We analyzed the mesoscale convection process of a local rainstorm in Tianjin caused by the typhoon remnant vortex using the reanalysis data from the European Center, cloud driving wind data from FY-2G satellite, two Doppler radar network data from Tanggu in Tianjin and Cangzhou in Hebei province, wind profiles and observations from the automatic stations.The triggering conditions and causes were studied as well.The results show that before the local convection occurring, there are no good dynamical and humidity conditions near the ground in the north-central part of Tianjin.There is an anomalous temperature inversion layer.The thickness of the negative anomalous temperature (cold pad) area exceeds 50 hPa.A larger wind vertical shear exists under the southeast tilting airflow to make the horizontal vortex tubes distorted and results in a vertical vorticity.Above the boundary layer, a convergence line forms between the positive and negative vorticities, which triggers release of the unstable energy.Consequently, a local convection process develops over the north-central Tianjin and moves northwestwards, merging with its northwest linear polymer, which leads the short-time heavy rainfall in the northern Tianjin.

From June 30 to July 4, 2016, the strongest extreme rainfall hit the Yangtze basin since the flood season.However, in the operation, the prediction of the location of rain belts has a significant error.Therefore, based on the ensemble forecast data from the European Medium-Range Weather Forecast Center (ECMWF), this study analyzed the basic situation of the deterministic and ensemble forecasting using the method of synoptic diagnosis and discussed the causes of forecasting error.The results show that the sub-synoptic scale disturbance along the Meiyu front is dominant in the variability of the Meiyu rain band position, which appears a negative-positive-negative distribution in the region from Huanghuai-Liaodong Peninsula to the Korean Peninsula-eastern Japan.Its strength has an important influence on the change of rain belt position.When the disturbance is strong, it is favorable for the low-level monsoon to extend to the north, and the cold air intensity is weak, thus causing the location of the rain belt to be north, and vice versa.In addition, it is found that the synoptic-scale fluctuation originates from the initial error field in the northeast of Qinghai-Tibetan Plateau by comparing the accurate and northerly members of the ensemble forecast group.Along with the eastward propagation of mid-latitude westerly wind fluctuation, the error moves eastward along the Meiyu front in the middle and lower layers, and increases continuously, finally causing the rain belt in the middle and lower reaches of the Yangtze River to be significantly northerly.

The near-surface temperature has an important influence on the diffusion and transfer process of airborne radioactive materials.Global weather forecast products are important basic data and initial conditions for the diffusion model and air quality model.Their errors directly affect the accuracy of simulation results.In order to investigate the errors of different products, three kinds of global weather forecast products, the GFS (Global Forecast System), ECMWF (European Medium-Range Weather Forecast Center), and T639 were selected from June 2016 to May 2017.Using the observational data from 2100 ground stations in China, the near-surface temperature of the forecast products was compared and analyzed, and their performance characteristics in different seasons and regions were investigated.The results show that the temperature forecast of the three meteorological products in China tends to be lower.The annual mean value of RMSE (Root mean square error) is between 2.60 ℃ and 3.52 ℃.The annual mean value of the correlation coefficient is between 0.89 ℃ and 0.92 ℃.The annual mean value of the average absolute error is between 1.87 ℃ and 2.67 ℃.Overall, the performance of ECMWF is the best, followed by the GFS and T639.The temperature prediction error is different at different seasons.Based on the RMSE and mean absolute error of the three products, it indicates that the performances in summer and autumn are better.While based on the correlation coefficient, it shows that the performances in autumn and winter are better.There are obvious regional differences in temperature forecast errors.The spatial distribution characteristics in the temperature forecast errors from the three weather forecast products are similar.The error values are lower in the east of China and higher in the southwest of China.At the same time, the error is lower in the coastal areas and higher in the inner land areas.

The daily average, maximum and minimum temperature observation data of 61 national meteorological stations in Liaoning province from 1961 to 2018 and the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis) reanalysis data were used to quantitatively analyze the impact of urbanization on temperature changes in Liaoning province.The results show that the temperature in Liaoning province exhibits a significant increasing trend, and the temperature increasing trend of observation data is more obvious than that of the reanalysis data.The daily average, maximum and minimum temperatures show the fastest warming rate in winter, followed in spring and autumn, and the slowest warming rate in summer.In terms of urbanization influence contribution rate, the largest rate is in autumn, those in summer and spring rank in the second, the rate in winter is relatively small.In terms of spatial distribution, the influence of urbanization in most areas of Liaoning province is on the increasing trend, presenting a distribution situation that the central part is larger than the periphery, the eastern part is larger than the western part, and the southern part is larger than the northern part.The higher the level of urbanization, the more obvious the increasing trend of OMR (Observation Minus Reanalysis) value is.The influences of average, maximum and minimum temperatures on urbanization are 0.13 ℃/10 a, 0.045 ℃/10 a and 0.216 ℃/10 a, respectively.Their contribution rates of urbanization are 38.5%, 19.5% and 43.4%, respectively, indicating that the rapid urbanization process is an important factor leading to temperature increase in Liaoning province.

Based on remote sensing data from the MODIS satellite, the Ozone Monitoring Instrument (OMI) and the CALIPSO Lidar, the hourly surface concentrations of six criteria air pollutants observed at national environmental monitoring stations, and the surface meteorological data such as visibility and haze records, we comprehensively analyzed air quality and aerosol composition over Fenwei Plain during autumn and winter of 2013-2018 and investigated the relationship between the satellite-retrieved aerosol optical depth (AOD) and surface pollutant concentrations.In addition, meteorological conditions and the effects of emission reduction were assessed using the chemical weather forecasting system-EMI evaluation model (CUACE-EMI) developed by the China Meteorological Administration.The results show that six among the eleven representative cities suffer from air pollution for nearly or more than half of the whole autumn and winter.When air pollution occurs, the pollution level mostly reaches moderation and above at these representative cities.Haze and heavy haze occur frequently in Sanmenxia, Linfen, Yuncheng, and Xi'an, with the occurrence frequency of severe haze of 11.63%-14.78%.The primary pollutants are PM_2.5 and PM₁₀ and dominantly consist of polluted dust, desert dust, and smoke dominate, with the occurrence frequency of 36.24%, 25.14%, and 22.96%, respectively.The correlation coefficients between MODIS-retrieved AOD and air quality index (AQI), PM_2.5, and PM₁₀ concentrations are 0.72, 0.70, and 0.64, respectively.Compared with each year from 2013 to 2017, meteorological conditions contributes with an increase of PM_2.5 concentration in 2018 by 17.06%, 1.58%, 4.34%, 11.25%, and 5.75%, respectively, emission reduction measures results in a decrease of PM_2.5 concentration in 2018 by 8.74%, 28.01%, 4.93%, 3.16%, and 42.62%, respectively.

Based on observational data of daily air quality index (AQI), hourly PM_2.5 concentration, and meteorological parameters in Tangshan from 2015 to 2017, we analyzed the characteristics of heavy air pollution and meteorological conditions for formation and dissipation of PM_2.5 pollution.The results show that the day number of heavy air pollution exhibits a decreasing trend from 2015 to 2017, with an annual mean value of 36 d.Heavy air pollution events occur most frequently during winter, followed by autumn.The primary air pollutant during heavy pollution events include PM_2.5, PM₁₀, and O₃, accounting for 87%, 6%, and 7%, respectively.The hourly PM_2.5 concentration has a positive correlation with relative humidity, total cloud fraction, and 24-h temperature change, and has a negative correlation with wind speed, air temperature, wind direction, and hourly precipitation.Such correlation is highest in winter, followed by autumn and spring.The relative humidity is higher than 50% for 90% of heavy PM_2.5 pollution events, and almost 98% in winter and autumn.The proportion of heavy PM_2.5 pollution events in the presence of wind speed larger than 4 m·s^-1 is 0.7%, and precipitation has a scavenging effect on air pollution to some degree.Pollution events tend to occur under conditions of increasing air temperature and humidity and negative pressure change.The average wind speed during the formation process of air pollution is 1.8 m·s^-1, with the dominant wind direction of the southwest, followed by southerly and westerly winds.The reduction of air temperature and humidity and positive pressure change favor the dissipation of air pollution, and the average wind speed reaches 3.1 m·s^-1 during the dissipation periods, predominantly controlled by easterly winds and then northeasterly and northerly winds.Wind speed larger than 3 m·s^-1 has a scavenging effect on air pollution, and northerly winds perform better than other wind direction despite lower wind speed.

Based on air quality data at five national environmental monitoring points released on the national urban air quality real-time release platform of China environmental monitoring station and meteorological data observed at a national weather station in Karamay from 2015 to 2017, we analyzed the spatiotemporal variation of PM_2.5 concentration in four districts of Karamay and the impact of meteorological conditions.The results showed that PM_2.5 concentration is the highest in January, February, and December, followed by March and November in Karamay from 2015 to 2017.The highest PM_2.5 level is observed at Dushanzi during February each year, with a maximum monthly mean value of 134 μg·m^-3 in February 2016, which is 2.8 times higher than the national PM_2.5 standard value and reaches a moderate pollution level.In terms of seasonal variation, PM_2.5 concentration in Karamay exhibits obvious wave crest and trough.The highest PM_2.5 values occur in winter and then in spring in all districts, and have different characteristics in summer and autumn in different districts.PM_2.5 concentration during the domestic heating period is higher than that in the non-heating period.On average, PM_2.5 concentration is higher in the Dushanzi area, followed by the Baikali beach area, Karamay area, and Wuerhe area in order.PM_2.5 concentration has a significantly positive correlation with air pressure and relative humidity and has a negative correlation with wind speed, air temperature, and wind direction.The negative correlations with the air temperature and wind direction are especially significant.

Characteristics of ozone pollution in Chongqing were analyzed based on daily air quality data from 2014 to 2018.The results indicated that O₃ is the second primary air pollutants following PM_2.5, and has a distinct seasonal variation.O₃ pollution mostly occurs in summer and is more severe than PM_2.5 pollution in July and August.The annual mean O₃ concentration tends to increase year by year, and O₃ has become the first primary pollutant in Chongqing since 2018, with the day number of O₃ as the daily primary pollutant exceeding that of PM_2.5 for the first time.This suggests Chongqing is turning from a city mainly affected by particulate matter pollution to the one dominantly affected by ozone pollution.Air temperature, humidity, and air pressure are important meteorological factors influenc the O₃ level in Chongqing based on correlation analysis between daily meteorological factors and daily maximum O₃ 8-h moving average (O_3-8H) concentration.The daily maximum O_3-8H concentration is predicted using methods of stepwise regression, support vector machine, and neural network based on meteorological factors.All the three methods perform well and have small underestimations on average.The support vector machine method performs better in prediction of the daily maximum O_3-8H concentration than the other two methods and has a good implication for O₃ concentration prediction in Chongqing.

Based on the data on the daily temperature of the 23 representative stations from 1961 to 2017 and the growth period of double-cropping late rice of the 14 agricultural meteorological observation stations in Jiangxi province from 1984 to 2017, the spatio-temporal variation characteristics of cold-dew wind in different developmental stages of late rice were analyzed using the mathematical-statistical method.The results show that the mild cold-dew wind occurs in the highest frequency before mid-September in Jiangxi province.Specifically, it occurs once every 3 years in northern Jiangxi, three times every 10 years in central Jiangxi, and once every 5 years in south Jiangxi, and its frequency of occurrence increases gradually from southeast to northwest.The frequency of moderate cold-dew wind is relatively lower before mid-September in Jiangxi province and is once every 10 years in northern and central Jiangxi and once every 20 years in south Jiangxi.The frequency of severe cold-dew wind is extremely low before mid-September in Jiangxi province and is 0.4% and 1.2% in central and northern Jiangxi, respectively.Meanwhile, the frequencies of moderate and severe cold-dew wind increase gradually from south to north.The frequencies of mild and moderate cold-dew wind are 34.1% and 7.5% from the panicle differentiation stage to heading and flowering period of late rice and are 26.9% and 8.0% in the grain-filling period, respectively.The severe cold-dew wind has a relatively small effect on late rice and its frequency of occurrence is less than 2% in each developmental period of late rice.

This paper analyses the effects of relative soil moisture (RSM) on maize development period and yield so as to provide a scientific basis for the maize production in west Songnen Plain.Taking west Songnen Plain in Fuyu county of Heilongjiang province as the research region, using the RSM data during 1982-2017, the development period and yield of maize during 1995-2017, the change characteristics of RSM and their effects on the development period and yield of maize were investigated based on comparative analysis, correlation analysis, and Mann-Kendall mutation testing methods.The results show that the RSM experiences an increasing-decreasing-increasing change trend in recent 36 years.On average, soil drought occurs every 4-6 years during sowing-emergence, jointing-tasseling, milk-mature stage and occurs every 2-3 years during the tasseling-milk stage.Soil drought does not occur during the emergence-jointing stage.The mutation year of RSM with a decreasing and increasing trend for each development period is around 1987 and 2013, respectively.The RSM is suitable and the drought is lighter in the 1980s.In the 1990s, the RSM declines rapidly, which results in the most severe droughts.Then, the drought gradually eases with the passage of the year.Soil droughts occurring in sowing-emergence and seedling-jointing have little effect on maize yield, and the jointing-maturing period is the key period when a drought affects maize yield.

Based on the near-surface ozone (O₃) concentration data and the meteorological observation data of Qinhuangdao from January of 2013 to December of 2014 and from January of 2017 to June of 2019, this paper analyzes the correlation between O₃ concentration and temperature in spring, summer, autumn and winter using the generalized additive model (GAM), regression analysis method, and EmpowerStats statistical analysis software based on R language controlling the hybrid effect of related meteorological elements (including air pressure, relative humidity, sunshine duration, total cloud cover, etc.) and time variation trends.The results show that the O₃ concentration reaches its maximum in summer, and is relatively lower in spring and the lowest in winter, respectively, which is closely related to the seasonal change in temperature.In each season, the O₃ concentration shows a nonlinear correlation with the air temperature, and the fitted curve has an inflection point.However, the correlation relationships on both sides of the inflection point have clear difference.Specifically, in spring, the O₃ concentration increases by 7.6 μg·m^-3 with the 1 ℃ increase of temperature when the daily mean air temperature (DMT) is above 15.0 ℃ and the increase rate is the 4 times of that when the DMT is below 15.0 ℃.In summer, when the DMT is above 27.2 ℃, the O₃ concentration increases significantly with the DMT with a rate of 13.9 μg·m^-3 per 1 ℃ which is the 11.6 times of that below 27.2 ℃.In autumn, when the DMT is above 21.4 ℃, the ozone concentration increases significantly with the increase of temperature with a rate of 47.5 μg·m^-3per 1 ℃ which is the 19.1 times of that below 21.4 ℃.On the contrary, the variation of O₃ concentration in winter is relatively stable and is hardly affected by the temperature.Notably, the rapid increase of O₃ concentration under the high temperature in summer and spring should be paid high attention due to its high basic value.

Based on the real-time observation data from the 33 meteorological stations in Heilongjiang province from 2008 to 2018, and the national standard i.e."Grades of Atmospheric Self-purification Capability", the spatial and temporal variation characteristics of atmospheric self-purification capability (ASPC) were analyzed and the relationship between ASPC and air quality index (AQI) in Harbin was investigated.The results show that the average ASPC index is 12.6×10⁴ km·a^-1 from 2008 to 2018 with an increasing trend in Heilongjiang province.Since 2015, the ASPC has increased significantly.The ASPC is highest in spring, fewer in autumn, and lowest in winter.In terms of spatial distribution, the ASPC is low in the north and high in the south and includes two levels i.e.the second level and the third level.Specifically, the ASPC in the whole province is in the second level in spring, which is favorable to the removal of atmospheric pollutants, while that in Mehe is in the fifth grade in winter, which is unfavorable to the removal of pollutants.There is a significant negative correlation between AQI and ASPC index in winter in Harbin.Considering the ventilation and rain washing effect, the ASPC has a close relationship with the AQI and directly affects the air quality in Harbin.

Variations of the heating, cooling energy consumptions for office buildings in the five cities of North China were analyzed under the background of climate warming based on the meteorological data during 1961-2017 and records of heating, cooling energy consumptions.In addition, the dominant meteorological factors affecting buildings' heating and cooling energy consumptions were determined using stepwise multiple linear regression between the simulated energy consumptions and meteorological factors.The results show that heating and total energy consumptions in all the selected five cities significantly decrease during 1961-2017, with the decreasing rates between 0.05 kWh·m^-2·(10 a)^-1 (Shijiazhuang) to 0.13 kWh·m^-2·(10 a)^-1 (Huhehot).By contrast, there are different variations tendencies in cooling energy consumptions in the five cities, with a significant increase in Huhehot (increasing 0.04 kWh·m^-2 per decade) and no obvious variations in other cities.There are obvious decreasing tendencies in the total energy consumptions for all the cities, with decreasing rate of 0.05 kWh·m^-2·(10 a)^-1 (Taiyuan) - 0.10 kWh·m^-2·(10 a)^-1 (Huhehot).The stepwise multiple linear regression indicates that temperature is the dominant climatic factor affecting building heating energy consumptions in all the 5 cities.The increase in temperature during the past decades leads to a decrease in heating energy consumptions.By contrast, cooling energy consumptions are affected by temperature and solar radiation.Cooling energy consumption significantly increases in Huhehot due to the rising of temperature and solar radiation.No obvious variation trends of cooling energy consumptions in other 4 cities are dominant as a result of the offset of temperature (increasing) and solar radiation (decreasing).In general, under the background of climate warming, heating energy consumptions obviously decrease and no apparent variations are found in cooling energy consumptions in North China, which is beneficial for improving building energy efficiency.

Using the daily electrical load and meteorological data of Huaibei in Anhui province from 2012 to 2016, statistical methods such as correlation analysis, regression analysis, and curve fitting were used to analyze the seasonal variation and weekend/holiday effect of electrical load.Main meteorological influence factors were extracted.The effect of temperature (1 ℃) on electrical load and the sensitivity of electrical load to maximum temperature were also analyzed.The trend load and trend equation were determined in this study.The methods of applying weekend/holiday effects to different forecasting models and scientific methods of extracting meteorological load were introduced.The multivariate regression equation and curve-fitting equation of daily electrical load forecasting were established using the trend method.Considering the weakness of the trend method, a 2-day increment method was proposed.The corresponding forecasting model was established.Among them, the historical fitting rate of the 2-day increment forecasting model and the accuracy of the trial forecasting in 2017 both reach 96%-97% which is 2%-3% higher than those with the trend method, and 4%-5% higher than the current assessment requirements.In conclusion, a 2-day increment method improves the accuracy of electrical load forecasting.