Radar rainfall uncertainty modelling influenced by wind

Radar‐based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the ‘ground reference’). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0–2 m/s), WDII (2–4 m/s) and WDIII (>4 m/s). The multivariate distributed ensemble generator is introduced and established for each subsample. Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band. On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data. Copyright © 2014 John Wiley & Sons, Ltd.     

Could operational hydrological models be made compatible with satellite soil moisture observations?

Soil moisture is a significant state variable in flood forecasting. Nowadays more and more satellite soil moisture products are available, yet their usage in the operational hydrology is still limited. This is because the soil moisture state variables in most operational hydrological models (mostly conceptual models) are over‐simplified—resulting in poor compatibility with the satellite soil moisture observations. A case study is provided to discuss this in more detail, with the adoption of the XAJ model and the Soil Moisture and Ocean Salinity (SMOS) level‐3 soil moisture observation to illustrate the relevant issues. It is found that there are three distinct deficiencies existed in the XAJ model that could cause the mismatch issues with the SMOS soil moisture observation: (i) it is based on runoff generation via the field capacity excess mechanism (interestingly, such a runoff mechanism is called the saturation excess in XAJ while in fact it is clearly a misnomer); (ii) evaporation occurs at the potential rate in its upper soil layer until the water storage in the upper layer is exhausted, and then the evapotranspiration process from the lower layers will commence – leading to an abrupt soil water depletion in the upper soil layer; (iii) it uses the multi‐bucket concept at each soil layer – hence the model has varied soil layers. Therefore, it is a huge challenge to make an operational hydrological model compatible with the satellite soil moisture data. The paper argues that this is possible and some new ideas have been explored and discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

济东煤田埠村煤矿瓦斯涌出量研究

煤矿瓦斯不仅关系到能否起到利用价值,同时也关系到众多矿工的安全问题,鲁中地区是山东重要的煤炭资源基地,埠村煤矿作为其中重要的一部分,其瓦斯研究价值不言而喻。运用沉积学、构造地质学、煤地质学,数学分析等相关学科和方法,从影响瓦斯涌出量的地质构造、顶底板岩性、岩浆岩、水文地质条件、上覆基岩厚度等地质因素进行综合分析,对埠村煤矿瓦斯涌出量进行量化预测,并作出其全井田瓦斯涌出量的等值线图。研究表明,埠村井田属低瓦斯矿井,瓦斯涌出量存在东高西低的趋势,主要受埠村向斜的控制,矿井瓦斯涌出量的预测分析可以对以后生产防治作出指导作用。     

沈阳地区大雾天气的UPS订正预报方法

选用2005-2009年沈阳地区5个气象站点的气象资料,总结了沈阳地区雾天的时空分布规律.沈阳地区一年中近一半的时间出现轻雾,大雾年平均16.4天;轻雾和大雾的季节分布都呈夏、秋季多,春季少的态势;大雾多发时段在清晨.在数值预报的基础上,利用UPS预报方法,进一步做出大雾天气订正预报,建立沈阳地区大雾天气UPS订正预报方法.当数值预报有大雾时,如T-Txover（最高气温出现时段温度露点差）≤5℃则可预报有大雾,当-4℃＜T-Txover≤5℃时可预报未来会出现能见度小于等于500 m的浓雾;如T-Txover≤-4℃则预报未来会出现能见度小于等于200m的强浓雾.如T-Txover＞5℃则有暖湿平流时预报有大雾,无暖湿平流时预报不会出现大雾.     

三维地震数据动态解释技术在朔州地区煤矿的应用

中国煤炭进出口公司朔州矿业公司所属4家煤矿临近担水沟大断层,地质条件复杂,次生构造发育。各煤矿于2005年均进行过三维地震勘探工作,但从采掘工程对地震勘探成果的验证情况来看,很多落差5~10m的断层均未能查明,严重制约了煤矿的安全生产。为此,结合采掘工程所获得的矿井地质资料,对三维地震资料进行了动态解释,同时利用地震波的运动学和动力学信息,有效解决了影响煤矿安全生产中的地质构造问题,取得了丰富的地质成果。     

High-pressure elastic behavior of Ca4La6(SiO4)6(OH)2 a synthetic rare-earth silicate apatite: a powder X-ray diffraction study up to 9.33 GPa

The compression behavior of a synthetic Ca₄La₆(SiO₄)₆(OH)₂ has been investigated to about 9.33 GPa at 300 K using in situ angle-dispersive X-ray diffraction and a diamond anvil cell. No phase transition has been observed within the pressure range investigated. The values of zero-pressure volume V ₀, K ₀, and $K_{0}^{'}$ refined with a third-order Birch–Murnaghan equation of state are V ₀ = 579.2 ± 0.1 Å³, K ₀ = 89 ± 2 GPa, and $K_{0}^{'} = 10.9 \pm 0.8$ . If $K_{0}^{'}$ is fixed at 4, K ₀ is obtained as 110 ± 2 GPa. Analysis of axial compressible modulus shows that the a-axis (K _a0 = 79 ± 2 GPa) is more compressible than the c-axis (K _c0 = 121 ± 7 GPa). A comparison between the high-pressure elastic response of Ca₄La₆(SiO₄)₆(OH)₂ and the iso-structural calcium apatites is made. The possible reasons of the different elastic behavior between Ca₄La₆(SiO₄)₆(OH)₂ and calcium apatites are discussed.     

GPS/terrestrial 3D laser scanner combined monitoring technology for coal mining subsidence: a case study of a coal mining area in Hebei, China

In order to overcome the shortage that point-based data acquisition techniques cannot retrieve the whole basin subsidence caused by underground mining, and to avoid complex splicing of terrestrial 3D laser scanner (TLS) point cloud data and the errors caused by such splicing, GPS/TLS combined technology is employed for mining subsidence monitoring. The basic idea of the monitoring technology is put forward. In this article, an application of the method to a coal mining area in China is presented. Support vector machine (SVM) model for GPS level conversion in the mining area is established, and a comparative analysis of SVM, BP neural network and polynomial established local quasi-geoid in the mining area is conducted. Ground surface digital elevation model (DEM) of the mining area is established by using TLS point cloud data, and the ground surface dynamic subsidence basin is obtained through a subtraction of two DEMs. The results indicate that the quasi-geoid established by using SVM model features a relatively high level of stability and accuracy and that the established mining surface DEM and subsidence basin can provide the fundamental data for the reconstruction of ecological environment in the mining area. GPS/TLS combined monitoring technology is a new monitoring technology, which entangles the advantages of both GPS and TLS and could offset their disadvantages, thus obtaining complementary advantages. According to analysis on its application in the mining area, we conclude that the technology is feasible and has a great application prospect for the mining area purposes.     

永昌电厂粉煤灰危害及其综合治理

通过１９９２年～１９９４年在永昌电厂粉煤灰场试验研究，分析了粉煤灰独特的理化性质，从风蚀的角度探讨了其危害的成因和特点，并提出了草障与植物相结合的粉煤灰综合治理措施．关键词     

Ensemble Stacked Auto-encoder Classification on LIDAR Remote Sensing Images

Light detection and ranging system (LIDAR) can obtain diverse remote sensing datasets which contains different land cover information. The datasets offer vital and significant features for land cover classification. As a new and effective deep learning algorithm, stacked auto-encoders (SAE) consists of multiple auto-encoders in which the code of each auto-encoder is the input of the successive one. The classification precision is closely related to hidden layers, and the number of samples in fine-tuning step also affects classification results. In this paper we study the classifiers based on different number of samples and hidden layers. According to appropriate parameters, we promote SAE with adaptive boosting ensemble strategy to build new classification method. Two tests which are based on LIDAR datasets are implemented. The experiment results prove that the fusion of deep learning and ensemble learning is effective to LIDAR remote sensing images. The proposed method is robust to similar scenes classification. The overall accuracy increases 6% compared with bagging method on test 1.