武汉市盛夏高温气候特征和成因及预报

利用1950～2005年盛夏(7～8月)逐日武汉市最高温度、2002年和2003年6～8月T213格点风和垂直速度资料,分析了武汉盛夏高温的气候特征,研究了高温过程及西太平洋副热带高压活动特点。分析结果表明:20世纪50年代末至60年代初、60年代中期、70年代后期、90年代和2000～2005年为高温日数偏多的5个阶段。80年代高温日数偏少。对较大的时间尺度而言,强的高温集中在50年代后期至60年代以及90年代后期至2005年。强高温过程集中在7月下旬至8月上旬。西太平洋副热带高压持续稳定控制长江中下游,是造成高温及强高温过程的主要环流系统。选用ECMWF和T213温度和纬向风场预报产品,应用灰色预测方法建立了武汉市盛夏日最高温度预报模型,该模型试用于2003年和2006年盛夏高温预报,检验结果表明该模型提供的高温定量预报有一定的参考价值。     

青海东部及邻区强震前地震活动特征参数Wq值的研究

利用1980～2007年以来,青海省东部及邻区（34°～40°N,96°～104°E）ML≥2．3级地震资料进行特征参数耽值的空间扫描。结果表明,在该区域内发生的9次6．0级以上地震中有6次震前7—28个月在震中周围出现叻值异常区,并且大部分地震前的异常区存在异常出现一异常区面积扩大一面积收缩一发震的变化过程,为地震的中短期预报提供了判定依据。     

烃气测量法在广西南丹县大厂镇更庄地区找矿潜力评价中的应用

广西大厂矿田为一大型-超大型锡多金属矿田。碳沥青、包裹体、矿石成分分析等方面的研究结果表明,区内成矿过程中存在大量有机质的参与,烃类气体是其重要的伴生组分。通过对土壤中的烃类组分分析发现,其背景含量与异常差异很明显,显示区内存在明显的成矿热液活动,深部有较大的找矿潜力。在总结烃类组分和微量元素As、Sb在更庄地区土壤中的异常展布特征的基础上,结合异常分布区的地质和构造条件,圈出了3个找矿有利区。     

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.     

基于井位的地震属性融合技术研究

在利用地震属性对储层预测的研究中,大部分理论方法主要存在利用单一属性预测储层这一缺陷,在实际应用中则存在单一属性不能正确预测储层的问题,这些问题应通过多元属性融合技术来解决,本文在已有井资料的基础上,对属性融合技术进行了研究,分析各属性对储层的影响因素,利用井位计算各地震属性融合比重,有机的结合了各属性的优点,提出了这一问题新的解决方法.实际资料的应用显示,该方法在储层预测中取得了良好的效果.     

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.