AREMS/973模式系统对2004年中国汛期降水实时预报检验

2004年夏季5～8月，AREMS/973模式系统投入了业务运行，模式区域主要覆盖了中国大陆及周边临近地区，该模式系统基于中国气象局MICAPS/9210业务数据平台，每天2次、完全自动的实时预报24 h累积降水。作者主要对每日0000 UTC时起报的24 h累积降水预报场进行综合检验，由此对AREMS/973模式系统的24 h降水预报能力给予客观的评价。AREMS/973模式系统对于中国汛期降水具有很强的预报能力，其24 h 累积降水的月总降水分布很好地反应了汛期各时期主雨带的位置、强度及范围，同时该模式系统能够准确预报出独立降水事件的发生和发展，其中，长江中下游和东北地区预报降水与观测降水的时间位相吻合最好；除了5月和8月华北地区、5月西南东部地区、以及5月和6月长江中下游地区，AREMS/973模式系统预报的降水强度总体上弱于观测降水强度，且对于25 mm 以上量级的降水过程， 模式预报的降水范围小于观测降水范围。AREMS/973模式系统预报降水的能力随着降水量级的增大而减小，降水量级愈大，模式预报技巧愈低，对于50 mm 以上量级的降水过程，模式系统预报降水事件的发生概率类似于随机偶然事件的发生概率。5月，长江中下游地区降水预报略优于其他地区，其次是华南地区，6月雨带北移至江南和江淮地区，此时，长江中下游地区降水预报显著优于其他地区，7月末至8月华北雨季开始，降水预报最优区北移至华北地区，即AREMS/973模式系统降水预报最优区随着雨带的北移而北移，主雨带所在区域往往也是模式降水预报最优的地区。作者对此进行了初步探讨。     

Cycling amongst Māori: Patterns,influences and opportunities

This paper outlines the results of mixed-methods research on Māori and cycling. Our findings suggest that Māori cycle at similar rates to Pākehā (NZ European); however conditions may differ, possibly indicating higher levels of “necessity cycling” amongst Māori. Māori experience similar barriers to cycling, including a lack of suitable cycling infrastructure, but these occur against a backdrop of stark social, economic and transport-related inequities. Particular barriers for Māori may include inflexible work conditions, concerns about neighbourhood safety, inadequate provision for social cycling, and lack of access to places of importance to Māori. We identify potential solutions, including more whānau-friendly and culturally safe cycling infrastructure, and cycling programmes designed around Māori commitments to whanaungatanga and kaitiakitanga.     

一种改进的全球对流层天顶延迟模型

顾及文献[16]所建立的全球对流层天顶延迟模型GZTD的时间分辨率为24h,为进一步提高GZTD模型的时间分辨率,利用GGOS atmosphere的2002—2009年全球天顶对流层延迟格网时间序列按照其6h的时间分辨率分别建模,再采用三次样条插值计算任意时刻的天顶对流层延迟估值,由此构建了一种时间分辨率更高(6h)的改进的GZTD模型(GZTD-6h)。经过两种模型内符合检验对比分析表明,GZTD-6h模型内符合精度(bias:0.17cm,RMS:3.9cm)优于GZTD(bias:0.17cm,RMS:4.4cm)。使用全球IGS站进行外符合检验,统计结果表明GZTD-6h模型(bias:-0.22cm,RMS:4.05cm)相比GZTD(bias:-0.45cm,RMS:4.51cm)改善明显。     

乌鲁木齐区域数值预报业务系统降水预报检验与评估分析

基于乌鲁木齐区域数值预报业务系统,运用Ts和Bias评分方法,对2012年9月1日—2015年8月31日逐日2个起报时次的逐6 h累积降水量的年与季节预报性能进行检验,并从空间上分析了2015年全疆站点逐6 h累积降水量在4个预报时段的评分特征。结果表明:(1)2个起报时次的降水评分相差较小,00 UTC起报略优于12 UTC起报,2015年系统改进了白天大量级降水的空报现象。(2)系统对晴雨预报较为准确,Bias接近1,空报、漏报率很小;随着降水阈值的升高,Ts评分减小,Bias变幅增大,空、漏报率也随之增加。系统对强降水过程以漏报为主。(3)系统的降水预报能力存在季节差异,夏季Ts评分最高,秋季次之,冬季最小;随时间模式对四季降水预报能力均有提高,降低了冬季大量级降水的漏报率和夏季大量级降水的空报率。(4)在新疆地区,08—14 BT(Beijing Time)、14—20 BT、20—次日02 BT空报站点数多于漏报,14—20 BT空报率最高;在02—08 BT整体呈漏报。(5)各站点整体来看,白天Ts评分高于夜间,山区及邻近地区评分高于平原地区;西天山评分略优于东天山,夜间晴雨预报有天山北坡漏报、南坡空报的趋势。     

积雪水热迁移机理与模型研究进展

积雪是陆地水文循环以及大气过程的重要组成部分,它调节了陆地和大气之间的能量交换,对下垫面的能量收支平衡与水热迁移起重要作用。在介绍积雪特性参数确定方法及积雪水热运移规律的基础上,对国内外现有积雪模型进行了分类和总结,并指出未来在积雪特性参数化方案、数值模拟计算、尺度转换等方面还应进行更深入的研究。     

The physical and chemical properties of circumstellar envelopes

An overview is given of recent results on some basic properties of the circumstellar envelopes of evolved stars. The focus is on well studied examples which illustrate envelopes of different types and at different stages of evolution. The close connection between the physical and chemical properties of the envelopes is emphasized.     

Rosat X-ray light curves of early-type stars

We have investigated ROSAT X-ray data of OB stars to search for evidence of time variability in the X-ray emission from early-type stars. As an example for such studies we present a detailed variability analysis for our two program stars Ori and Ori which have been multiply observed with ROSAT. The long-term analysis of both stars now covers a time range of 2.5 years and includes six pointed PSPC observations, an additional pointed HRI observation of Ori and the ROSAT all-sky survey data of both stars. Over a long time range the X-ray light curves of Ori and Ori show no evidence for variability. In the case of Ori we detected a moderate increase in X-ray count rate during a period of 2 days which can be explained as a strong shock propagating in the wind of an O-type star.     

On the red coloration of saltern crystallizer ponds. II. Additional evidence for the contribution of halobacterial pigments

In a previous paper we attempted to assess the contribution of red bacteria of theHalobacterium — Haloferax — Haloarcula group and of the -carotene-rich green algaDunaliella salina to the red colour of saltern crystallizer ponds. By means of light absorption measurements, we showed that bacterioruberin contained in the bacteria was mainly responsible for the colour of the brines, in spite of the fact that -carotene derived fromDunaliella was the pigment present in the greatest amount. This apparent discrepancy was explained by the very smallin vivo optical cross-section of -carotene, which is densely packed in globules inside theD. salina cells. We recently observed that the centrifugation technique used in the previous study to collect biomass from the ponds was unsuitable for this type of measurements, as a substantial part of theDunaliella cells present did not sediment upon centrifugation due to the low specific gravity caused by the high -carotene content. Therefore similar measurements were performed with biomass collected by filtration. Again,in vivo absorption spectra were dominated by the absorption peaks of bacterioruberin. The results reported here show that, in spite of the methodological problem associated with the earlier study, all views and conclusions expressed in our earlier paper retain their validity.     

The relation between intensity and magnitude for Italian earthquakes

This paper describes the problems concerning the relation between intensity and magnitude, which are substantially different quantities by nature. In consideration of the necessity to translate magnitude values into intensity values, andvice versa, to find magnitude values for historical earthquakes, we have searched for a correspondence that may exist between intensity and magnitude, hypothesising that magnitude values were distributed as a known function. Therefore, we have analysed two distribution functions, first the Gaussian distribution, then a box function, of which the goodness of fit has been estimated by the ² test. In conclusion, the probability distribution of magnitude vs intensity seems better described by a Gaussian curve.