine air currents originating from the western Pacific Ocean and the South China Sea, which are directly related to the air quality of Yongxing Island.

Based on the data observed with visibility observers, wind profile radars, and from intensive automatic stations and conventional meteorological observations, the synoptic diagnosis of the two foggy weather processes and their responses to boundary layer thermodynamic conditions in Liaoning province in 2018 were investigated.The results show that the causes of the two heavy patches of fogs are quite similar.The fog experience two development phases under similar weather conditions.The first phase of the fog is radiation fog when the central part of Liaoning province lies in the weak convergence zone, the fog appears in the southerly wind area where the seawater vapor is transported to the Yingkou-Shenyang area.Then a weak ascending cooling cooperates with radiative cooling to form a near-surface temperature inversion.At the same time, the dew-point deficit decreases, and the relative humidity increases, leading to the explosive development of the fog.In the second development phase, the cold advection invades the near ground, causing the near-surface temperature to drop and forming inversion, and the fog develops again.So, the low-level cold advection intrusion is an important aspect that cannot be ignored in fog prediction, and its arrival time and location are the key influencing factors of fine fog prediction.

By using the QFW-6000 ground-based microwave radiometer in Fuxin Mongolian autonomous county in Liaoning province and its nearby sounding data, the retrieval accuracy of the microwave radiometer was evaluated, and the changing characteristics of the integrated cloud liquid water content (IIW), the integrated total water vapor content (TWV) and precipitation were analyzed.The results show that the inversion parameters of the microwave radiometer are significantly correlated with those of the sounding data.The retrieved relative humidity is generally greater than that of the sounding data, and the inversion errors of relative humidity in the near ground and the upper layer are both within 5%.Based on the statistical analysis of the IIW and precipitation, it is found that the IIW increases steeply and rapidly to more than 1 mm before the start of precipitation and remains above 2 mm as the precipitation continues, then quickly drops back below 0.2 mm with the end of the precipitation.The IIW and TWV in turn decrease in order of rainy days, cloudy days, and clear sky.Meanwhile, the TWV has a similar vertical structure under different weathers, and all show a decreasing trend with height increasing.In addition, the lapse rate of water vapor content is relatively smaller at a high altitude and larger in the near-surface layer.Besides, the vertical distribution structures of the IIW in the cloudy sky and the clear sky are similar with the maximum values of 0.15 g·m^-3 and 0.10 g·m^-3 at a height of 1.0 km, respectively.Whereas, the IIW on rainy days has two peak intervals respectively located at the heights of 1.0 km and 2.5 km.In the meanwhile, the IIW and TWV show the daily change characteristics with high values in the daytime and low values at night and in the early morning, while the cloud base heights show the opposite trends.

The daily effective temperature was calculated using the observation data from Jinhua meteorological station from 1969 to 2018, and the annual and seasonal average effective temperatures were calculated.The linear trend was used to estimate the change trend and the characteristics of the cold/hot/comfort days were statistically analyzed on multiple time scales.The results show that the average effective temperature of Jinhua city shows significant fluctuation and rising trend throughout the year and seasons.It crosses the average value steadily around 2000 and shows the increasing trend with an annual climate tendency rate of 0.67℃/10 a, and the fluctuation amplitude and upward trend are different among seasons with the largest in winter and the smallest in summer.The probability of warm or cold winter increases first, then decreases, and then increases slightly as the N-type, while the probability of hot summer or cold summer increases weakly.The comfort period shows a bimodal distribution, mainly concentrates in April-June and September-October, with the most comfortable days in May.The 50-year average beginning and end dates of the comfort period are April 4 and November 8, respectively.Over time, the initial date shows a significant advance trend (about 5.7 d/10 a), while the end date shows a significant delay trend(about 4 d/10 a).The overall climate comfort rate shows a weak upward trend.The numbers of comfortable days and hot days increase significantly with the rates of 5.08 d/10 a and 2.31 d/10 a respectively, while the number of cold days decreases significantly, reaching 7.39 d/10 a.On the whole, the winter in Jinhua city is milder, while the summer is hotter relative to foretime.Meanwhile, the comfortable time in spring increases significantly, while the cold uncomfortable time in autumn reduces significantly.

Based on the wind speed observation data of 24 wind towers in Liaoning and Jilin provinces, the linear regression method was adopted to revise wind speed forecast biases of the high-resolution mesoscale model.Firstly, the impacts of the training sample duration and rolling method on correction effectiveness were studied to determine the optimal scheme by four different calibration experiments, and the applicability of the station correction method on different underlying surfaces was synthetically analyzed.Then, the determined station correction relation from the 24 wind towers was used to correct gridded forecast wind field data, and the other 23 wind towers data were employed to assess the correction effect.The results show that the duration of the training sample has a direct impact on the correction effect.In the experiment area, the duration of 20-day for the training sample can achieve the best effect.When the training sample duration is 20 days, the correction effects of different sample selection methods are consistent.The prediction effect under various underlying surfaces can be significantly improved with the linear correcting method, and the improvement is the most obvious in the hilly area with the root mean square error (RMSE) reducing by 1.61 m·s^-1.The RMSEs in the plain and coastal areas decrease by 0.95 m·s^-1 and 0.91 m·s^-1, respectively.The overall correction experiments of the gridded wind speed data indicate that the extrapolation of the correction relation can achieve an obvious correction effect with the RMSE reducing by 0.20 m·s^-1.Therefore, the method can be effectively applied to the region where observation is scarce and will be available for modifying grid wind speed data in the future.

Based on daily air temperature, precipitation data in Chaoyang city during 1961-2016, and socioeconomic statistics data from 2007 to 2016, the evaluation system of urban adaptability to climate change including climate and urban subsystem was constructed with the analytic hierarchy process (AHP) method.The adaptive ability is divided into five levels according to the comprehensive index, and the membership degree of climate risk, city adaptation, comprehensive adaptability were dynamically assessed during 2007-2016.The result shows that the risk of climate subsystem increases significantly from a "little low" to a "general" level, which is attributed to the increases in disasters and extreme climate risks.With the development of the urban economy and the improvement of public services, the adaptation of the city subsystem is constantly improved from a "low" level to a "high" level.Under the combined effect of climate and city, the comprehensive adaptability of Chaoyang increases obviously from a "little low" to a "little high" level with a rate of 0.029/a in the past ten years.

Since 2014, the Institute of Atmospheric Environment, China Meteorological Administration, Shenyang has built a forecasting system of air quality for operation in Northeast China based on CUACE and CMAQ models.The research progress of air quality models and the current status were introduced in this paper to provide technical support for air quality and fog-haze forecasting in Northeast China.With the development of operational requirements for refinement and longer forecasting timelines, there are some problems, such as low forecasting accuracy, shortage of computing resources, and lack of technological innovation ability, exiting in the current operating system.The counter-measures are proposed to improve the forecasting level of air quality models in Northeast China, including strengthening the inventory research of air pollution source and formulating technical guidelines, the research and operation application of observational data assimilation technology, the improvement and optimization of physical process parameterization schemes, methods for error correction of numerical forecast products, the development of high-resolution models and maximum prediction time for 7-10 days, and introducing of talents and strengthening technological innovation.

Locust is the stubborn pest insects which can damage crops and ecosystems.Traditional methods for monitoring locust have many disadvantages.To effectively apply massive field image data to achieve real-time monitoring of locusts, a locust automatic identification model based on a deep learning network was established in this study.Firstly, 280 locust RGB images photographed by the mobile phone camera in a complex field environment from the grasslands of Xilinhot, Inner Mongolia were obtained.Then the Faster R-CNN network structure which performs better in recognition was used.The accuracy of this model is 0.756.The model performs well on locust detection and outperforms the previous methods in the identify results and practicality.The model can accurately identify the locust from the complex environment on fields, which provide auxiliary information for the control of locusts.It is a basis for establishing a real-time monitoring system for monitoring locusts.At the same time, the network structure can also be applied to other pests and diseases' monitor.In addition, the model broadens the application field of deep learning algorithms.