基于Denauit-Hartenbery的高分辨Kirkpatrick-Baez镜成像结构设计与控制方法研究

针对在高能等离子体X射线诊断中的Kirkpatrick-Baez （KB）高分辨显微控制较困难的问题,基于Denauit-Hartenbery （DH）原理,率先提出了双5自由度KB镜成像结构,分析了双机械手的各个连杆坐标系和位姿结构的运动学方程,从理论上分析双5自由度KB镜的像差.在此基础上搭建双5自由度KB镜光路系统,编制了KB镜控制流程,并获取了清晰的十字成像.实验结果表明,该方法设计的双5自由度机械手控制方法可以实现KB镜的精确控制,从而得到高精度分辨成像效果.     

两次华南飑线卫星亮温特征与强对流天气分析

利用Himawari-8高时空分辨率红外亮温资料和ERA-Interim再分析资料,对比冷锋型和暖区型飑线个例("4·13"和"5·6")亮温特征与雷暴大风、地面强降水的关系,结果表明:(1)两次过程的云顶最低亮温、冷云区平均亮温差异小,但两次过程初生阶段云型不同,"4·13"与"5·6"相比,冷云顶面积较小、持续时间较短、移动速度较快;(2)"4·13"("5·6")的亮温梯度大值区主要位于冷云区东南侧(西南侧),与云团移动方向平行(垂直);两次过程雷暴大风与亮温梯度均具有较好的空间对应关系,亮温梯度增大超前于雷暴大风增强,可作为提前预警指标;(3)"4·13"地面强降水集中分布在低亮温区西侧,原因为风暴顶前移导致强降水与冷云区具有空间位置差异;"5·6"地面强降水则与云顶低亮温具有较好的对应关系。     

一例苏皖中尺度对流复合体模拟结果的诊断分析

利用2010年7月22日苏皖中尺度对流复合体（MCC）数值模拟输出结果,考察了模式对MCC的模拟能力,并对模拟结果做了动力和热力诊断分析,以揭示盛夏江淮下游MCC的特征。结果表明：1） 三重嵌套网格距为3.3 km的区域WRF模式的模拟效果较佳,结果与实况一致,并可利用模拟降水的范围及强度来确定MCC的位置及演变。2） 此MCC维持约10 h,其南北不对称,并随西太平洋副热带高压西伸北抬而随之北抬。MCC核心区对流层低层有水汽丰沛的入流,并有强辐合区,呈对流不稳定层结;中层有深厚的强上升运动,并因凝结潜热大量释放呈中性层结;高层则有出流;MCC核心区对流降水非常强。3） 在垂直剖面上,该核心区散度存在中低层辐合、高层辐散的柱状结构,此配置有利于强对流维持和加强,中低层以上有深厚的强上升气流柱,这些都是MCC核心区存在强对流的标志。该MCC的螺旋结构表明其中的强对流高度有组织。     

极端气温和极端降水复合事件的气候特征

基于1961-2014年的中国台站资料,侧重探讨极端气温和极端降水复合事件的气候特征。结果表明,基于客观方法检测出四类极端复合事件：暖湿、暖干、冷湿和冷干。四类事件的频发区分别位于东北地区、华南地区、西藏及华南地区和长江以南及云贵高原一带。全国暖湿、暖干事件频数呈增加趋势,春夏季频发;冷湿事件频数在西藏东北一带呈增加趋势,而在长江流域呈减少趋势,夏秋冬季频发;冷干事件频数呈减少趋势,秋冬季频发。四类极端复合事件经验正交分解的时间序列主模态均表现为年代际信号。进一步分析四类复合事件与能源消费量的联系表明,极端湿事件与能源消费量呈正相关,而极端干事件则相反。     

基于Hurst指数的黑龙江省作物生长季降水趋势研究

基于黑龙江省78个气象站1971—2016年逐日降水资料,综合采用墨西哥帽小波分析、Hurst指数分析等方法,对黑龙江省作物生长季(5—9月)降水量变化和未来趋势进行分析及预测。结果表明:1971—2016年,黑龙江省生长季、5月、6月降水量存在7 a、14 a、7 a左右的主周期,7月、8月、9月降水量存在2 a、3 a、7 a左右的第1主周期及6 a、11 a、21 a左右的第2主周期,各月均存在最近几年降水偏多的趋势;作物生长季降水量年际间为波动式振荡变化,7月、8月振荡幅度相对较大。年代际变化总体存在增加—减少—增加趋势,20世纪80年代、90年代降水量普遍偏多,2010年以来出现急转升高变化;单站各月Hurst指数均在0.5以上,降水存在比较明显的赫斯特现象;降水主要出现在夏季且以7月最为集中,最近几年降水偏多、7月异常降水集中以及主要流域未来7月降水的持续增加趋势在农业防灾减灾上值得关注。     

A complementary depiction of the interannual variation of atmospheric circulation associated with ENSO events: Research note

Abstract

A simple diagnostic scheme, which combines a low‐pass temporal filter (with an 18‐month cutoff time) with a regular empirical orthogonal function (EOF) analysis, is used to delineate the synchronous evolution of El Nino‐Southern Oscillation‐related (ENSO‐related) modes in various variables of the ocean‐atmosphere system. Based on the causal relation chain of diabatic heating, divergent circulation and rotational flow, the diagnostic scheme extracts ENSO modes from the following data sources: the Pacific sea surface temperature (SST), the past 14‐years (1979–1992) of data generated by the Global Data Assimilation System of the National Meteorological Center, and a 10‐year (1979–1988) general circulation model climate simulation made at the Goddard Laboratory for Atmospheres. The analysis reveals the following: (a) the eigencoefficient time series of the first eigenmodes of selected filtered variables, which explain about 40–50% of their total variance, synchronize with the filtered SST averaged over Area NINO‐3; (b) the spatial structures of the first eigenmodes resemble the ensemble departures associated with ENSO events of these variables from their long term means; and (c) the results show that the proposed scheme can be easily applied to isolate and illustrate the time evolution of ENSO modes which exist in the long term observational database as well as in climate simulations.     

Permafrost hydrology in North America 1

Abstract

This paper reviews hydrologic processes in the permafrost regions of northern North America. Much work has recently been done at specific experimental plots to parallel the progress in laboratory investigations, improving our understanding of the heat and water fluxes in thawed and frozen grounds, infiltration in frozen soils, evaporation in a cold environment, interaction between snow and its frozen substrate, and the dynamics of storage in the active layer. Field research on permafrost slopes and in northern research basins adds to our knowledge of permafrost groundwater hydrology, runoff generating processes, river freeze‐up and breakup processes and allows more precise definition of basin water balance. Sufficient hydrometric data are now available to analyse the streamflow characteristics in an area with permafrost, and more work should be done along this line. It is urged that process studies be continued to gain a better understanding of the effect of permafrost upon the hydrologic cycle. Further research is needed to predict the impacts of human activities on the movement and redistribution of water.     

Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an Ensemble Kalman Filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution

Extending an earlier study, the best track minimum sea level pressure (MSLP) data are assimilated for landfalling Hurricane Ike (2008) using an ensemble Kalman filter (EnKF), in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS (Global Forecast System) analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity (Z) only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind (Vr) data (assimilated at 10-min intervals), but assimilating MSLP at 60-min intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly (noticeably) larger positive impact than Vr(Z) data. When Vr or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.