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Evaluation of daily precipitation in China from ECMWF and NCEP reanalyses

ＩＮＴＲＯＤＵＣＴＩＯＮＷｅａｔｈｅｒｆｏｒｅｃａｓｔｉｎｇａｓａｍａｊｏｒｓｕｂｊｅｃｔｉｎａｔｍｏｓｐｈｅｒｉｃｓｃｉｅｎｃｅｈａｓｄｅｖｅｌｏｐｅｄｓｉｎｃｅｔｈｅ 1 950’ｓｉｎｔｏａｍｏｄｅｒｎｓｃｉｅｎｃｅ .Ｎｕｍｅｒｉｃａｌｗｅａｔｈｅｒｆｏｒｅｃａｓｔｍｏｄｅｌｓａｒｅｅｘｔｅｎｓｉｖｅｌｙａｎｄｆｒｅｑｕｅｎｔｌｙｕｓｅｄｔｏｃｈｅｃｋｔｈｅｔｈｅｏｒｉｅｓ,ｒｕｌｅｏｕｔｔｈｅｏｌｄｉｎｃｏｒｒｅｃｔｏｎｅｓａｎｄｐｒｅｓｅｎｔｎｅｗｉｄｅａｓ,ａｎｄｓｕｇｇｅｓｔｍｅｔｈｏｄｓ…     

Exercise in downscaling on sea surface temperature along Chinese coast

ＩＮＴＲＯＤＵＣＴＩＯＮＡｎｔａｒｃｔｉｃｋｒｉｌｌ(ＥｕｐｈａｕｓｉａｓｕｐｅｒｂａＤａｎａ)ｉｓａｋｅｙｏｒｇａｎｉｓｍｏｆｔｈｅＡｎｔａｒｃｔｉｃｍａｒｉｎｅｅｃｏｓｙｓｔｅｍａｎｄａｐｏｔｅｎｔｉａｌｆｉｓｈｅｒｙｒｅｓｏｕｒｃｅ.Ｉｔｈａｓｂｅｅｎｔｈｅｓｕｂｊｅｃｔｏｆｍｕｃｈｒｅｓｅａｒｃｈ.Ｄｅｓｐｉｔｅｔｈｅｌｏｎｇｈｉｓｔｏｒｙｏｆｒｅｓｅａｒｃｈｄａｔｉｎｇｂａｃｋｔｏｔｈｅ1930’ｓ,ｔｈｅｒｅａｒｅｓｔｉｌｌｑｕｅｓｔｉｏｎｓａｂｏｕｔｉｔｓｐｏｐｕｌａｔｉｏｎｓｔｒｕｃｔｕ…     

Trends and risk evolution of drought disasters in Tibet Region,China

Journal of Geographical Sciences - The risk posed by natural disasters can be largely reflected by hazard and vulnerability. The analysis of long-term hazard series can reveal the mechanisms by...     

Benthic foraminiferal fauna and sediment provenance of Eocene syn-rift sequences in Taiwan: implication for onset of Asian epi-continental marginal seas off China coast

Marine Geophysical Research - Early Cenozoic rift basins developed commonly on the Mesozoic basement along the SE Asia Continent. However, Eocene–Oligocene sequences were only exposed widely...     

胶州湾沉积物中石油降解菌的降解能力及多样性研究

2013年青岛输油管道爆炸,大量石油污染了附近海岸。课题组采集了污染的沉积物样品,以原油为唯一碳源和能源,富集了四个石油降解菌群。生物多样性和群落分析表明,Luteibacter、Parvibaculum 和属于食烷菌科的一个属是降解菌群的主要优势菌,都属于变形菌门。从石油降解菌群中分离筛选,获得了9株具有不同16S rRNA基因序列的降解菌,分别属于8个属。重量法测定降解菌的石油降解率,其中5株的石油降解率大于30%。GC-MS分析结果表明,石油降解菌多倾向于降解烷烃,对多环芳烃的降解能力较差,其中5株细菌的烷烃降解率较大,仅1株菌D2对多环芳烃的降解率较大,其降解率在34.9%到77.5%。通过对高通量数据的分析,表明食烷菌属是菌群A和菌群E的主要降解菌群,其中筛选获得的菌株E4可能是菌群E的一株优势降解菌。本研究所筛选菌株证明了其石油降解潜力,为油污染海滩生物修复提供了菌株资源。     

球面距离计算方法及精度比较

球面距离(角间距)计算是天文或地理学中极常用的计算之一,也是目标查找、锥形检索、交叉证认等方法的基础。数学上,通过球面几何可以直接计算出两点的距离,前人已经推导出了多个复杂程度不一的计算方法。但是由于计算机的精度有限,在进行数值计算时有舍入误差,导致公式计算结果出现偏差。对几个常用的球面距离计算公式进行了考察,测试并对比它们在不同计算环境下的精度与优缺点。此外还展示并比较了几种常用天文软件包、数据库的球面距离计算方法,以期有助于天文工作者选择适合自己当前需要的计算方法。     

More than two hundred and fifty thousand spectroscopic binary or variable star candidates discovered by LAMOST

About 786.4 thousand stars were observed by LAMOST twice or more during the first stage of its spectroscopic survey. The radial velocity differences for about 256 thousand targets are larger than10 km s^-1 and they are possible spectroscopic binary or variable candidates(SBVCs). It is shown that most SBVCs are slightly metal poorer than the Sun. There are two peaks in the temperature distribution of SBVCs around 5760 K and 4870 K, while there are three peaks in the distribution of the gravitational acceleration at 2.461, 4.171 and 4.621 cm s^-2. The locations of SBVCs on the [Fe/H]-T, [Fe/H]-log g, log g-T and H-R diagrams are investigated. It is found that the detected SBVCs could be classified into four groups. The first group has higher log g～4.621 and lower T ～ 4870 K which are mainly cool red dwarf binaries. The second group of SBVCs has logg around 4.171 cm s^-2 that includes binaries and pulsating stars such as δSet and γ Dor variables. The gravitational accelerations of the third group of SBVCs are higher and some of them are below the zero-age main sequence. They may be contact binaries in which the primary components are losing energy to the secondaries in the common envelopes and are at a special stellar evolutionary stage.The last group is composed of giants or supergiants with log g around 2.461 cm s^-2 that may be evolved pulsating stars. One target(C134624.29+333921.2) is confirmed as an eclipsing binary with a period of 0.65 days. A preliminary analysis suggests that it is a detached binary with a mass ratio of 0.46. The primary fills its critical Roche lobe by about 89%, indicating that mass transfer will occur between the two components.     

Assessment of prospective hazards resulting from the 2017 earthquake at the world heritage site Jiuzhaigou Valley,Sichuan, China

On August 8, 2017, a Ms = 7.0 magnitude earthquake occurred in the Jiuzhaigou Valley, in Sichuan Province, China(N: 33.20°, E: 103.82°). Jiuzhaigou Valley is an area recognized and listed as a world heritage site by UNESCO in 1992. Data analysis and field survey were conducted on the landslide, collapse, and debris flow gully, to assess the coseismic geological hazards generated by the earthquake using an unmanned aerial vehicle(UAV), remote-sensing imaging, laser range finders, geological radars, and cameras. The results highlighted the occurrence of 13 landslides, 70 collapses, and 25 potential debris flow gullies following the earthquake. The hazards were classified on the basis of their size and the potential property loss attributable to them. Consequently, 14 large-scale hazards, 30 medium-sized hazards, and 64 small hazards accounting for 13%, 28%, and 59% of the total hazards, respectively, were identified. Based on the variation tendency of the geological hazards that ensued in areas affected by the Kanto earthquake(Japan), Chi-chi earthquake(Taiwan China), and Wenchuan earthquake(Sichuan China), the study predicts that, depending on the rain intensity cycle, the duration of geological hazard activities in the Jiuzhaigou Valley may last over ten years and will gradually decrease for the following five to ten yearsbefore returning to pre-earthquake levels. Thus,necessary monitoring and early warning systems must be implemented to ensure the safety of residents,workers and tourists during the construction of engineering projects and reopening of scenic sites to the public.     

BoxCox multi-output linear regression for 10.7 cm solar radio flux prediction

We consider the problem of predicting the mid-term daily 10.7 cm solar radio flux(F10.7),a widely-used solar activity index.A novel approach is proposed for this task,in which BoxCox transformation with a proper parameter is first applied to make the data satisfy the property of homoscedasticity that is a basic assumption of regression models,and then a multi-output linear regression model is used to predict future F10.7 values.The experiment shows that the BoxCox transformation significantly improves the predictive performance and our new approach works substantially better than the prediction from the US Airforce and other alternative methods like Auto-regressive Model,Multi-layer Perceptron,and Support Vector Regression.