中国南方冰雹气候特征的三维EOF分析

根据我国南方61个气象观测站1956～1995年月冰雹日数资料,应用三维EOF和小波统计分析方法,研究了冰雹的主要空间分布类型、季节变化特征、年际变化和年代际变化规律。结果表明:我国南方多冰雹带位于云贵至中南地区的湖南、江西,并向北、向东南逐步递减,最大值出现在贵州兴仁;我国南方冰雹季节变化显著,春季2～5月份为多冰雹季节,其中3月份出现最大值。冬半年(10月～次年5月)冰雹主要活动于南区(25～30 N、115 E 以西地区),夏半年(6～9月)冰雹主要活动于北区(30 N以北地区);近40年来冰雹总趋势是逐渐减少的,具有28年、14年、8～9年、5年和3.5～4年左右的周期震荡,并且10年以下周期具有50～60年代以3.5～4年周期为主,70年代以8～9年、3.5～4年周期为主,80年代以8～9年、5年、3.5～4年周期为主,90年代以8～9年、5年周期为主的年代际变化规律。     

华北降水年代际、年际变化特征与北半球大气环流的联系

利用美国NCEP再分析月平均资料及我国华北地区26个测站月降水资料,采用奇异值分解（SVD）方法,研究了华北降水的年代际和年际变化特征与北半球大气环流的联系.结果表明,首先对降水和大气变量资料作相应时间尺度的分离是非常必要的,否则所得到的SVD结果不能反映年代际变化特征而只能反映年际变化特征;华北降水年代际和年际变化对应的大气环流异常有明显的差异;对应发生在20世纪70年代后期华北降水的一次年代际跃变,环流场均存在明显的跃变,而且有一个从地面向对流层上层传播的过程.     

我国四季极端雨日数时空变化及其与海表温度异常的关系

利用1960—2004年我国586个气象站的逐日降水观测资料,对每个季节和每个站点,以雨日降水量升序排列的第90个百分位值定义极端日降水阈值,分析揭示了我国四季极端雨日数的时空变化特征、与海表温度异常的关系以及相联系的大气环流异常型。结果表明,我国长江流域极端雨日数在冬季和夏季呈显著增加趋势,华北地区极端雨日数在冬季显著增加、而在夏季显著减少,华南地区极端雨日数在春季显著增加,东北地区极端雨日数在冬季和春季显著增加,而西北地区极端雨日数在四季均显著增加。各季极端雨日数在线性趋势变化之上表现年际和年代际变化特征,并且其典型异常型明显不同,春、秋季表现为长江以南与以北地区反位相的"偶极型"变化,夏季表现为长江流域与华南、华北地区反位相的"三极型"变化,冬季表现为全国大部分地区同位相的"单极型"变化。我国季节极端雨日数与印度洋-太平洋海表温度异常的关系主要表现为与ENSO的关系,而ENSO影响我国极端降水异常是通过相应的大气环流异常型来实现的。     

冬季东亚副热带急流与温带急流的比较分析：大尺度特征和瞬变扰动活动

使用ERA40再分析的月资料和逐日资料,从大尺度特征和瞬变扰动活动两个角度对冬季东亚副热带急流(EASJ)和东亚温带急流(EAPJ)进行了比较分析.结果表明,使用月资料分析的EASJ与EAPJ在高层风场上没有清晰的地理分界区,而使用逐日资料计算得到的冬季逐日急流发生数则以高原北部上空所处的纬度带为分界岭,存在两个急流中心集中区,分别对应于EASJ和EAPJ区域.通过分析东亚上空天气尺度瞬变扰动活动(STEA)表明,与强盛的冬季EASJ相伴随的是较弱的南支STEA,而与较弱的EAPJ相伴随的北支STEA却十分活跃,显示出EAPJ是与瞬变活动相伴而存的急流.进一步的诊断分析揭示了冬季东亚温带急流的两种主要异常模态,一种是EAPJ区域反气旋性/气旋性异常环流型,另一种是局地西风的减弱/增加.与第1种异常模态相关的北半球大尺度环流异常主要集中在欧亚中高纬地区,其形成受中高纬大气环流以及东亚上游大气环流异常的共同影响.当EAPJ局地西风减弱/增加时.EASJ东段至西太平洋上空的西风急流呈现出与之相反的变化型,北半球大气环流异常表现为大气遥相关的欧亚(EU)型.冬季EAPJ的两种异常模态还与东亚上空STEA异常密切联系,其中在北支STEA区域出现的瞬变异常以波列的形式沿STEA北支轴线传播到达东亚沿海上空,然后东传入洋面上空,而对于靠近STEA南支轴线的异常扰动活动则只能存在于东亚东部及其沿海上空的200 hPa层上.     

北半球夏季位势高度场遥相关型的观测研究

本文利用点相关普查和经验正交函数展开分析了北半球夏季500hPa位势高度场的年际变化特征,指出在北半球夏季主要存在4种遥相关型,它们分别被称作亚洲/北美型、欧亚型、东北太平洋型和北美/西欧型.其中前两种遥相关型和东亚夏季大气环流年际异常有直接的联系.北半球夏季遥相关型的揭示对于研究东亚大气环流年际变异的机理及其预报有参考价值.     

PREDICTION OF FLOOD SEASON PRECIPITATION IN SOUTHWEST CHINA BASED ON IMPROVED PSO-PLS

In order to achieve the best predictive effect of the Partial Least Squares (PLS) regression model, Particle Swarm Optimization (PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares (PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season. Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function (MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also build up to improve the prediction results. Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model. The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation (PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.     

TYPICAL ANOMALOUS WIND STRESS PATTERNS IN TROPICAL PACIFIC ASSOCIATED WITH EL NINO/SOUTHERN OSCILLATION

Based on years of month-to-month observations of sea surface temperature anomaly (SSTA) and wind stress anomaly, typical wind stress patterns in the tropical Pacific associated with ENSO are Studied with the techniques of linear regression and EOF analysis. The anomalous field, which is linearly correlated with ENSO, is found to be varying at low frequencies on the temporal scale and to be in four typical patterns of distribution horizontally.Pattern 1 is of the easterly anomaly and wind stress divergence in the equatorial region east of the date line. Pattern 2 is of the westerly anomaly and wind sttess convergence in the equatorial region east of the date line. Pattern 3 is of the westerly anomaly and wind stress convergence south of the Equator but east of the data line, with the easterly anomaly west of it. Pattern 4 is of the weak easerly anomaly east 160°W and the westerly anomaly west of 160°W. Wind stress fields linearly independent of ENSO are of a high-frequency process with a typical pattern off the Equator that has a large horizontal amplitude. Using an ocean anomaly-forcing model with the regressed wind stress anomaly field that is associated with ENSO, principal signals of ENSO are reproduced. It indicates the fundamental nature of the typical wind field anomaly patterns revealed for the genesis of El Nino.     

Remote forcing of Indian Ocean warming on Northwest Pacific during El Niño decaying years: a FOAM model approach

This paper attempts to analyze in detail the remote influence of the Indian Ocean Basin warming on the Northwest Pacific (NWP) during the year of decaying El Niño. Observation data and the Fast Ocean-Atmosphere coupled Model 1.5 were used to investigate the triggering conditions under which the remote influence is formed between the positive sea surface temperature (SST) anomaly in the North Indian Ocean and the Anomalous Northwest Pacific anticyclone (ANWPA). Our research show that it is only when there is a contributory background wind field over the Indian Ocean, i.e., when the Indian Summer Monsoon (ISM) reaches its peak, that the warmer SST anomaly in the North Indian Ocean incites significant easterly wind anomalies in the lower atmosphere of the Indo-West tropical Pacific. This then produces the remote influence on the ANWPA. Therefore, the SST anomaly in the North Indian Ocean might interfere with the prediction of the East Asia Summer Monsoon in the year of decaying El Niño. Both the sustaining effect of local negative SST anomalies in the NWP, and the remote effect of positive SST anomalies in the North Indian Ocean on the ANWPA, should be considered in further research.     

华南前汛期降水异常与太平洋海表温度异常的关系

利用近50年华南地区站点逐日降水观测资料和全球大气、海洋分析资料,分析了华南前汛期降水异常的变化特征及其与太平洋海温异常的联系.结果表明,近50年来华南前汛期降水总体呈现减少趋势.影响华南前汛期降水异常的太平洋海温异常型是一个类似于ENSO的西太平洋暖池模态,即显著海温异常区域位于西太平洋暖池.西太平洋暖池区域(120°E-180°E,20°S-20°N)前期冬季海温异常同华南前汛期降水存在显著的负相关关系,是具有预报意义的海温关键区.该关键区海温异常影响华南前汛期降水的可能物理过程是:当前期冬季暖池异常偏暖时,菲律宾周围地区对流活动加强,导致Walker环流及东亚太平洋中低纬局地Hadley环流增强;该异常通过影响东亚－太平洋遥相关波列,使前汛期期间西太平洋副高加强西伸,脊线位置偏北,同时副热带西风急流减弱北退.随着Hadley环流上升支的增强,东亚副热带地区下沉运动也增强了,华南地区对流活动受到抑制.而且由于副高的增强,经过其北侧向华南地区的西南水汽输送辐合也减弱了,因此前汛期降水偏少.冷海温年的情形则相反,华南前汛期降水偏多.近50年来华南前汛期降水总体呈现趋势性减少正是由于前冬西太平洋暖池趋势性增暖所致.