Recent significant tornadoes in China

正1.Introduction Compared with the United States,whose annual number of tornadoes can exceed 1000,the average number of tornadoes per year in China over the past half a century is estimated to be fewer than 100(Fan and Yu,2015),even though both countries are located in a similar latitudinal zone of the Northern Hemisphere.The annual average recorded number of F1(Fujita scale)or EF1(enhanced Fujita scale)intensity or higher tornadoes in China between 1961 and 2010 is about     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

2015年秋季我国气候异常及暖湿成因分析

2015年秋季,全国平均气温较常年同期偏高,降水量偏多明显,为1951年以来同期第三高值,我国总体呈现“暖湿”的特征,但南北方降水特征有明显差异。受水汽条件不同的影响,华南地区为持续性降水,而华北表现为阵性降水。诊断分析表明,华南的持续性降水偏多与超强厄尔尼诺和赤道印度洋偏暖的共同作用有关。赤道东太平洋和赤道印度洋地区海温偏高,加强了西太平洋地区低层的反气旋环流异常,造成西太平洋副热带高压偏强偏西。受副热带高压西侧异常南风的引导,来自南海和西太平洋的异常水汽向华南输送,造成了秋季华南持续降水偏多。而受中高纬度环流经向度偏大的影响,冷空气在华北活跃,配合东路和南路水汽的阶段性输送,则造成了华北的降水偏多。     

高性能计算技术在气象领域的应用

高性能计算通过应用超级计算机与并行处理技术解决复杂的计算问题,是信息技术发展比较迅猛的领域之一。气象应用始终是高性能计算的重要领域,高性能计算技术有效地解决了高分辨率、高精度气象数值预报模式发展限制,在气象预报预测业务中发挥着核心支撑作用。数十年以来,由于数值天气模式研究和业务运行对计算资源的强烈需求,国内外气象领域高性能计算机及应用迅速发展起来。气象领域对高性能计算能力及系统的可靠性需求日益提升。高性能计算技术将与气象预报应用日益融合,相互影响促进,不断创新发展。为满足气象预报预测业务和科研工作需求,中国气象部门将进一步提升高性能计算能力,并致力于优化集约高性能计算系统布局,高效管理计算资源,发挥最大效益。     

镇江市酸雨特征及影响因素分析

利用2008年1月至2012年12月镇江市酸雨观测资料,详细分析了镇江市酸雨强度和发生频率的年、季、月变化特征,并对影响酸雨发生的气象因素进行了分析。结果表明:镇江市酸雨年、季、月平均pH值均较低,酸雨强度均较大,春季和秋季是酸雨的高发季节。由降水量、风向、逆温及外来污染源垂直输送等气象要素对镇江市酸雨的影响可知,镇江市强酸雨的发生频率和占总降水量的比例均较高;NE、SE和E这3个风向对镇江地区酸雨的总贡献率超过50.0%;华北、华东、湖南和湖北地区是镇江市酸雨发生的可能外来污染源;低层(925hPa及以下高度)出现逆温时,更易出现酸雨,且逆温层厚度均较深厚;逆温温差的强度与酸雨强度有一定的关系,即温差越大,pH值越低,酸雨强度越大。     

防雷箱残压“二次感应”的研究试验与仿真

防雷箱广泛应用在各类低压配电系统的雷电防护中,为研究防雷箱在8/20μs冲击电流作用下常出现"二次感应"的现象,基于Haefely DIAS 733测试系统通过大量试验,结合电磁感应理论对感应电压进行计算,并利用PSPICE建立感应回路。结果表明:1防雷箱内部平行布线且输入输出同端口时,"二次感应"电压幅值服从电磁感应定律,且可进行较精确计算。2采用双绞线布线、地线与输出线非平行、输入与输出线异端口且与地线非平行3种方法可有效地降低因电磁感应而增加的残压,从而获得较低的输出残压。3试验表明,当内部采用输入与输出线异端口且与地线非平行的综合布线方案后,效果最明显,在60kA冲击电流下,相对未改进的防雷箱,采样点残压下降约45%。     

基于LLS的雷电流波头时间分布特征

利用湖北省2007—2013年雷电定位系统(Lightning Location System,LLS)监测的雷电流相关参数资料,采用数理统计方法,对雷电流波头时间的年、季、月、日变化以及累积概率和概率密度分布进行了统计分析。结果表明,正闪电平均波头时间为4.8μs,负闪和总闪电为3.6μs;一年中,正闪电7月波头时间最短,月变化特征大致呈"V"形;负闪和总闪电5—7月波头时间比其他月份短,月变化特征大致呈"U"形。正闪电波头时间季节、日变化明显,负闪和总闪电季节、日变化不明显。波头时间小于等于10μs时,正闪、负闪和总闪电的波头时间累积概率分别为95.5%、99.7%和99.6%,总闪电95%的波头时间大于1.5μs,50%的大于3.1μs,5%的大于6.0μs。波头时间在2~3μs时,正闪、负闪和总闪电的概率密度最大。气温较低月份,波头时间相对较长,反之,波头时间相对较短,其原因可能与雷电流幅值大小有关,具体原因有待进一步研究。     

Erratum to: Climate change projections over India by a downscaling approach using PRECIS

The author “Bhaski Bhaskaran” and his affiliation “Fujitsu Laboratory of Europe, Middlesex, UK” should be replaced by “Balakrishnan Bhaskaran”, “Fujitsu Laboratories of Europe Limited, Hayes Park, Middlesex, UK”, respectively.The corrected name and affiliation are shown in this erratum.     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

Diurnal variations of temperature as a cause of periodic variations of high-frequency geoacoustic emission

Investigated is the periodic component of geoacoustic emission within the frequency band from 2.0 to 6.5 kHz registered by the piezoceramic hydrophone installed near the bottom of Lake Mikizha in the Kamchatka Peninsula. It is revealed that the variation period is 24 hours and the maximum variation is observed in summer, when the average daily air temperature is above 10°C. In that period the close connection is revealed between the series of air temperature and geoacoustic emission. Taking into account the similar results of measurements of the ground surface slope at the observation site, the most probable reason for the diurnal variations of high-frequency geoacoustic emission is the deformation of near-surface sedimentary rocks caused by diurnal variations of temperature.