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.     

气象观测设备运行状态综合判定技术应用

从气象观测设备运行保障角度出发,基于各观测设备自身运行状态检测信息,结合气象观测数据、气象观测元数据信息以及各级气象观测技术保障业务人员人工填报业务数据,研制了我国气象观测设备运行状态综合判定技术;同时制订了各气象观测设备运行状态分类标准和显示标准,将设备运行状态分为正常、报警、故障和非观测4类状态,分别用绿色、橙色、蓝色和灰色标识.气象观测设备运行状态综合判定技术在一定程度上促进了我国气象观测装备技术保障工作的规范化、标准化开展.该技术贴合气象装备技术保障业务实际需求,设备运行状态判定真实率达100％,结果经实践证明科学合理有效,基于该方法开展的设备运行监控保障工作提高了观测系统稳定可靠的运行能力.     

东中国海黑潮海洋锋的季节变化特征及其成因

利用高分辨率海洋和大气再分析资料研究了东中国海黑潮海洋锋的季节变化特征及其成因。研究表明,东海黑潮海洋锋存在明显的季节变化,从冬季到次年春季逐渐增强,并在春季达到最强,初夏以后强度逐渐减弱,9—10月达到全年最弱。通过诊断混合层热流量方程发现,东海黑潮区一年四季均表现为暖的温度平流,有利于海洋锋的形成和维持,该暖平流也存在季节变化并在春季达到最大,对海洋锋在春季达到最强起了重要作用。海气界面净热通量在秋冬季对海洋锋的形成有促进作用,有利于海洋锋增强,而在春夏季则起抑制作用,促进海洋锋消亡。温度垂直输送全年对海洋锋起一定程度的抑制作用。总之,在海温水平平流和海表净热通量的共同作用下导致海洋锋春季达到最强,而夏秋季海表净热通量和温度垂直输送作用的共同作用致使海洋锋减弱并最后消失。因此,海洋的动力和热力共同作用导致了东海黑潮海洋锋的季节变化,其中海温水平平流和海表净热通量对海洋锋的季节变化起主要作用,而温度垂直输送项对海洋锋的发展起抑制作用,但影响相对较小。     

基于多模式集成技术的地面气温精细化预报

基于ECMWF、JMA、T639、WRF四个数值模式2012年6月1日—9月30日地面气温3—60 h预报资料和郑州加密自动站资料,利用多模式集合平均(EMN)、消除偏差集合平均(BREM)、加权消除偏差集合(WBREM)及多模式超级集合(SUP)4种方法,对2012年8月29日—9月27日郑州城区11个站点地面逐3 h气温进行多模式集成预报试验,采用绝对误差对预报结果进行检验评估,结果表明:在30天的预报期内,BREM、WBREM及SUP对于大多数站气温预报效果有明显改善,而EMN方案对11个站预报效果改善则不太明显;4种方案中,BREM和WBREM预报效果相对较好且稳定,各个站上3—60 h预报的绝对误差均在2℃附近或以下;SUP方案虽然对个别站预报误差较低,但是其预报效果并不稳定,一些站点的个别预报时效误差大于2℃。对于郑州观测站的气温预报而言,4种集成方案20时起报的气温误差明显小于08时起报的误差,并且20时起报的SUP集成方案绝对误差明显小于其他方案的绝对误差。总体而言,BREM、WBREM及SUP三种集成方案能够给郑州精细化预报业务提供较好的参考。     

冬季东亚高空副热带急流和温带急流协同变化与中国南方地区降水的关系

利用NCEP/NCAR再分析数据和中国台站降水资料研究冬季东亚高空副热带急流和温带急流协同变化特征及其与中国南方地区降水的关系,发现冬季东亚高原急流与温带急流同期反向协同变化特征最为显著。即高原急流增强,同时温带急流减弱(SW型)和高原急流减弱,同时温带急流增强(WS型)。当高原急流增强(减弱)而温带急流减弱(增强)时,中国南方地区降水显著增加(减少)。合成分析表明,不同急流协同变化型态下冷暖空气活动特征存在较大差异,高原急流与温带急流的反向协同变化可以真实反映与冬季中国南方地区降水相关联的冷暖空气活动特征,进而导致不同降水形态的产生。     

应用系统分析与事故树模型的设备雷电灾害风险评估

分析目前国内雷电灾害风险评估发展现状,针对精细化雷击风险评估需求,以某大桥塔体电梯雷电灾害风险评估为例,在对雷击损坏途径进行系统分析的基础上,引入事故树(FTA)分析方法对电梯雷击损坏事件的致因关系进行了建模、分析与评价,得出电梯雷击损坏事件发生的年预计概率、导致雷击电梯损坏事件发生的关键以及最佳控制雷击电梯损坏事件发生的最有效因素。     

省级气象广域网网络优化的设计与应用

目前湖北省气象地面宽带网省—地—县3级结构中,省局与各市(州)局间采用单条专线连接,手动VPN(Virtual Private Network)拨号线路备份的传输方式,这种传输方式不足以保证数据传输的可靠性。现设计新增一条专线,实现省到地的双线路备份。同时,增加一台省端网络互联交换机,升级地市端的网络互联路由器硬件,将数据划分为业务数据和视频会商两类,定义不同的传输方式。业务数据采用OSPF(Open Shortest Path First)动态路由方式进行传输,视频会商通过配置静态路由BFD(Bidirectional Forwarding Detection)联动的路由方式进行传输,并通过QoS(Quality of Service)保证其4 Mb/s的带宽,从而实现省到地数据业务更合理可靠地传输。     

2012/2013年冬季中国气温异常成因分析

2012/2013年冬季,我国平均气温为-3.8℃,较常年同期(-3.4℃)偏低0.4℃,就空间分布来看,我国东北、华北、黄淮、江淮和新疆北部气温较常年同期偏低。利用1951-2013年国家气候中心整理的全国160站月平均气温资料、英国Hadley中心全球海温资料、NCEP/NCAR再分析大气环流资料、德国不莱梅大学提供的海冰卫星遥感资料,通过EOF分析、回归分析、合成分析、相关分析方法研究了引起2012/2013年冬季我国气温异常的东亚中高纬大气环流异常,并从海洋环境要素异常的角度分析造成这种环流异常的原因。分析结果表明:2012/2013年冬季我国气温异常分布主要是由于北极涛动(AO,Arctic Oscillation)呈负位相,西伯利亚地区高度场异常偏高,东亚大槽明显偏深的环流形式引起的。而太平洋年代际振荡(PDO,Pacific Decadal Oscillation)负位相是引起西伯利亚高压强度偏强和东亚冬季风强度偏强的年代际海洋背景,前期9月海冰范围异常偏小是导致2012/2013年冬季AO呈现负位相及我国东北和新疆北部呈现异常低温的主要原因。     

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.