云南区域性暴雨过程时空变化特征

针对中国气象局发布的《区域性重要过程(暴雨)监测和评价业务规定》确定了云南本地化区域性暴雨过程识别阈值,经灾害数据检验客观有效。进一步应用气候趋势分析、M-K检验、小波分析、EOF分析及REOF分析等方法研究了1961—2022年云南区域性暴雨过程的时空分布特征。结果表明:云南历次区域性暴雨过程综合强度波动较大,年平均综合强度呈减小趋势,但不显著,月平均综合强度呈波动分布,峰值出现在1月,雨季各旬平均综合强度变化较小。云南区域性暴雨过程年均4次,1977年以后呈减少趋势,但不显著,没有明显突变点,2008—2022年过程频次有6~8 a周期,但不稳定,云南区域性暴雨过程夏季最多,冬季最少,6月中旬至8月下旬过程数约占全年近7成。云南区域性暴雨过程呈南多北少分布,北部边缘虽然频次较少,但过程降水强度较大。云南区域性暴雨过程呈现全省大部一致型、东北-西南反向型及南北反向型等5个主要模态,进一步可分为5个典型区,即南区、西区、中东部区、北区及西北区,过去62 a,南区、西区和中东部区波动较大,变化趋势不明显,北区平稳少变,西北区前期以偏少为主,后期增加明显。

斯里兰卡极端降水时空演变特征及趋势预测

斯里兰卡是中国“一带一路”倡议的重要节点国家,极端降水对当地社会经济威胁较大,极易引发灾害。选取当地10个气象站2008—2020年的逐日降水资料,基于RClimDex模型计算斯里兰卡6个极端降水指标,利用经验正交函数分析、Morlet小波分析和重标极差法(R/S分析)等,分析极端降水事件的时空演变特征。结果表明:极端降水指数在时间分布上,除持续干旱指数(CDD)呈减少趋势外,其他各指数均为上升趋势;极端降水指数空间差异表现为北部沿海平原地带的极端降水强度高于南部沿海平原,拉特纳普勒市以西地区为斯里兰卡降水事件的多发区;极端降水指数周期性明显,绝大多数极端降水指数以6~7 a为第一主周期。R/S分析表明当地极端降水事件未来持续可能性较大。研究成果可为斯里兰卡极端气候预测、生态环境保护、防灾减灾工作等提供决策支持。

云南雨日特征及其影响因子分析

为研究云南雨日特征,基于1960~2019年气象站点观测的逐日降水资料、NCEP/NCAR再分析资料和NOAA的重构海温资料,分析了云南降水日数的时空变化特征及其与海温的联系。结果表明,云南降水主要集中在5~10月,以6~8月最多,并以5 d及以上的长持续降水为主。但是,近年来,云南雨日数有明显下降趋势,EOF分析第一模态表明,云南5~10月雨日变化主要为全区一致型。通过线性相关分析发现,同期北印度洋海温与云南雨日有显著负相关关系,将10°~25°N,55°~65°E区域平均海温定义为北印度洋海温指数,通过海温指数与同期大气环流的回归分析,结果表明:当印度洋海温偏高时,200 hPa西风急流增强,云南地区附近高空有风场辐合,500 hPa低纬度地区位势高度增加,700 hPa低空存在较明显的水汽通量辐散和下沉运动,由印度洋和南海向云南输送的水汽减少,不利于降水发生。通过对比海温低值和高值年的雨日数可以发现,印度洋海温高值年,云南5~10月雨日数均为负距平,反之亦然。研究成果可为金沙江中下游水资源调度和水库群联合调度提供参考。

青海省多年地表感热通量的时空变化特征

选取青海省35个气象站观测数据,基于CHEN-WENG感热系数方案,计算了1980—2017年青海省地表感热通量。利用小波分析、Mann-Kendall突变检验和经验正交函数法(Empirical Orthogonal Function,EOF),对感热通量的时空变化特征及其影响因子进行分析。结果表明:(1)1980年以来,青海省全年和各季节感热通量总体上均呈上升趋势,并具有28 a的主周期和约18 a的副周期,冬季的周期变化较为复杂;(2)全年和各季节感热通量与地气温差存在显著相关性,2004—2017年受地气温差增大的影响上升;(3)全年、春季和秋季感热通量与风速存在显著相关性,1980—2004年受风速减小的影响感热通量下降;(4)夏季降水与感热通量呈显著负相关;(5)从空间上看,全年以及春季感热通量呈现出显著的东西分异,秋、冬季表现出一定程度的南北分异。

基于EOF的1951-2020年东亚季风区降水特征及其对夏季风不同配置的响应研究

东亚季风区夏季降水受季风影响显著,不同季风配置通过影响区域水汽输送,在东亚季风区形成不同的降水格局,降水格局的变化容易引起旱涝灾害的发生。本文基于经验正交函数(EOF)分析,利用全球降水气候中心(GPCC)降水资料、美国国家环境预测中心/大气研究中心(NCEP/NCAR)再分析资料和不同夏季风指数,分析了1951-2020年东亚季风区夏季降水格局,进一步结合相关分析、水汽通量分析等,研究了4种夏季风强弱不同配置对东亚季风区夏季降水的影响。结果表明:(1)1951-2020年东亚季风区降水经历了先减少后增加的变化。EOF分析较好地展现了东亚季风区夏季降水的时空分布,东亚季风区夏季降水主要表现为南北向“-、+、-”的三极型分布与南北方降水反相变化的偶极型特征;东亚季风区夏季降水异常主要发生在三极型降水结构的相位转换上,其次是偶极型的相位转换;(2)东亚季风区夏季降水异常是东亚季风、南亚季风、西风环流以及西太平洋季风等系统共同作用的结果。导致东亚季风区降水异常增加(减少)的季风配置主要为配置1:西太平洋季风强,东亚季风和西风弱(配置2:西风强,东亚季风和南亚季风弱);(3)配置1时,西太副高偏南偏西,中高纬形成西风槽,季风区南方季风较强,容易通过切变线以及抬升作用在季风区中部形成降水,导致异常降水增加,配置2时,西风强劲,南方水汽动力不足,无法深入大陆,造成异常降水减少。本文研究结果为气候变化背景下,探究东亚季风区异常降水机理提供理论基础,也为应对区域极端降水事件以及旱涝灾害防治工作提供重要的科学参考依据。

具有正弦波纹的平行板微通道中Jeffrey流体周期电渗流动

研究了具有正弦波纹的平行板微通道中Jeffrey流体周期电渗流动(AC EOF).通过摄动展开法,求解了动量方程,得到了平行板微管道中Jeffrey流体AC EOF速度的近似解析解及其体积流率.在此基础上,研究了相关无量纲参数,如振荡Reynolds数Re_(Ω)、压力梯度G、Deborah数De、滞后时间λ2ω、电动宽度K、正弦波纹的小振幅δ、相位差θ及其波数λ对平均速度u_(m)(t)与平均速度的振幅|U_(m)|的影响.结果表明:Newton、Maxwell和Jeffrey流体之间的速度振幅差异是明显的;Jeffrey流体速度分布受壁面波纹的影响显著,并呈现出明显的波动现象,同时,速度分布还受上下板波纹相位差θ的影响;随着Re_(Ω)的增大,AC EOF速度和u_(m)(t)呈现出快速振荡且振幅逐渐减小的趋势;De数类似于Re_(Ω),使得在外加电场作用下,AC EOF速度更容易产生振荡;同时,AC EOF速度和|Um|随着λ2ω的增大而减小;对于给定的Re_(Ω),相位滞后χ(代表电场和平均速度之间的相位差)随着θ的变化呈明显增大或减小趋势;而χ随着G,λ2ω和θ的增大而减小,但是在较大的λ下(如λ>3.4),χ几乎没有显著变化.

基于EOF的江苏省多年降雨和气温时空分布特征

基于1950—2022年江苏省降雨和气温数据,采用EOF方法分析地区降雨和气温的时空分布特征。结果表明,73年间江苏年均降雨呈整体一致性,高值区位于宁镇扬中部及常州、无锡等地区;地区气温变化趋势整体一致性较高,江南和江北地区的差异逐渐缩小;地区降雨及气温变化与城市化关系密切。江苏省整体降雨量近73年呈减少趋势,气温呈逐年上升趋势,城市化改变下垫面、产生热岛效应影响降雨及气温变化。该研究为地区降雨和气温预测提供借鉴。

西南雨季降水变化与海温的关系

为了研究中国西南雨季降水变化和海温的关系,利用西南地区1960~2022年81站共63年的逐日气象观测降水量资料、同期英国哈德莱中心月平均海表温度(SST)资料(格点分辨率为1˚ × 1˚)、欧洲气象资料中心(ERA-interim)的月平均降水再分析资料(格点分辨率为0.25˚ × 0.25˚)。通过相关分析、经验正交函数分解(EOF)和奇异值分解(SVD)等方法,对西南雨季降水变化与全球SST之间的关系进行了研究分析,结果表明:1) 西南地区63年的雨季降水空间分布不均,呈现东多西少,南多北少的态势,同时,其与前冬、春季澳大利亚东北部太平洋,夏、秋季北印度洋和同期5⁓10月份的北印度洋海温呈现显著负相关关系,即关键区海温异常偏暖(冷),西南雨季降水偏少(多)。2) EOF分析表明:在第1模态下,前一年冬季和当年春季关键区海域海温的分布形式多呈现出西高东低的形式,包括了东太平洋冷舌和西太平洋暖池形态,并且在千禧年之前大部分都是海温多为偏冷状态;而千禧年之后关键区海温由偏冷转为偏暖状态。而当年夏秋季和雨季同期关键区海域海温呈现出全区一致偏暖状态,并且在90年代之后海温从偏冷转变为偏暖状态,其第2空间模态为印度洋正偶极子分布形式。3) SVD分解表明:关键区海温与川西高原地区雨季降水存在正相关关系,而与川东、黔南和云南呈现出一个显著的负相关性,不同季节的海温关键区影响的降水大值区域可能略有不同,但总体来说,当关键区海温异常偏高(低),川西高原的雨季降水异常偏多(少),而其余大部分地区降水异常偏少(多);其分解结果与相关系数的分析结果基本一致并且近年来西南地区的雨季降水呈现出逐年减少的态势。In order to study the relationship between precipitation change and sea surface temperature in the rainy season in southwest China, the daily meteorological observations of 81 stations in Southwest China from 1960 to 2022 for a total of 63 years were used measured precipitation data, the monthly mean sea surface temperature (SST) data of the Hadley Center in the United Kingdom (grid resolution of 1˚ × 1˚) and the monthly mean precipitation reanalysis data of the European Meteorological Data Center (ERA-interim) (grid resolution of 0.25˚ × 0.25˚). The relationship between precipitation change in the rainy season in southwest China and global SST was analyzed by correlation analysis, empirical orthogonal function decomposition (EOF) and singular value decomposition (SVD). The results shows: 1) The spatial distribution of rainy season precipitation in southwest China in 63 years was uneven, showing a trend of more precipitation in the east and less in the west, and more in the south and less in the north, and at the same time, it was significantly negatively correlated with the sea surface temperature in the Pacific Ocean in northeast Australia in the early winter and spring, the northern Indian Ocean in summer and autumn, and the northern Indian Ocean in May and October in the same period, that is, the SST in the key areas was abnormally warm (cold), and the precipitation in the southwest rainy season was less (more). 2) EOF analysis shows that in the first mode, the distribution of sea surface temperature in the key areas in the winter and spring of the previous year mostly shows the form of high in the west and low in the east, including the cold tongue of the eastern Pacific and the warm pool of the western Pacific, and most of the SST is in a cold state before the millennium. After the turn of the millennium, the sea surface temperature in key areas changed from cold to warm. However, the SST in the key areas of the key area in the same period of summer, autumn and rainy season showed a uniform warming state in the whole region, and after the 90s, the SST changed from cold to warm, and the second spatial mode was the normal dipole distribution of the Indian Ocean. 3) SVD decomposition showed that the sea surface temperature in the key area and the rainy season in the western Sichuan Plateau. There is a positive correlation with precipitation, and there is a significant negative correlation with eastern Sichuan, southern Guizhou and Yunnan, and the influence of key areas of SST in different seasons is positive. The precipitation area may be slightly different, but in general, when the sea surface temperature in the key area is abnormally high (low), the rainy season precipitation in the western Sichuan Plateau is abnormally biased more (less), while most of the rest of the precipitation is abnormally low (more). The decomposition results are basically consistent with the analysis results of the correlation coefficient and in recent years. The rainy season precipitation in southwest China shows a decreasing trend year by year.

1961—2020年河南省夏季极端降水异常分布特征及其与大气环流和海温的关系

利用河南省125个气象台站逐日降水量资料和ERA5再分析资料以及HadISST海表温度资料,分析了1961—2020年河南省夏季极端降水的气候特征和主模态,及其与大气环流和海温的关系。结果表明:河南省极端降水的气候态频次和强度均呈由西北向东南递增的分布,东南部地区极端降水最多,东部和北部地区次之,西部山脉地区最少。标准差分布与气候态类似,降水较多的区域也具有较大的年际差异。河南省夏季极端降水年际变化显著,但线性趋势较弱。河南省夏季极端降水EOF第1模态呈全省一致偏多的空间分布特征,欧亚型遥相关模态有利于高纬度冷空气南下,副热带高压西伸偏北有利于充沛的水汽向河南地区输送,同期海温场呈现出显著的黑潮延伸体正异常海温、La Niña模态、南印度洋海温偶极子负位相和北大西洋海温三极子正位相模态,反之亦然;第2模态为河南省北部地区偏多和南部地区偏少的反位相分布特征,乌拉尔山地区的阻塞高压和东西伯利亚地区的长波槽有利于高纬冷空气南下,副热带高压西伸偏北有利于水汽向河南地区输送,同期海温场呈现出典型的La Niña模态,但降水的极端性越强,海温异常信号也越复杂,反之亦然。

西南雨季降水时空变化特征

为研究西南雨季降水的时空变化特征,利用1960~2022年西南地区81个气象站点的逐日气象降水量观测资料,通过EOF分解、Morlet小波分析和EEMD分析等方法,对西南地区雨季降水量的多尺度变化特征进行了详细研究。结果表明:1) 西南地区63年来雨季降水量空间分布不均匀,大体有由东向西逐渐递减,以及由南向北逐渐递减的变化趋势。川西高原为降水量低值区,雅安、峨眉以及云南南部为降水量高值区。西南地区趋势系数正值区和负值区交错分布,正值区主要在川西高原以及川东和贵州,负值区在云南地区。2) EOF分析表明:西南地区雨季降水量第1模态为全区一致型,大值中心位于云南地区以及四川中南部。西南地区雨季降水量第2模态显示为北负南正型,正值大值中心位于云南地区,负值大值区位于川西和川东。西南地区雨季降水量第3模态显示为东北到西南正负交错的分布类型。西南地区雨季降水量第4模态为西负东正的分布类型。西南地区雨季降水量第5模态显示为东北到西南呈正负交错的分布类型。3) 小波分析表明西南地区雨季降水量主要有3~4年、7~8年、10~14年、15~23年的变化周期;EEMD分解表明西南地区雨季降水量主要有2.66年、5.33年、10年、21.3年的变化周期。由此可知,西南地区主要存在4年、8年和20年左右的周期。In order to study the spatio-temporal variation of rainy season precipitation in Southwest China, the multi-scale variation of rainy season precipitation in Southwest China was studied in detail by means of EOF decomposition, Morlet wavelet analysis and EEMD analysis, based on the daily meteorological precipitation observation data of 81 meteorological stations in Southwest China during 1960~2022. The results show that: 1) The spatial distribution of rainy season precipitation in Southwest China in the past 63 years is uneven, with a gradual decline from east to west and from south to north. The West Sichuan Plateau has a low precipitation value, while Ya’an, E’mei and southern Yunnan have a high precipitation value. The positive and negative regions of the trend coefficient in Southwest China are interleaved, the positive regions are mainly in the west Sichuan Plateau, the east Sichuan and Guizhou, and the negative regions are in Yunnan. 2) The EOF analysis shows that the first mode of precipitation in the rainy season in southwest China is the uniform type in the whole region, and the large value center is located in Yunnan and central and southern Sichuan. The second mode of rainy season precipitation in Southwest China shows that the north is negative and the south is positive. The positive value center is located in Yunnan, and the negative value area is located in west and east Sichuan. The third mode of precipitation in the rainy season in Southwest China shows the distribution type of positive and negative interleaving from northeast to southwest. The fourth mode of precipitation in the rainy season in Southwest China is the distribution type of west negative and east positive. The fifth mode of precipitation of rainy season in Southwest China shows the distribution type of positive and negative from northeast to southwest. 3) Wavelet analysis shows that the rainy season precipitation in Southwest China mainly has a change cycle of 3~4 years, 7~8 years, 10-14 years and 15~23 years. The EEMD decomposition shows that the rainy season precipitation in Southwest China mainly has a change cycle of 2.66 years, 5.33 years, 10 years and 21.3 years. It can be seen that there are cycles of about 4 years, 8 years and 20 years in Southwest China.

西南地区夏季降水变化与南亚高压的关系

为进一步研究西南地区夏季降水变化与南亚高压的关系,利用1961~2022年6~8月西南地区76个气象观测站的逐日气象观测降水量资料、同期NCEP/NCAR的月平均再分析资料(包括高度场、风场、地面气压场、相对湿度场、比湿场),网格距2.5˚ × 2.5˚,通过合成分析、EOF分解、SVD奇异值分解、南亚高压特征量等方法,详细探讨了西南地区夏季降水变化特征与南亚高压的关系其主要结论如下:(1) 西南地区1961~2022年夏季降水呈负趋势,每10年夏季平均降水量减少4.377 mm,降水量空间分布不均匀,大致为自西北向东南呈增加趋势。(2) 根据EOF分析,前三个模态累计方差贡献率达到了51.6%,能够较好地反映出夏季降水的空间分布类型。第一模态表明西南地区整个地区全年都呈现出多雨(少雨)的趋势。第二模态呈现出正距平区和负距平区交错分布的情况。第三模态呈现出夏季降水从北向南呈“正–负–正”分布类型的情况。(3) 1961~2022年南亚高压特征量不仅存在明显的年际变化,而且也存在明显的年代际变化,除脊线位置外。从1961~2022年,南亚高压的强度在逐渐增强,面积在逐渐增大,东脊点位置逐渐偏西,西脊点位置逐渐偏东,脊线位置逐渐偏南。(4) 从1961~2022年夏季南亚高压各特征参数与西南地区同期降水的点相关分布中可以看出,南亚高压脊线位置、面积指数、强度指数与西南地区同期降水关系最密切。从西南地区夏季降水与同期南亚高压的SVD分解表明:第一模态空间分布型反映了南亚高压偏弱(强)年与西南地区偏旱(涝)年对应关系一般。第二模态空间分布型反映了南亚高压偏弱(强)年与西南地区偏旱(涝)年对应关系较好。

Effect of boundary slip on electroosmotic flow in a curved rectangular microchannel

The aim of this study is to numerically investigate the impact of boundary slip on electroosmotic flow(EOF) in curved rectangular microchannels. Navier slip boundary conditions were employed at the curved microchannel walls. The electric potential distribution was governed by the Poisson–Boltzmann equation, whereas the velocity distribution was determined by the Navier–Stokes equation. The finite-difference method was employed to solve these two equations. The detailed discussion focuses on the impact of the curvature ratio, electrokinetic width, aspect ratio and slip length on the velocity. The results indicate that the present problem is strongly dependent on these parameters. The results demonstrate that by varying the dimensionless slip length from 0.001 to 0.01 while maintaining a curvature ratio of 0.5 there is a twofold increase in the maximum velocity. Moreover, this increase becomes more pronounced at higher curvature ratios. In addition, the velocity difference between the inner and outer radial regions increases with increasing slip length. Therefore, the incorporation of the slip boundary condition results in an augmented velocity and a more non-uniform velocity distribution. The findings presented here offer valuable insights into the design and optimization of EOF performance in curved hydrophobic microchannels featuring rectangular cross-sections.

Spatiotemporal variations of parameters of internal solitary waves in the northern South China Sea

The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the dynamic parameters of the ISWs in the northern South China Sea(SCS)were studied based on the reanalysis of long-term temperature and salinity datasets.The results for spectrum analysis show that there are definite geographical differences for the periodic variation of the parameters:in shallow water,all parameters vary with a wave period of one year,while in deep water wave components of the parameters at other frequencies exist.Using wavelet analysis,the wavelet power spectral densities in deep water exhibited an inter-annual variation pattern.For example,the wave component of the dispersion coefficient with a wave period of about half a year reached its power peak once every two years.Based on previous work,this inter-annual variation pattern was deduced to be caused by dynamic processes.In further work on the regulatory mechanisms,empirical orthogonal function(EOF)decomposition was performed.It was found that the modes of the dispersion coefficient have different geographical distributions,explaining the reason why the wave components in different frequencies appeared in different locations.The numerical simulation results confirm that the variations in the parameters of the ISWs derived from the eKdV equation could affect the waveforms significantly because of changes in the polarity of the ISWs.Therefore,the periodic variations of the dynamic parameters are related to the geographical location because of dynamic processes operating.

太行山地区1973—2022年暴雨时空演变特征分析

基于太行山地区13个气象站点50 a的逐日降水数据,使用Mann-Kendall突变检验、信噪比检验、Sen’s斜率估计、小波分析法、正交函数分解等方法,对太行山地区的暴雨时空演变规律进行了研究。结果表明:(1)太行山地区年暴雨量波动幅度较大,根据拟合斜率和Sen’s斜率估计法的结果可知,太行山地区的暴雨量将会呈增多趋势;(2)Mann-Kendall突变检验和信噪比检验结果表明,2006年太行山暴雨量发生了由少变多的突变;(3)Morlet小波分析结果表明,在2017—2020间研究区暴雨量存在2~3 a的周期尺度变换;(4)该地区暴雨量经验正交函数(EOF)展开的前两个模态显示,暴雨量呈现全区一致变化以及西南—东北向的空间变化特征,这两个模态情境下的暴雨状况受到环流因子和地形的共同影响。

Spatio-temporal variability of surface chlorophyll a in the Yellow Sea and the East China Sea based on reconstructions of satellite data of 2001-2020

Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental monitoring.The spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East China Sea(ECS)in 2001-2020 were investigated by reconstructing the MODIS Level 3 products with the data interpolation empirical orthogonal function(DINEOF)method.The reconstructed results by interpolating the combined MODIS daily+8-day datasets were found better than those merely by interpolating daily or 8-day data.Chl-a concentration in the YS and the ECS reached its maximum in spring,with blooms occurring,decreased in summer and autumn,and increased in late autumn and early winter.By performing empirical orthogonal function(EOF)decomposition of the reconstructed data fields and correlation analysis with several potential environmental factors,we found that the sea surface temperature(SST)plays a significant role in the seasonal variation of Chl a,especially during spring and summer.The increase of SST in spring and the upper-layer nutrients mixed up during the last winter might favor the occurrence of spring blooms.The high sea surface temperature(SST)throughout the summer would strengthen the vertical stratification and prevent nutrients supply from deep water,resulting in low surface Chl-a concentrations.The sea surface Chl-a concentration in the YS was found decreased significantly from 2012 to 2020,which was possibly related to the Pacific Decadal Oscillation(PDO).

中国东部及西北太平洋地区边界层高度变化特征

利用19792019年ERA5逐月大气边界层高度(ABLH,Atmospheric boundary layer height)资料,采用气候态分析、距平分析、极值分析、经验正交(EOF)分析及小波分析等方法,研究了中国东部及西北太平洋区域大气边界层高度的变化特征。结果表明:(1)太阳辐射对边界层高度有重要影响,边界层高度的季节变化存在显著的海陆差异,大陆(海洋)上,夏季时边界层高度最高(低),而在冬季时最低(高)。(2)边界层高度的海陆差异随季节改变,春秋季海陆差异较小,冬夏季时最明显。(3)大陆上边界层高度极大值出现在夏半年的月份,极小值出现在冬半年的月份;海洋上的相反。(4)41年边界层高度场的EOF分析得到两个模态,第一模态表现为南北经向和海陆差异,第二模态为自西北至东南的“+-+-”相位分布,第一模态的时间系数以3~7 a的周期最显著,第二模态同时存在4~7 a和9~11 a的振荡周期。

2021年1月与2024年1月我国东部极端寒潮发生原因及机制探讨

伴随全球变暖和有卫星观测记录以来北极秋季海冰密集度的下降,近几年,东亚地区极端降温事件频繁发生。基于北极涛动指数、北极海冰密集度及温度、位势高度和风场等大气环流数据,研究2021年1月和2024年1月我国极端寒潮的时空变化特征及其原因机制。2021年1月1日至9日,2024年1月17日至24日,我国东部地区(115°00′~120°00′E,20°00′~37°30′N)均出现极端寒潮,最低温异常分别达到-9.028℃和-7.574℃,降温中心分别集中在蒙古高原南部和秦岭北部。2021年1月我国东部地区平均气温降至-3.8℃,达到21世纪以来的次低温纪录。本文基于美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)的再分析资料,得出西风带的异常减弱致使西风带对高纬度冷空气的阻拦作用减弱。2021年、2024年寒潮爆发时位于西伯利亚、太平洋西北部的高压中心和华北地区低压中心均出现显著增强,导致位于西伯利亚的冷空气的南移,致使我国东部地区出现寒潮现象。2024年1月极端寒潮现象显示于气温经验正交分解(Empirical Orthogonal Function,EOF)的第一模态(29.33%),而EOF分解的第二模态(14.92%)显示2021年出现降温现象。回归分析表明北极涛动指数和北极海冰的下降与1月我国东部地区气温之间存在较强的关系,北极涛动负相位及北极海冰的减少也是促使北极冷空气南下的重要因素。异常强劲的北风、减弱的西风急流及对应高压中心的移动共同作用导致了极地寒流入侵我国大陆地区,是造成2021年1月和2024年1月我国大陆地区极端天气事件的主要原因之一。

金沙江流域中上游近10年降水时空分布特征分析

对金沙江流域中上游地区进行近10年降水量EOF分析中,年降水量前3个载荷量的累计方差贡献已经达到83.47%,且第一模态的方差贡献率就已经达到68.86%;干湿季降水量前3个载荷量的累计方差贡献已经达到95.77%,且第一模态的方差贡献率就已经高达92.2%,四季降水量前3个模态的方差贡献率其载荷量的累计方差贡献已经达到93.77%,第一个模态的方差贡献率高达89.91%。所以近10年,年、干湿季及四季降水的第一模态已经能较好的表征该变量场的空间分布及时间分布特征。年、干湿季、四季降水量第一模态空间分布场均为全区均为正值,说明降水量变化一致性为金沙江流域中上游地区年、干湿季、四季降水量变化的主要特征,其中迪庆州南部,丽江市东部地区一带特征值最大,在最具代表性的变化中这些地区降水量变化最敏感,即降水量增加最多或减小最少。金沙江流域中上游地区年、干湿季、四季降水量四季降水量具有一致性,即整个流域中上游段降水量变化是一致的。

多种地表温度资料在青藏高原的适用性及其主要影响因子研究

本文使用2001~2016年站点观测资料评估三套地表温度再分析产品(ERA5、JRA-55和MERRA-2)在青藏高原(简称“高原”)的适用性,并分析三个气象因子(海拔、NDVI和积雪覆盖率)对高原地表温度的影响程度。基于偏差、均方根误差和相关系数指标综合比较各再分析资料对观测值的模拟情况,结果表明:MERRA-2在青藏高原适用性最好,与地表温度相关性最显著,而ERA5和JRA-55在高原适用性不佳,对地表温度偏差较大,相关性弱。通过EOF分析得到青藏高原全域地表温度呈现随年份上升的趋势,三种再分析资料对于地表温度的时空变化均与观测资料所得的结果有所差异,其中仅有MERRA-2在第二模态中青藏高原大部分区域(除西南部分区域外)的地表温度呈现随年份上升的趋势。在全年和冬季两个尺度上,海拔和积雪覆盖率因子对地表温度均为负影响,而NDVI因子对地表温度的影响随季节变化,冬季为负影响(总效果系数为−0.117),全年尺度下为正影响(0.134),三个再分析地表温度对三个因子的响应情况与观测地表温度相差不大,其中综合比较MERRA-2的响应效果与观测资料最为接近。This paper uses station observations from 2001~2016 to assess the applicability of three surface temperature reanalysis data (ERA5, JRA-55, and MERRA-2) on the Qinghai-Xizang Platea (referred to as the “Plateau”) and to analyze the influence of three meteorological factors (elevation, NDVI, and snow cover) on surface temperatures. Based on the deviation, root-mean-square error and correlation coefficient statistical indexes and the in-situ observation data, the accuracy of the reanalysis data is comprehensively analyzed. Our findings indicate that MERRA-2 has the best applicability and shows the most significant correlation with the surface temperature, while ERA5 and JRA-55 have poor applicability on the plateau, large bias and weak correlation with the surface temperature. The EOF analysis shows that the surface temperature of the whole Plateau has been increasing over the years. However, the spatial and temporal variations of the surface temperature of three reanalysis data are different from those obtained from observations, with only MERRA-2 showing an increasing trend with years in most areas of the Plateau in the second mode (except for part of the southwestern part of the Plateau). At both the year-round and winter scales, the elevation and snow cover factors have a negative effect on surface temperature, while the effect of the NDVI factor on surface temperature varies seasonally. In winter, it has a negative effect in winter (total effect coefficient of −0.117), while it has a positive effect at the year-round scale (0.134). Furthermore, we found that the response of the three reanalyzed surface temperatures to the three factors is similar to that of the observed surface temperatures, with MERRA-2 showing the closest response effect to observations.

基于K-均值聚类法和EOF分析的贵港市台风暴雨特征分析

利用实况观测资料、中国气象局台风最佳路径数据集,采用K-均值聚类法,结合经验正交函数(EOF)分析方法,分析研究2010—2023年间影响贵港市的65个台风造成的暴雨特征。结果表明:(1)有39个台风造成暴雨,每年平均有2.8次台风暴雨,7、8月是台风暴雨的集中期,港南区南部、覃塘区中西部及桂平北部山区台风暴雨多且高发。(2)根据K-均值聚类分析,将65个台风分为A、B、C类。A、B类台风源地为西北太平洋,C类台风多来自南海。A、B类台风以西北行为主,C类台风路径复杂且在北部湾活跃。B类台风强度最强,C类台风强度最弱。(3)根据贵港市三类台风暴雨第一特征向量空间分布情况分析,A、B类台风暴雨年降雨量表现为全市普遍偏多或者普遍偏少分布型式,C类台风暴雨的年降雨量变化趋势主要为桂平东北部、南部和平南西北部偏多,其余地区偏少,或者桂平东北部、南部和平南西北部偏少,其余地区偏多。