1∶25万福贡县幅、丽江市幅、腾冲县幅、大理市幅、潞西市幅、凤庆县幅地质调查成果与进展

认为牛井山蛇绿构造混杂岩与铜厂街蛇绿岩相当;新建石鼓蛇绿构造混杂岩;沿怒江断裂带多处发现硅质岩、浅变质玄武岩及砂板岩复理石建造;澜沧江构造带东侧发现大量火山岩;结晶基底中有大量变形变质古老花岗岩类。     

铅在高岭石表面的解吸动力学

经过酸性条件下（pH=3.1)Pb(Ⅱ）高岭石表面解吸动力学的研究，发现解吸反应与吸附反应动力学相似，也是一个二阶段的过程，即初始的快速解吸之后伴随着一个缓慢的释放过程。在快速解吸阶段，7min内解吸量即已达到41％。吸附铅没有完全解吸，有近38％残留在高岭石表面，即解吸和吸附过程不是完全可逆的。解吸过程可以用扩展Elovich方程、Elovich方程、双常数方程和一级扩散方程较好地似合。     

山东东营凹陷渐新统东营组的天文地层研究

选定东营凹陷牛38、郝科1和利1等3口井东营组进行天文地层研究。以牛38井沙河街组一段顶界31.8Ma作为东营组底界年龄,得到3口井各段的年龄平均值为:东二段底28.406Ma,东一段底25.385Ma和顶界24.467Ma。东营组底界(31.829Ma)大致相当于"中国区域地层表"提出的32Ma的渐新统底年龄,但是与"国际地层表"提出的渐新世鲁培尔阶底界(33.9Ma)相差1.9Myr。东三段底界、顶界的平均年龄分别为31.829Ma、28.406Ma大致可与中国的乌兰布拉格阶或国际的鲁培尔阶中上部相当;东二段底界至东一段顶界的平均年龄大致相当于塔本布鲁克阶或夏特阶。根据东营组岩石地层与天文地层研究认为,在三角洲沉积过程中,东营组各段界线是穿时的,如东三与东二段界线的年龄相差可达1.4Myr。根据偏心率理论曲线和小波分析,夏特阶底界(28.4Ma)附近的变化可能与偏心率100ka左右理论周期的特殊变化有关。     

CFG短桩复合地基技术用于高层建筑地基处理的探讨

阐述了ＣＦＧ短桩复事地基技术的特点及应用范围,并列举了２个具体的工程实例,经过实践验证该技术是一项省钱、施工质量容易得到保证且能满足复合地基承载力及最终沉降量要求的新技术。探讨了ＣＦＧ短桩复合地基设计中的几个理论问题,产提出了该项技术的进一步研究方向。     

相山铀矿田横涧矿床的成因归属

横涧矿床位于相山火山-侵入杂岩体（相山铀矿田）的北部，是相山众多与花岗斑岩有关的铀矿床的典型代表，矿体主要产于横涧花岗斑岩体中，其外接触带粉砂岩、变质岩中也有矿体分布。矿体形态为脉状，是岩浆热液作用的产物，成矿前热液呈碱性，第一期成矿热液呈弱碱性，第二期成矿热液呈弱酸性，该花岗斑岩以往认为是潜火山岩，但潜火山岩本身概念太复杂，实际上不便运用，相山铀矿田边部的浅成一超浅成侵人体，与其说是潜花岗斑岩（潜英安斑岩）石油如说是花岗斑岩（英安斑岩）。横涧矿床是斑岩铀矿床。     

宁夏盛夏大雨和暴雨时的气流分析

一个地方降水量的大小取决于潮湿空气的向上输送。气层的湿度大,上升速度大,降水强度就大,反之降水强度小。在作暴雨预报吋,除注意大形势的特点和几种天气系统与大雨和暴雨的关系外,还需要从引起降水的水汽垂直输送条件去考虑。去年我们同兄弟省(区)协作搞的高原低气压系统的研究中,也发现当高原有低气压系统移出并引起本区     

IAP GCM模式大气背景环流在厄尔尼诺年的异常

本文分析了中国科学院大气物理研究所大气环流模式(IAP GCM)模式大气5～9月平均环流(本文称为背景环流)。结果表明;厄尔尼诺年一系列重要系统(南方涛动、瓦克环流、哈德莱环流、西太平洋副热带高压和热带辐合带)及大范围降水均发生明显异常;北半球西太平洋热带、副热带是环流异常的主要区域。它们与观测资料的分析结果基本一致,从而论证了该模式在低纬环流研究中的应用前景。     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS

In the context of tower measured radiation datasets.following the correction principle meetinga diagnostic equation in data quality control and in terms of a technique for model construction ondata and ANN(artificial neural network)retrieval for BP correction of radiation measurementswith rough errors available,a BP model is presented.Evidence suggests that the developed modelworks well and is superior to a convenient multivariate linear regression model,indicating its wideapplications.     

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November–December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ～10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.     

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960–2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001–2010) and the predicting period (2011–2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.