秦岭成矿带Pb、Zn化探异常与铅锌矿常无对应关系的原因

秦岭成矿带中Pb、Zn化探异常区常无铅锌矿,而铅锌矿区又常无Pb、Zn异常,这种Pb、Zn异常与铅锌矿的不对应关系,通常导致大量的Pb、Zn异常无找矿指示意义,甚至给找矿评价、勘查部署选区工作带来了某些误导。本次的大量实验研究表明,秦岭地区水系沉积物中Pb、Zn元素主要在较粗粒级(-10~+60目)组分和细粒(小于140目)组分两端粒级中富集,而在中等粒度(80~120目)中贫化。以往所采用的统一粒度(-60目)样品分析所圈定的Pb、Zn异常,主要是细粒级吸附态的Pb、Zn在远离铅锌矿源区富集的反映,强化了远离矿源“次生富集”的信息。据此建议,在秦岭地区开展的面积性化探工作中,首先通过典型矿区粒级试验,建立区内不同背景区成矿元素的合理粒级范围,使所选用的粒度组合(粗、中、细粒)对在不同粒级中富集的元素更具代表性,改变秦岭Pb、Zn等化探异常指导找矿工作的被动局面,确保化探工作能更好地指导地质找矿和勘查部署选区工作。     

Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China

Surface solar irradiance (SSI) nowcasting (0–3 h) is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants. In this study, an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A (FY-4A) geostationary satellite. The system is composed of two key components: The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model. The second component is the SSI nowcasting model, the core of which is the derivation of the cloud motion vector (CMV) using the block-matching method. The goal of simultaneous estimation and nowcasting of global horizontal irradiance (GHI) and direct normal irradiance (DNI) is fulfilled. The system was evaluated under different sky conditions using SSI measurements at Xianghe, a radiation station in the North China Plain. The results show that the accuracy of GHI estimation is higher than that of DNI estimation, with a normalized root-mean-square error (nRMSE) of 22.4% relative to 45.4%. The nRMSE of forecasting GHI and DNI at 30–180 min ahead varied within 25.1%–30.8% and 48.1%–53.4%, respectively. The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern, being lower in spring–summer and higher in autumn–winter. The FY-4A has great potential in supporting SSI nowcasting, which promotes the development of photovoltaic energy and the reduction of carbon emissions in China. The system can be improved further if calibration of the empirical method is improved.     

斜坡地质灾害气象预报预警空间数据库的设计与建立

斜坡地质灾害的频发常常带来重大的经济损失,尤其是降雨诱发的斜坡地质灾害问题日益严重.鉴于传统数据库不能有效地管理空间数据的局限性,采用ArcSDE Geodatabase和SQL Server空间数据库技术有利于对斜坡地质灾害气象预报预警的各种空间数据进行高效、统一、科学地管理.从数据准备、空间数据库的设计、空间数据库的建立三大方面介绍了斜坡地质灾害气象预报预警空间数据库的设计与建立;最后以云南省怒江州为例,对该数据库进行实例验证,充分地论证了该数据库具有一定的可行性.研究成果可为斜坡地质灾害气象预报预警和防治提供科学的依据.     

GPS周跳探测的方法研究

在分析Blewitt方法的基础上,加入一种新的方法共同探测周跳。新方法将无电离层组合作为基本观测值,对其进行星间差分,差分结果消除了电离层延迟和接收机钟差的影响,而且差分后的组合观测值具有良好的光滑性,有利于周跳的探测。实验证明,3种方法共同使用可以更准确地探测出周跳发生的位置。     

综合信息矿产资源预测半定量快速分析方法初探-以巴音诺尔公-狼山地区铜多金属矿预测分析为例

本文在研究巴音诺尔公-狼山地区区域地质背景和主控矿因素的基础上,应用MAPGIS图形编辑、数据库管理及空间分析查询检索功能,建立了渣尔秦山群、岩浆岩、断裂构造、矿产和地球化学等控矿信息图层,分析了该地区矿产资源与各类控矿信息的关系,确定了10个单位(2km)的范围内为大型断裂构造对矿产地的最佳影响带.以铜矿为例,圈定了霍各乞外围铜预测区、盖沙图-阿责庙一带铜预测区和朱拉扎嘎-红格尔玉林金铜多金属预测区.初步形成一套基于MAPGIS系统下的综合信息矿产资源预测半定量快速分析方法.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

基于多模式集成技术的地面气温精细化预报

基于ECMWF、JMA、T639、WRF四个数值模式2012年6月1日—9月30日地面气温3—60 h预报资料和郑州加密自动站资料,利用多模式集合平均(EMN)、消除偏差集合平均(BREM)、加权消除偏差集合(WBREM)及多模式超级集合(SUP)4种方法,对2012年8月29日—9月27日郑州城区11个站点地面逐3 h气温进行多模式集成预报试验,采用绝对误差对预报结果进行检验评估,结果表明:在30天的预报期内,BREM、WBREM及SUP对于大多数站气温预报效果有明显改善,而EMN方案对11个站预报效果改善则不太明显;4种方案中,BREM和WBREM预报效果相对较好且稳定,各个站上3—60 h预报的绝对误差均在2℃附近或以下;SUP方案虽然对个别站预报误差较低,但是其预报效果并不稳定,一些站点的个别预报时效误差大于2℃。对于郑州观测站的气温预报而言,4种集成方案20时起报的气温误差明显小于08时起报的误差,并且20时起报的SUP集成方案绝对误差明显小于其他方案的绝对误差。总体而言,BREM、WBREM及SUP三种集成方案能够给郑州精细化预报业务提供较好的参考。