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侵入砂体对深水油气勘探的研究意义

侵入砂体是指深水沉积砂体在超压流体作用下侵入至上覆泥质沉积物中形成的砂体,它是深水盆地松散沉积物变形构造中常见的地质现象。侵入砂体由于其特殊的形成特点,可以明显改变原生沉积地层的几何形态和深水沉积物的物性参数,进而使储层复杂化,影响深水油气的勘探与开发,主要表现在:①侵入砂体改变了深水储层的性质及分布;②侵入砂体可能形成油气运移或散失的通道,并且改变了深水油气储层的垂向连通性;③大规模的侵入砂体本身也可以作为良好的油气储层;④侵入砂体的研究可以揭示未固结砂的流动机理,对于研究深水环境中广泛出现的浅水流的成因具有重要的借鉴意义。     

近30年辽河口盖州滩沉积环境研究

对采自辽河口盖州滩北部柱状样进行沉积物粒度分析和137Cs、210Pb年代测试,结合1988年、1994年、1999年和2013年4期遥感影像数据,对近30年盖州滩沉积环境进行了研究。沉积物样品主要为砂质粉砂和粉砂,分选较差,极正偏,峰态很窄至宽。近30年来盖州滩沉积速率为2.83 cm/a,沉积较为快速。1969年,盘山建闸后,虽然辽河入海径流量和输沙量减少,盖州滩沉积物粒度仍受到径流、潮流和波浪的共同影响,以径流影响为主;1999年,辽河截流后,径流影响较弱,潮流和波浪对沉积物改造作用增强。盖州滩发育过程受到径流、潮流和波浪以及人类活动的共同影响,但近30年来盖州滩主体形态无明显变化,仅北部沙洲不断淤高,与主沙体连为一起,南部受波浪改造呈鸟足状。     

预报文稿解译与精细化发报程序相结合的应用

采用在原有的预报流程基础上,通过解译预报文稿与精细化发报程序结合应用来制作预报。在实际应用中能够正确快速的把预报文稿进行翻译并入库,缩短了制作预报的时间,提高了预报业务流程的完整性,减轻了预报员的劳动量,同时也避免了重复性工作,为及时准确的发报工作提供了保证。     

雷击火灾现场痕迹物证的鉴识方法

对雷击火灾现场痕迹物证的提取方法及提取部位进行了论述.根据火灾现场雷击痕迹物证的特点,应采用剩磁检测和金相分析的方法进行雷击火灾的技术鉴定,结合实际火灾案例论述了对利用剩磁检测方法和应用金相分析方法鉴别雷击火灾痕迹物证的原理和判据;分析了雷电的种类及引发火灾的机理,归纳总结了雷击引发火灾的主要原因,并提出了预防雷击火灾的措施.     

Sensitivity and uncertainty analysis of mesoscale model downscaled hydro‐meteorological variables for discharge prediction

Precipitation and Reference Evapotranspiration (ETo) are the most important variables for rainfall–runoff modelling. However, it is not always possible to get access to them from ground‐based measurements, particularly in ungauged catchments. This study explores the performance of rainfall and ETo data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis data for the discharge prediction. The Weather Research and Forecasting (WRF) mesoscale model coupled with the NOAH Land Surface Model is used for the retrieval of hydro‐meteorological variables by downscaling ECMWF datasets. The conceptual Probability Distribution Model (PDM) is chosen for this study for the discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimations are taken into account for the PDM calibration and prediction in the case study catchment in England following the Generalized Likelihood Uncertainty Estimation approach. The goodness of calibration and prediction uncertainty is judged on the basis of the p‐factor (observations bracketed by the prediction uncertainty) and the r‐factor (achievement of small uncertainty band). The overall analysis suggests that the uncertainty estimates using WRF downscaled ETo have slightly smaller p and r values (p= 0.65; r= 0.58) as compared to ground‐based observation datasets (p= 0.71; r= 0.65) during the validation and hence promising for discharge prediction. On the contrary, WRF precipitation has the worst performance, and further research is needed for its improvement (p= 0.04; r= 0.10). Copyright © 2013 John Wiley & Sons, Ltd.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.