渤海湾西部沿岸地区气温特征的观测研究

在渤海湾西岸沿着垂直于海岸的方向从海边向内陆设置了4个自动气象观测站,以10 min为时间间隔观测地面1.5 m处气温.结合邻近气象台站的温度观测,分析渤海湾沿岸气温的时空变化特征.结果表明:秋、冬季气温升降迅速,夏季气温升降缓慢;冬季和夏季气温日较差小,秋季气温日较差大;日平均气温与日最低气温从海边向内陆逐渐降低,日最高气温和气温日较差从海边向内陆逐渐升高;夏季海陆风日的气温时空变化显著,海边地区的气温日变幅小,内陆地区的气温日变幅大,海陆温差随着离岸距离的增加而加大;秋、冬季海陆风日的海边和内陆之间的气温日变幅小,海边和内陆之间的海陆温差变幅基本相当;海陆风环流影响海岸地区气温的时间范围主要是海陆风日,空间范围从海边伸向内陆10 km左右,最大范围从海边伸向内陆50～60 km左右.     

渤海湾西部沿岸地区气温特征的观测研究

在渤海湾西岸沿着垂直于海岸的方向从海边向内陆设置了4个自动气象观测站,以10 min为时间间隔观测地面15 m处气温。基于这些野外观测数据和邻近气象台站的温度观测数据,对渤海湾沿岸地区气温的时空变化特征进行了分析。结果表明：秋、冬季气温升降迅速,夏季气温升降缓慢;冬季和夏季气温日较差小,秋季气温日较差大;日平均气温与日最低气温从海边向内陆逐渐降低,日最高气温和气温日较差从海边向内陆逐渐升高;夏季海陆风日的气温时空变化显著,海边地区的气温日变化幅度小,内陆地区的气温日变化幅度大,海陆温差随着离岸距离的增加不断加大;秋、冬季海陆风日的海边和内陆之间的气温日变化幅度小,海边和内陆之间的海陆温差变化幅度基本相当;海陆风环流影响海岸地区气温的时间范围主要是海陆风日,空间范围初步确认是从海边伸向内陆10 km左右,估计最大范围可能是从海边伸向内陆50~60 km左右。     

基于逐时降水站点资料空间插值方法对比研究

自动站降水资料的格点化是目前气象业务及研究亟需解决的问题,空间插值方法是数据格点化最直接有效的方法。本文选取3个降水个例分别作为大范围强降水、大范围弱降水和局地强对流3种类型降水的典型代表,采用8种常用的插值方法,设计3组试验,通过交叉检验对比8种插值方法降水的插值效果及站点密度对插值效果的影响。结果表明:高密度站点试验表明(站点平均距离约为9.0 km),8种插值方法降水的插值效果依次为CR、IW、NN、LP、KR、TL、MQ和SP,8种插值方法之间的差异小于样本间的差异,即插值效果主要取决于站点的分布而不是插值的方法。低密度站点试验表明(站点平均距离约为30.0 km),大范围降水个例(包括大范围强降水和大范围弱降水)插值的均方根误差(Root Mean Square Error,RMSE)显著增大,不同插值方法之间的差异也增大;而局地强对流个例中,插值后均方根误差增大幅度较小,不同插值方法之间的差异较小。利用CR、IW和NN等3种插值方法开展站点密度敏感性试验,试验表明站点密度提高有利于减小降水插值误差,但站点平均距离达13.0 km甚至更密时,降水误差减小的趋势变缓。     

中国地面气温和降水网格化数据精度比较

采用2013年国家气象信息中心逐日的839个中国基准、基本气象站,2419个国家级地面气象站,3万个逐小时中国地面站气温和降水数据,利用ANUSPLIN、SHERPAD和OI插值方法进行网格化,比较了日尺度上不同方法插值不同站网密度站点数据的精度。结果表明:台站点数量多的插值结果,对降水或气温描述的准确性高,相关性随站点密度增加而增大,均方根误差随站点密度增加而减少。不同方法得出的相关性、均方根误差随季节变化明显,且差异较大,并具有月尺度变化特征。夏季站点数量相同的不同插值结果,降水场的相关系数、RMSE等评分指标均优于年平均,气温的相关系数则低于年平均。降水场的评估指标时间序列不确定性更大,波动范围大于气温。国家气象信息中心制作的基于气候背景场OI插值的2419站降水场和考虑高程ANUSPLIN插值的2419站气温的综合评价较好。     

利用区域自动站资料分析核桃花期冻害气象条件及成因

利用太行山南部核桃种植区100个区域自动站和6个国家气象站的常规观测资料、NCEP/NCAR再分析数据和临城"绿岭"核桃种植基地核桃发育期观测及冻害资料,对核桃遭遇花期冻害时区域自动站和国家站之间的气温指标相关性及种植区冻害风险气候特征进行研究,结果表明:1区域自动站和国家气象站的气温相关性显著,其中,90%的站点相关系数高于0.9;2同一时刻,区域站气温与海拔高度呈负相关,当核桃花期国家气象站气温分别低于3.6℃、2℃、0.4℃、-0.4℃时,可能受冻的核桃种植区海拔高度分别为1000m以上、600m以上、200m和0m以上。3太行山南部山区核桃花期年平均冻害风险日数为3.4天。4核桃花期冻害和极涡及乌拉尔山高压脊异常偏强有关。     

丽水市白云山森林公园山地立体气候资源分析

根据丽水市白云山森林公园5个不同海拔的气象自动站逐时气温、降水、风速资料,研究白云山气候资源随时间、海拔的变化规律,从而探究丽水白云山立体气候资源的分布特征。结果显示,丽水市白云山森林公园气温日(年)变化,日(年)最高出现在14时(7月),最低出现在06时(1月),随着海拔的增加,气温日(年)较差缩小。气温随海拔的递减率约0. 6℃/100 m,午后(夏季)递减率较高,夜间(冬季)较低。降水日(年)变化特征,午后至上半夜(夏、秋季)降水较多,17时(6月)出现峰值,20时(8—9月)次之。降水随海拔变化先增加后减少,最大降水量高度约为海拔700 m。风速日(年)变化特征,白云山顶风速最大,林场风速最小,高海拔风速夜间(3—8月)较大,低海拔风速受下垫面影响波动变化。     

EC细网格对黑龙江省日极端气温预报的误差分析

本文采用2013、2014(1-10月)年的EC细网格2 m气温预报资料,通过双线性插值方法对黑龙江省83个站点进行插值,得出EC细网格模式对于日极端气温的预报误差并进行分析。结果表明,EC细网格模式本身对于最高气温的预报准确率高于最低气温,预报误差具有显著的季节性变化特征,但不同的起报时间对误差的影响并不明显。EC细网格模式的预报误差符合正态分布,具有可订正性。     

卫星云图资料在降水量客观分析中的应用试验

利用葵花8号卫星资料和地面降水观测资料,采用Spark GBT(Gradient Boosted Trees Regression)学习算法对中国西部地区进行定量降水估计(QPE),采用三维Barnes插值方法得到逐时降水格点场。试验结果表明,引入卫星资料估计降水,可以有效地改进站点密度小的区域的降水格点场的客观分析精度。     

CLDAS气温数据在中国区域的适用性评估

以中国48 708个地面气象自动站逐小时气温数据为基础,采用平均偏差(Bias)、相关系数、均方根误差(RMSE)、平均绝对误差(MAE)等评估指标,对比分析2017年10月、2018年1月、4月、7月CLDAS-V2.0气温(分辨率为0.062 5°),探讨中国8个分区春、夏、秋、冬4个季节CLDAS与站点气温的相关性及偏差分布特征。结果表明:1) CLDAS气温较好地反映了中国气温的年际变化,非独立性检验、独立性检验与站点气温的平均相关系数分别为0.995、0.991,东北地区相关性最高,西南地区相关性最低。2) CLDAS与站点气温的Bias为-0.011℃,非独立性检验的RMSE、MAE分别为1.275、1.645℃,独立性检验的RMSE、MAE分别为0.867、1.089℃,总体上CLDAS气温误差小,可信度较高。3)春、秋季的偏差小于夏、冬季;东北、华北、江淮、华南地区的偏差小于西北、西南地区; 84.6%站的冷偏差或暖偏差在1℃内,冷暖偏差空间分布均匀。4) CLDAS的最高气温存在冷偏差,最低气温存在暖偏差,夏季最高气温的最大误差为-0.59℃。5) CLDAS平均偏差的日变化为-0.23～0.07℃,白天呈冷偏差,夜间呈暖偏差,夏季平均偏差的日变化较显著,偏差的日较差为0.26℃;全国8个分区夏季平均偏差日变化最大为1.06℃,秋、冬、春季变幅相似,西南地区平均偏差日变化最大而江淮地区最小。     

基于自动站观测资料的深圳城市热岛研究

利用103个自动站气温观测资料,对深圳的城市热岛现象进行了研究.分析表明:1)高速的城市化进程造成自动站周边下垫面属性的变化,对气温造成了显著影响,这种影响即使是在仅仅10年内也表现得较为明显.2)由于深圳地处海滨,深圳的近地层气温分布是海陆作用叠加城市热岛效应形成的结果.按照传统定义的深圳城市热岛,其空间分布在不同季...     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

IMPACT OF ATMOSPHERIC LOW-FREQUENCY OSCILLATION ON THE IMMIGRATION OF NILAPARVATA LUGENS(STL) IN HUNAN AND JIANGXI PROVINCES

Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation(10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens(Stl), in Hunan and Jiangxi provinces. The results showed the following:(1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days.(2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 h Pa.(3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration.     

A COMPARATIVE ANALYSIS OF ATMOSPHERIC AND OCEANIC CONDITIONS BEFORE THE OCCURRENCE OF TWO TYPES OF EL NIO EVENTS

The atmospheric and oceanic conditions before the onset of EP El Ni?o and CP El Ni?o in nearly 30 years are compared and analyzed by using 850 hPa wind, 20℃ isotherm depth, sea surface temperature and the Wheeler and Hendon index. The results are as follows: In the western equatorial Pacific, the occurrence of the anomalously strong westerly winds of the EP El Ni?o is earlier than that of the CP El Ni?o. Its intensity is far stronger than that of the CP El Ni?o. Two months before the El Ni?o, the anomaly westerly winds of the EP El Ni?o have extended to the eastern Pacific region, while the westerly wind anomaly of the CP El Ni?o can only extend to the west of the dateline three months before the El Ni?o and later stay there. Unlike the EP El Ni?o, the CP El Ni?o is always associated with easterly wind anomaly in the eastern equatorial Pacific before its onset. The thermocline depth anomaly of the EP El Ni?o can significantly move eastward and deepen. In addition, we also find that the evolution of thermocline is ahead of the development of the sea surface temperature for the EP El Ni?o. The strong MJO activity of the EP El Ni?o in the western and central Pacific is earlier than that of the CP El Ni?o. Measured by the standard deviation of the zonal wind square, the intensity of MJO activity of the EP El Ni?o is significantly greater than that of the CP El Ni?o before the onset of El Ni?o.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on