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181.
Accepting the concept of standardization introduced by the standardized precipitation index, similar methodologies have been developed to construct some other standardized drought indices such as the standardized precipitation evapotranspiration index (SPEI). In this study, the authors provided deep insight into the SPEI and recognized potential deficiencies/limitations in relating to the climatic water balance it used. By coupling another well‐known Palmer drought severity index (PDSI), we proposed a new standardized Palmer drought index (SPDI) through a moisture departure probabilistic approach, which allows multi‐scalar calculation for accurate temporal and spatial comparison of the hydro‐meteorological conditions of different locations. Using datasets of monthly precipitation, temperature and soil available water capacity, the moisture deficit/surplus was calculated at multiple temporal scales, and a couple of techniques were adopted to adjust corresponding time series to a generalized extreme value distribution out of several candidates. Results of the historical records (1900–2012) for diverse climates by multiple indices showed that the SPDI was highly consistent and correlated with the SPEI and self‐calibrated PDSI at most analysed time scales. Furthermore, a simple experiment of hypothetical temperature and/or precipitation change scenarios also verified the effectiveness of this newly derived SPDI in response to climate change impacts. Being more robust and preferable in spatial consistency and comparability as well as combining the simplicity of calculation with sufficient accounting of the physical nature of water supply and demand relating to droughts, the SPDI is promising to serve as a competent reference and an alternative for drought assessment and monitoring. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   
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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 εNd 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.  相似文献   
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赵娟 《地质与勘探》2016,52(3):518-523
青海省祁漫塔格地区位于柴达木盆地西南缘,是青海省重要的铁多金属成矿基地之一。利用88442个野外采样点1∶5万水系沉积物测量数据,采用Geo Expl软件圈定了该地区的衬值异常。衬值是指各元素0.5 km×0.5 km网格化数据与其园域搜索半径5km、移动步长0.5km移动平均值的比值。选用自定义累频"90、95、98、100"确定了异常的外、中、内带。通过常规方法和衬值圈定的异常的对比,表明衬值异常能够削弱不同地质背景差异,使异常形态更为规整,浓集中心更为明显,并且可能发现新异常。  相似文献   
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The possible mechanism behind the variability in the dipole pattern of boreal winter precipitation over East Asia is analyzed in this study. The results show that the SST anomalies(SSTAs) over the South Pacific Ocean(SPO) in boreal autumn are closely related to the variability in the dipole pattern of boreal winter precipitation over East Asia. The physical link between the boreal autumn SPO SSTAs and the boreal winter East Asian precipitation dipole pattern is shown to mainly be the seasonal persistence of the SPO SSTAs themselves. The seasonal persistence of the SPO SSTAs can memorize and transport the signal of the boreal autumn SSTAs to the following winter, and then stimulates a meridional teleconnection pattern from the SH to the NH, resulting in a meridional dipole pattern of atmospheric circulation over East Asia in boreal winter. As a major influencing factor, this dipole pattern of the atmospheric circulation can finally lead to the anomalous precipitation dipole pattern over East Asia in boreal winter. These observed physical processes are further confirmed in this study through numerical simulation. The evidence from this study, showing the impact of the SPO SSTAs in boreal autumn,not only deepens our understanding of the variability in East Asian boreal winter precipitation, but also provides a potentially useful predictor for precipitation in the region.  相似文献   
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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.  相似文献   
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