Long-term numerical simulation can describe the characteristics and change rules of outdoor thermal environments more comprehensively, which has an important theoretical and practical significance.At present, the meteorological data input in the simulation is mainly the typical meteorological days generated by the Finkelstein-Schafer (FS) statistical method or principal component analysis method, which is not continuous enough to describe the simulation scene.The solar term knowledge originates from the middle and lower reaches of the Yellow River in China.By combining the solar terms knowledge with the FS method, the typical solar terms with meteorological representative days are selected, and the solar term′s typical meteorological days (STTMD) can be generated for long-term outdoor thermal environment simulation.Based on the meteorological observation data from 1981 to 2010 and 1991 to 2020, the STTMD meteorological element data series of eight stations such as Zhengzhou and Ji′nan are generated, and the meteorological representation and application scope of the data were analyzed.The results show that the data has more accurate meteorological characterization in the middle and lower reaches of the Yellow River, which can greatly simplify the numerical simulation of long term, and has the possibility of further deepening and extending application.It is also found that the phenological significance of the data is not accurate enough for regions outside the middle and lower reaches of the Yellow River.
Using the data from the automatic field microclimate observation station in winter wheat main producing areas in Northwest-Shandong Plain of China and the national meteorological observation station in Qihe from January 2019 to June 2022, the microclimatic characteristics of winter wheat fields in different growth stages were analyzed, and the difference of climate between two observation stations was compared.The results were as follows at various growth stages of winter wheat, the air temperatures at different layers, total radiation, photosynthetically active radiation (PAR), wind velocity, and soil temperatures at the depth of 5~20 cm all present an obvious unimodal diurnal variation and the diurnal variation of relative humidity is opposite to that of air temperature.Air temperature in wheat fields decreases and increases with the increasing height above the ground in the daytime and nighttime, respectively.The relative humidity is highest at the height of 30 cm above the ground in all growth stages and reaches the maximum in the nighttime at the late growth stage and in the daytime at the middle growth stage.Soil temperature in each layer is highest at the late growth stage, and their diurnal variations decrease with the increasing soil depth.Daily average air and soil temperatures in farmland are lower than those in Qihe station, and the daily average relative humidity in farmland is higher than that in Qihe station.Both the total radiation and PAR are strongest at the late growth stage.The wind speeds of all layers show the same change trend at the early growth stage of winter wheat, while the wind speed at the height of 60 cm is most intensively affected by the growth and development of winter wheat.The air temperatures and relative humidities at different layers and soil temperatures at the depth of 5~10 cm in farmland show obvious diurnal changes under different weather conditions at the middle and late growth stages, and the above-mentioned situation is the most obvious in sunny days, followed by cloudy days and the weakest in overcast days.
Using the data of encrypted automatic meteorological observation station, typhoon real track data, NECP reanalysis data, and FY-4A satellite TBB data, the causes of the complex path and heavy rain of typhoon "LUPIT" (No.2109) were comprehensively analyzed.The results show that the strong subtropical high in late July and the strong monsoon convergence zone along the coast of South China provide good energy and water vapor conditions for the offshore generation and development of typhoons.The slow movement of the typhoon, the strengthening of the upturning cooling effect of the seawater, the increase of vertical wind shear in the environment, and the weakening of the divergence conditions in the upper level make it impossible for typhoon "LUPIT" to strengthen rapidly offshore.The subtropical high ridge line of tropical high-pressure retreats rapidly to the south, and the change of deep guiding airflow and the interaction of the two typhoons make "LUPIT" appear an unusual "S" path.Water vapor transported by the southwest monsoon, the strong influence of low-level jets, the typhoon′s inverted trough, and the asymmetry of the typhoon′s structure are the main reasons for the uneven distribution of heavy rain and falling areas in Zhangzhou.
Based on the dual polarization Doppler weather radar high altitude observation data, encrypted automatic station data, and ERA5 reanalysis data in Yingkou of Liaoning province, a thunderstorm gale process formed by the collision of sea breeze front and gust front in the northern Liaodong Bay on May 28, 2021, was analyzed.The results show that the mesoscale sub-cold front moves southward and forms a surface convergence line that confronts the northerly wind and the southwest sea breeze, which triggers strong convection in conjunction with dryline.Before the collision between the sea breeze front and the gust front, the echo centroid of the individual in the mature stage of the upstream sinks, and the sinking outflow promotes the rapid development and enhancement of the downstream monomer.The cold pool density current plays a key role in triggering and enhancing the downstream cumulus convection.The surface temperature is increased by the daytime clear sky radiation warming, which is conducive to the accumulation of convective effective potential energy.The unstable stratification and strong vertical wind shear in the Panjin region lead to the continuous development and strengthening of the southern pressure of the convective system.A conceptual model of thunderstorms generated by the frontal collision of the sea breeze front and gust front in this area is initially established.The northern sea breeze front collides with the gust front generated by southward convection in Liaodong Bay, and the air climbs northward along the gust front.Under the influence of the northward guiding flow, the northward moving component of the convective system is canceled out, and the southward convective system continues to develop under better environmental conditions.This will maintain the strength of the convection and affect the downstream areas of thunderstorms and gales.
Using the conventional observation data and NCEP FNL reanalysis data, the causes of a failed forecast snowstorm process were analyzed in the Liaoning region in early 2016.The results show that there are frontogenesis activities in the heavy snowfall area, and the frontogenesis is the direct cause of the intensification of snowstorms.Continuous frontogenesis occurs during snowfall, and the total frontogenesis function develops higher with time.The positive contribution of the horizontal deformation term to frontogenesis is greater during the heavy snowfall stage, especially when the contribution of elongation deformation to the horizontal deformation term is significant.The northeast wind in the eastern Liaoning provice and the easterly wind in the northern Yellow Sea both pass through the θse line, and the two air currents converge to form a narrow band with more dense θse contour lines.The clear contrast of temperature and humidity in this area produces frontogenesis.Under frontogenesis, the secondary circulation of the front is conducive to the development and maintenance of the upward movement, and the dynamic lifting and the enhancement of water vapor convergence lead to heavy snowfall weather.The frontogenesis caused by the enhancement of elongation deformation in the forecast is easy to ignore, which is the main cause of the snowfall forecast error.Therefore, the snowfall amount in the corresponding region can be revised to reduce the forecast error.
To reduce the economic losses and improve the ability of tea production to resist meteorological disasters, the risk analysis of frost damage during the spring tea growing season was carried out based on the daily minimum temperature data of 84 meteorological stations in Guizhou province from 1961 to 2020, and the frost damage risk index was constructed using the methods of mathematical statistics and spatial analysis.The results show that light frost damage is the main disaster of spring tea in Guizhou province.The number of days frost occurred and the station ratio of frost for each grade shows a decreasing trend.The frost damage risk level is high in the west and the north, becomes lower in the east and the south.The areas with sub-high risk are mainly distributed in the north of Liupanshui, the most of the west and the middle of Bijie, the part of the northern Guiyang, the part of the Daloushan in the north of Zunyi, Fanjing Mountain Area in the middle of Tongren, Leigong Mountain Area in the southeast of Guizhou province and Doupeng Mountain Area in Southern Guizhou province.The total area is about 3.7×104 km2, accounting for 21.7% of the entire area of Guizhou province.The lowest risk areas are mainly distributed in the Beipanjiang River Basin in the southwest Chishui River Basin in the north and Duliu River Basin in the southeast and the area is about 2.34×104 km2, accounting for 14.3% of the total area of Guizhou province.
Based on the precipitation enhancement operation data of aircraft from 2016 to 2019 and inversion products of FY-2E/2G/4A meteorological geostationary satellite, using the method of determining the affected area of precipitation enhancement by HYSPLIT model and the analysis method of satellite inversion product and radar product, the response of cloud physical parameters of satellite inversion after precipitation enhancement seeding was analyzed, the cloud physical characteristics and evolution law after precipitation enhancement seeding were obtained, and the comprehensive application ability and benefits of meteorological satellites in the inspection and evaluation of artificial precipitation enhancement effect were improved.The result shows that: for most of the precipitation enhancement operations in the study, the four cloud physical parameters under the influence of natural variation and artificial seeding reach the maximum variations in 2~3 hours after the end of seeding.For the 9 aircraft precipitation enhancement operations, average variation ranges of cloud top temperature, cloud effective radius, optical thickness, liquid water path, and hourly precipitation in 3 hours after the end of seeding reach -1.44~2.09 ℃, -3.73~3.84 μm, -5.47~3.62, -112.59~61.12 mm, -0.06~0.27 mm, respectively.Cloud top temperature and hourly precipitation are increased, and cloud effective radius, optical thickness, and liquid water path are decreased for more than half of the precipitation enhancement operations, and precipitation for individual operations is reduced.For different cloud bodies, when the distribution of liquid water and the structure of the cloud system vary dramatically and are very uneven, the variations of cloud physics and precipitation induced by precipitation enhancement seeding will be significantly different.At present, for the precipitation variation induced by natural variation and artificial seeding, it's difficult to eliminate the influence of the two factors on precipitation variation scientifically and completely.
Using daily pollen concentration samples from 12 stations in Beijing during March 1 to September 30, from 2012 to 2020, spatial-temporal distribution characteristic of pollen concentration was analyzed.The results show that there are two peaks of pollen concentration with one in the spring pollen period (March-May) and the other in the summer-autumn pollen period (Autumn-September).The pollen concentration peak in spring is 3.3 times higher than that in summer and autumn.Pollen concentrations of 75% stations show an increasing trend during the studied period.The pollen concentration is lower during the intermittent period with its unobvious increasing trend.While in spring and summer-autumn, pollen concentration is higher with its obvious increasing trend.As a whole, daily average temperature, daily average relative humidity, and total daily precipitation are negatively correlated with pollen concentration.This result is consistent among the 12 stations.In addition, there is a stable correlation between wind speed, humidity, rainfall, and pollen concentration.However, the correlations between temperature, air pressure, and pollen concentration in different periods are more complicated, or even opposite.
Based on the data of precipitation observation at Liaoning national station and regional automatic station, doppler weather radar, wind profile radar, and hourly reanalysis data of the ECMWF ERA5, the formation mechanism of extreme disaster-causing thunderstorms and gales in Shenyang from the evening to the night of June 25, 2022, and the prediction effect of the numerical model were analyzed.The results show that the extreme hailstorm occurs under the background of the northeast cold vortex, the shear line, and the ground convergence line jointly trigger the generation of convective storms, and the strong vertical wind shear and unstable stratification enhance the development of hailstorms.In the process of the strong hailstorm, several strong convective cells gradually develop and appear as bow echoes after consolidation and enhancement.The existence of shallow low-level gamma mesoscale vortices in the front of the bow echo can lead to the enhancement of downdraft, and the combination with the backward inflow jet can lead to local extreme wind.The mesoscale numerical model can predict the circulation situation and precipitation area of severe convective weather to a certain extent, but the prediction of the intensity of the convective storm is still difficult.
Based on observation data of carbon fluxes in the Liaohe River Delta from 2019 to 2020, the temporal change features of carbon fluxes in Phragmites communis wetlands in the Liaohe River Delta were analyzed and spatial representativeness of carbon fluxes was further investigated using the Kljun model.Results show that the net absorption cycle of Phragmites communis wetlands was 156 d under the influence of periodic changes in air temperature and shortwave radiation in 2019, the NEE peak value of -40.59 μmol·m-2 s-1 appeared in the first and middle ten-day of July, and carbon sequestration of 0.561 kgC·m-2 was higher than that in 2020.In 2020, the net absorption cycle was 131 d, and the NEE peak value of -41.39 μmol·m-2 s-1 appeared in mid-June, which was earlier than in 2019.The lower carbon sequestration ability was caused by stronger respiration at night during the growing season.Carbon fluxes showed an obvious "U-shaped" diurnal variation in spring and summer and varied stable in autumn and winter.The maximum daily average carbon fluxes in summers were -16.86 μmol·m-2 s-1 in 2019 and -13.61 μmol·m-2 s-1 in 2020, and appeared at 11:00 in 2020, which was earlier than in 2019.In addition, the distribution areas of the prevailing wind direction and the farthest distance contribution point of 90% of the unstable stratification status were in the direction of 0°~90° and the direction of 180°~270°.The average windward distance of 80% of the flux source region was 120 m at the farthest, while the flux contribution peak value appeared at 10 m far from the flux tower.The range of the source region expanded gradually with the increase in atmospheric stability, and that of 70% in the prevailing wind direction was distributed with reed vegetation providing 76.20% of carbon flux information, while the water body contributed 17.82% of carbon flux information.Flux observation data were at the representative level in the daytime of summer and an acceptable level all year round.The research helps to know the capacity and role of the wetlands ecosystem in the Liaohe River Delta in carbon cycling and carbon emission reduction.
The long-term monitoring data of CO2 concentration in the Longfengshan regional atmospheric background station from 2009 to 2019 were used to analyze the daily, monthly, and interannual variation trends of CO2 concentration in Northeast China.The meteorological driving factors affecting the CO2 concentration of atmospheric background stations in the Longfengshan region were discussed.The results show that the diurnal variation of CO2 concentration in Longfengshan atmospheric background station is the largest in summer, followed by autumn, spring, and the smallest in winter.The daily peak value occurs before 08:00 and the lowest value occurs around 16:00.The monthly variation of CO2 concentration shows that the maximum value appears in January, with an average value of about 416.1×10-6, and the minimum value appeared in July, with an average value of 391.1×10-6.The seasonal variation of CO2 concentration shows that the CO2 concentration in winter is significantly higher than that in other seasons, with an average value of 415.4×10-6, and the lowest value appeared in summer, with an average value of 395.8×10-6.During the study period, the average CO2 concentration of Longfengshan station shows an increasing trend, with an annual growth rate of 2.45×10-6/a.At the Longfengshan background station, the maximum frequency wind direction appears in SSW direction in spring (21.0%), the maximum frequency wind direction appears in SSW and S direction in summer (23.1% and 22.7%), the maximum frequency wind direction appears in SSW direction in autumn (23.4%), and the maximum frequency wind direction appears in SW direction in winter (31.6%).The high CO2 concentration in different seasons occurs under the north wind dominant condition.There is a negative correlation between CO2 concentration and temperature (- 0.54), and a significant negative correlation between relative humidity and CO2 concentration in spring and autumn (- 0.44 and - 0.55).There is a small correlation between CO2 concentration and wind speed, which may be related to the transport of higher-concentration CO2 sources.
It is generally believed that a high vertical resolution numerical weather prediction model can improve the prediction ability of the fog formation stage, but there is much debate on whether the simulation ability of the fog development and disappearance stage can be improved.To explore the applicability of a high vertical resolution model for radiation fog simulation in the Bohai Sea Rim, three test indexes including probality of detection (POD), false alarm rate (FAR), and equitable threat score (ETS) were used to evaluate the simulation effect of different vertical resolution models for different stages of radiation fog process in the Bohai Sea Rim.The results show that setting a higher vertical resolution in the upper layer of the model has little effect on fog simulation, while setting a higher vertical resolution in the lower layer can generally improve the simulation ability of fog formation time, fog duration, and fog layer thickness, but the simulation of fog region highly depends on the fog distribution pattern.For the scattered and non-uniform fog process, the high vertical resolution model seems to improve the POD, but it is accompanied by the increase of FAR and the decrease of ETS.For the uniformly distributed fog process, the high vertical resolution model can improve all the test indexes simultaneously, and effectively improve the prediction of such type of fog process.
Using the MWP967KV ground-based microwave radiometer data at Shanghai Pudong International Airport Ground Meteorological Observation Station from 2018 to 2019, the civil aviation surface observation data, and the conventional sounding data of Baoshan Station, we analyzed the reliability of the microwave radiometer detection data.On this basis, we calculated the atmospheric parameters related to the occurrence of thunderstorms, selected the appropriate parameters as the forecasting factors, established the BP (Back Propagation) artificial neural network model for airport thunderstorm forecast, and evaluated the forecasting effect of the model.The results show that the mean absolute deviations of temperature, relative humidity, and water vapor density obtained by the MWP967KV ground-based microwave radiometer with the corresponding sounding data are 1.94 ℃, 16.05%, and 0.82 g·m-3, respectively, the root mean square errors are 1.41 ℃, 20.14%, 1.90 g·m-3, respectively, and the correlation coefficients are 0.99, 0.66, and 0.85, respectively.The established BPNN model can predict the occurrence of thunderstorms accurately.The forecast accuracy rates of 2 h, 3 h, and 6 h reach 93.27%, 93.33%, and 89.47%, respectively, and the missing rates are 6.73%, 6.67% and 10.53%, respectively.The reporting rates reach 4.90%, 4.78%, and 2.86%, and the critical success indices reach 89.99%, 80.33% and 81.18%, respectively.Therefore, this study realizes the intelligent forecast of thunderstorms to some extent, and the model can be applied to the forecasting and early warning of thunderstorm weather at airports and single stations.
选用VLF/LF(ADTD_2C)三维闪电监测资料和双偏振雷达资料,对2017-2020 年福建省31 个冰雹、32 个短时强降水单体闪电特征进行统计分析.结果表明:福建省多数冰雹云单体降雹前闪电频数超过 50 次/6 min,而强降水单体超过50 次/6 min的较少;冰雹单体正地闪和正云闪比率较高,强降水单体则较低.80%冰雹单体闪电频数峰值较降雹时间提前3～25 min,并在降雹前出现总闪频数跃增,递增率多数大于4 次/min.超过 1/2 的强降水单体,其闪电频数峰值时间较降水峰值提前2～35 min,闪电频数在降水峰值前增大,递增率多数小于4 次/min.两类单体成熟阶段的云闪频数最高,云闪主要集中分布在2～6km高度层.冰雹单体中,融化层以下为冰雹和大雨粒子组成的低层,以上为冰雹和霰组成的高层;强降水单体中,融化层以下为大雨粒子组成的低层,以上为冰晶、过冷水滴组成的高层.闪电频数与强回波中心高度和强回波伸展高度均为正相关,对流发展高度越高,冰相过程越显著,闪电活动越强.
利用常规气象观测资料、颗粒物监测及激光雷达探测数据,采用天气学分析、空气质量轨迹追踪模型,对 2021 年3 月呼和浩特市发生的一次严重沙尘污染天气成因及传输特征进行分析.结果表明:此次污染过程有两次污染物浓度峰值,可分为两个阶段.3 月14-15 日受高空低槽和地面冷锋共同影响,呼和浩特市出现大风、扬沙、沙尘暴和强沙尘暴天气,PM2.5 在15 日03:00 升至1382 μg·m-3,严重污染时间长达15 h;3 月16-18 日高空为弱脊后西南偏西气流,地面气压场减弱,近地面扩散条件较差,发生污染物积累和沙尘回流,导致严重污染长时间持续.激光雷达数据表明,3 月14 日白天呼和浩特高空有大量细粒子聚集,15 日凌晨该地区首要污染物由PM2.5转为PM10,12:00 PM10浓度升至4099 μg·m-3,16 日沙尘回流,再次出现严重污染;17 日污染物浓度开始降低,18 日 05:00 此次沙尘天气结束.后向轨迹分析表明,蒙古国为此次严重沙尘污染的主要源区.
选用2015-2018 年安徽省淮北市6 种主要污染物浓度月变化数据,利用聚类分析、潜在源分析(PSCF、CWT)、T-mode斜交旋转分解(PCT)等方法,分析淮北市冬半年重污染的传输通道、潜在源区,重污染天气不同阶段(形成、维持和结束)的大气环流类型.结果表明:淮北市冬半年PM2.5、PM10、CO、NO2 和SO2 的月均质量浓度较高.短距离轨迹为当地冬半年重污染的主要输送轨迹,山东西部、江苏中北部是淮北市重污染的重要潜在源区.持续性重污染形成、维持及结束期间的环流场存在异同点,共同点为3 阶段均存在占比不同的均压场,不同点为大气环流分型、气压值、气压梯度、风向风速等存在差异.持续性重污染形成期间,大气环流可分为5 种类型,其总次数的90%与均压场有关;持续性重污染维持期间6 种大气环流类型均与均压场有关,但范围明显大于形成时期,气压值与形成时期相近,风速普遍为静风;持续性重污染结束期间淮北市多处于冷高压前沿,均压场仅占15%,气压梯度、海平面气压值及风速均显著增大.
应用红外云图亮温阈值法判断对流云团,统计分析2015-2019 年中国长三角地区盛夏副热带高压(副高)控制下的局地对流天气时空分布.结果表明:副高触发的对流天气频次日变化为午后单峰型,对流天气的高频区位于大城市附近、江南丘陵地区以及水陆交界处,而江河湖面、峡谷和盆地较少.副高偏南型的对流天气高频区位于山区和沿海地区,其低层处于有利于下垫面加热的偏西风场;副高偏北型高频区位于内陆,其低层的东风降低了沿海地区的下垫面温度,对流触发频率较小.不同对流参数的统计表明,对流有效位能对局地对流天气的触发有较明显参考.沿海地区的副高偏南型对流有效位能略高于副高偏北型,内陆地区相反.副高偏南型各站的抬升指数和抬升凝结高度均较低,表明该类型天气的不稳定性较强.
选用1961-2019 年中国东北地区204 个站日降水量资料、NCEP/NCAR逐月再分析资料及美国国家气候预测中心(CPC)提供的Ni?o3.4 指数,利用相关分析、小波分析、M-K突变、合成分析等方法,分析东北地区夏季降水变化特征,探讨其与前期厄尔尼诺—南方涛动(El Ni?o-Southern Oscillation,ENSO)的联系.结果表明:中国东北地区夏季年降水量呈递减趋势,以2.39 mm/10 a速率减少,空间变化呈西南部减少、东北部增加趋势,辽宁地区减少趋势最显著.夏季降水存在显著的2～3a、6～7a和8～9a振荡周期.1961-1982 年和1998-2019 年为相对干旱时期,1983-1997 年为相对湿润时期,突变年份为1983 年和1998 年.中国东北地区夏季降水与前一年夏季 ENSO 关系密切,1997-2019 年二者呈显著的正相关.1997-2019 年,前一年夏季Ni?o3.4 区海温异常变化,引起水汽输送异常和局地垂直运动变化,对东北地区夏季降水产生显著影响,Ni?o3.4 指数可以作为次年中国东北地区夏季降水的预测因子.
选用常规气象观测资料、探空站气象观测资料、卫星云图反演沙尘产品、NCEP/NCAR再分析资料对 2021 年 3 月27-28 日中国北方一次典型强沙尘天气进行分析.结果表明:干燥(平均降水量小于10 mm、降水量负距平的地区超过80%)、高热(气温较常年偏高超过6℃)为此次沙尘天气的气候条件.同时,受蒙古气旋影响,中低空、地面大风和强烈的垂直上升运动为此次沙尘天气提供了动力条件.大气温度直减率、抬升指数、K指数、对流有效位能指数、IConve(700-850)指数、IConve(700—sfc)指数、PW指数表明,沙源地大气层结存在不稳定状态,为沙尘天气发生提供了热力条件.24 h后向轨迹模型表明,沈阳上空500 m高度的沙尘来自内蒙古地表抬升至1000 m高空后远距离输送,而 1000～2500 m高度的沙尘源自蒙古国 4000～5000 m高空,随气流下沉远距离输送至目标区域.
选用1981-2020 年中国东北地区199 站气象资料,将1981-2010 年与1991-2020 年作为气候态的气温、降水、大气环流场及海温场进行比较,并分析气候平均值的改变对气候评价、气候变化和气候预测的影响.结果表明:新、旧气候态下,东北地区的气温、降水、大气环流及海温场均有明显变化,且环流和海温场的变化与气温和降水变化相对应.新气候态下,东北地区的春季、秋季、冬季降水量增加,夏季降水量减少,平均气温均升高.冬季高纬阻塞高压和西太平洋副热带高压强度增强,东亚大槽强度减弱,与冬季气温升高相对应;夏季鄂霍次克海阻塞高压和西太平洋副热带高压减弱,与东北夏季降水减少相一致.全球海温总体增暖,而南半球中高纬地区变冷,夏季海温差值在赤道太平洋地区表现为西正东负的分布特征,与夏季东北气温升高相一致.
