Carboniferous eclogite and garnet–omphacite granulite from northeastern Hainan Island,South China: Implications for the evolution of the eastern Palaeo‐Tethys

High‐P (HP) eclogite and associated garnet–omphacite granulite have recently been discovered in the Mulantou area, northeastern Hainan Island, South China. These rocks consist mainly of garnet, omphacite, hornblende, quartz and rutile/ilmenite, with or without zoisite and plagioclase. Textural relationships, mineral compositions and thermobarometric calculations demonstrate that the eclogite and garnet–omphacite granulite share the same three‐stage metamorphic evolution, with prograde, peak and retrograde P?T conditions of 620–680°C and 8.7–11.1 kbar, 820–860°C and 17.0–18.2 kbar, and 700–730°C and 7.1–8.5 kbar respectively. Sensitive high‐resolution ion microprobe U–Pb zircon dating, coupled with the identification of mineral inclusions in zircon, reveals the formation of mafic protoliths before 355 Ma, prograde metamorphism at c. 340–330 Ma, peak to retrograde metamorphism at c. 310–300 Ma, and subsequent pegmatite intrusion at 295 Ma. Trace element geochemistry shows that most of the rocks have a MORB affinity, with initial ε_Nd values of +2.4 to +6.7. As with similar transitional eclogite–HP granulite facies rocks in the thickened root in the European Variscan orogen, the occurrence of relatively high P?T metamorphic rocks of oceanic origin in northeastern Hainan Island suggests Carboniferous oceanic subduction leading to collision of the Hainan continental block, or at least part of it, with the South China Block in the eastern Palaeo‐Tethyan tectonic domain.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

城市点云的区域生长三角网构建方法

成熟的倾斜摄影测量及密集匹配技术可获取密集的城市点云,对点云进行三角网构建是真三维建模的关键技术之一。提出了基于区域生长的三角网构建方法,使用双约束条件提高点云的搜索效率,基于点边表数据结构进行网格生长,利用夹角约束和三角形孔洞修补优化网格。实验证明,该方法获取的三角网能够准确地表达楼体、桥状建筑等在内的城市实体,网格优化后的孔洞比例约为1‰并且该方法具有一定的抗噪能力。     

依兰-伊通断裂新构造活动规律分析

作为郯庐断裂带北段主干的依兰-伊通断裂, 其新构造活动性与活动规律仍然存在不同的认识.本次工作通过详细的野外调查, 发现该断裂内活断层广泛存在, 由东、西两支北东走向的主干活断层构成, 沿着古近纪地堑边界断层发育.这些活断层主要呈破碎型结构, 多为逆右行平移活动.通过对这些活断层一系列实测擦痕反演应力场, 显示它们多是在东西向挤压中活动的, 而现今应力场转变为北东东-南西西向区域性挤压.依据本次野外观察与¹⁴ C定年, 并结合前人定年结果与近代地震分布, 表明依兰-伊通西支活断层的最新活动时代为全新世与晚更新世相间, 而东支活断层的最新活动时代主要为早-中更新世.依兰-伊通断裂内活断层显示了明显的差异性活动, 表现为西支的活动强度明显大于东支, 西支的最新活动时代皆晚于东支, 沿走向上活动性强、弱相间与最新活动时代不断变化, 以及近代地震活动不均一分布.它们沿走向上的分段性、差异性活动主要是因为被一系列北西向断层切断所致.     

Characteristics of the two active stages of lightning activity in two hailstorms

Two hailstorms that occurred in Beijing and Tianjin, respectively, are investigated, based mainly on the total lightning data observed by the System d’Alerte Fondre par Interferometric Radioelecctrique (SAFIR3000), the cloud-to-ground (CG) lightning data of a CG lightning location system, and the echo data of a Doppler radar. Both hailstorms exhibited two lightning frequency peaks: the first was before the hailfall and the second was after the hailfall, with the second peak greater than the first. The dominant polarity of the CG lightning was positive in the stage around the first frequency peak, but changed to negative in the stage around the second frequency peak. The evolution of radar echoes and the height distribution of lightning radiation sources are explored, revealing that both hailstorms had stronger convection and the main positive charge occurred at mid levels during the stage around the first frequency peak. However, whilst the Beijing hailstorm experienced the enhancement of convection, with the main positive charge at upper levels around the second frequency peak, the Tianjin hailstorm generated its second frequency peak during the period when the convection kept weakening and the main positive charge dropped to mid–lower levels. Through evaluation of the radar parameters, we investigate the mechanisms responsible for the second stage of active lightning discharge. Furthermore, the lightning activity exhibited a close relationship with radar echo parameters and hailfall. Lightning jump signals were found before the hailfall and were associated with the change ratio of the 40-dBZ echo volume above the ?15°C level, which demonstrates the application value of lightning data in severe weather warning.     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.     

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.     

中短期数字化天气预报技术现状及趋势

建立无缝隙集约化的天气预报业务体系,需要进一步加强中短期天气预报业务技术的发展。通过对现状回顾,指出经过2011—2015年的气象现代化建设,在我国国家级和部分省级业务单位已初步建立了数字化中短期业务预报技术流程,其包括数值模式系统、客观方法释用、预报主观编辑制作以及精细化格点后处理四个方面。但与国外发达国家相比,我国中短期业务技术还存在数值模式发展水平不高、客观预报技术方法未全面深入开展、缺乏有效技术工具支撑预报员进行订正、格点化的处理技术基础薄弱等问题。文章在发展现状和问题分析的基础上,提出未来应提高自主数值模式系统的基础支撑作用,深入发展海量预报信息的客观提取及订正技术,加强开发主观和客观融合的技术和工具平台,完善精细化的格点处理技术的发展思路,并进行具体阐述。最后,从技术发展的基础数据支持、路线选择、发展规律以及交流共享四个方面提出参考意见。