Assessment of the Decadal Prediction Skill on Global Land Summer Monsoon Precipitation in the Coupled Models of ENSEMBLES

Global monsoon precipitation plays a crucial role in the local social economy and global large-scale circulation and energy cycle. Using the decadal prediction output for 1960-2015 from ENSEMBLES Stream 2, the decadal hindcast skill of climate models on global land monsoon precipitation and the potential source of predictability were examined in this paper. It is found that the decadal variation of global and southern hemispheric land monsoon precipitation is not well hindcasted by ENSEMBLES. However, the Northern Hemispheric land Summer Monsoon (NHSM) precipitation in hindcast is well predicted, including the observed downward trend from 1960 to the late 1970s and upward trend since the 1990s. The main deficiency is that the minimum NHSM precipitation occured in mid-1970s, which is 10-year earlier than the observation, leading to poor prediction of NHSM precipitation from the mid-1980s to early 1990s. Mega-ENSO and Atlantic Multi-decadal Oscillation (AMO) are the two main factored that modulate the decadal variation of NHSM precipitation. The result shows that the relationships of NHSM precipitation with mega-ENSO and AMO in ENSENBLES are higher than the observation. The climate models well predicted the increase from 1960 to the late 1970s and decrease trend since the 1990s of mega-ENSO and AMO. It is the primary source of the prediction skill on NHSM changes during the two periods. Although AMO is well predicted by ENSEMBLES (highest correlation coefficient with observation is 0.85), the prediction skill of mega-ENSO is limited, leading to poor performance in predicting NHSM precipitation from the mid-1980s to early 1990s. Thus, improving the prediction of mega-ENSO can be seen as one important method of better decadal prediction of NHSM precipitation.     

Impacts of Initialization Schemes of Oceanic States on the Predictive Skills of the IAP Near-Term Climate Prediction System

Based on the near-term climate prediction system of the Institute of Atmospheric Physics (hereafter IAP-DecPreS), we developed two distinct initialization schemes for the Coupled Global Climate Models (CGCM), FGOALS-s2. The first scheme used the Incremental Analysis Update (IAU) to assimilate gridded oceanic temperature and salinity data derived from the EN3 dataset. The second scheme used the merge of the ensemble optimal interpolation (EnOI) and IAU scheme (hereafter EnOI-IAU) to assimilate raw observational oceanic temperature and salinity profiles. The predictive skills of the decadal prediction experiments based on the two schemes were compared. Several metrics including temporal correlation and root mean square skills score indicate that the experiment based on the EnOI-IAU shows significantly higher predictive skills in the Sea Surface Temperature (SST) anomalies in the North Pacific associated with the Pacific Decadal Oscillation (PDO), than the experiment based on the IAU. In contrast, for the Atlantic Multi-Decadal Oscillation (AMO), the predictive skills of the experiment based on the EnOI-IAU are lower than that based on the IAU. The AMO has two activity centers, located in the subpolar and tropical North Atlantic. The skills of the experiment based on the EnOI are close to that based on the IAU in the tropical North Atlantic, while much lower than the latter in the extratropical region due to a false simulation of the warming trend in the region.     

The Simulation of Stratocumulus and Its Impacts on SST:Based on the IAP Near-Term Climate Prediction System

The near-term climate prediction system (DecPreS) is built on the initialization of the ocean state, which can be regarded as a full-coupled system with “adjusted” air-sea interactions. The relationship between stratocumulus and Sea Surface Temperature (SST ) is an essential part of air-sea interactions. In this study, we investigated such a relationship in DecPreS of the Institute of Atmospheric Physics, in which the merge of the Ensemble Optimal Interpolation (EnOI) and Incremental Analysis Update (IAU) scheme was employed. EnOI-IAU generally reproduces the spatial pattern of SST and low-clouds. However, the simulated cloud fraction/liquid water path are underestimated while the SST is overestimated in stratocumulus regimes, especially in the subtropical East Pacific and South Ocean. It is partly because the unrealistic air-sea interaction dominates these regions that the underestimated stratocumulus allows more input of incoming shortwave flux (20 W/m²). The deficient stratocumulus is highly related to the unrealistic vertical structure of Atmospheric Boundary Layer (ABL), in which the moisture, temperature and vertical heat transports concentrate at the surface layer. Our results imply that stratocumulus and ABL be important in DecPreS. Clarifying the importance of ABL and stratocumulus will provide a possible way to improve the DecPreS.     

基于主成分分析和贝叶斯正则化方法的神经网络年最大洪峰流量预测模型探讨

针时水文预测建模中输入因子过多而导致神经网络结构规模过大,泛化能力差的问题,利用主成分分析和贝叶斯正则化方法对神经网络进行改进,优化网络结构,从而提高泛化能力。以洮儿河流域镇西站年最大洪峰流量预测为例,研究结果表明,改进的神经网络预测方法与传统的神经网络方法相比,泛化能力有显著提高,而且网络的收敛也比较稳定,实际预测中效果良好。     

基于粒子群优化神经网络算法的深基坑变形预测方法

深基坑变形预测是进行施工参数调整和确保深基坑施工安全的重要手段,而如何对其变形进行有效、准确的预测是一个有待解决的技术难题。采用粒子群优化算法对神经网络模型的初始权值和阈值进行优化,并将已有的变形监测数据作为神经网络的输入参数,建立了基于粒子群优化神经网络算法的深基坑变形预测方法。将形成的方法应用于长春市火车站北广场深基坑开挖监测工程中。结果表明：8号水平位移测点预测结果的均方根误差为3.78%,平均百分比误差为5.48%;9号地面沉降点预测结果的均方根误差为5.62%,平均百分比误差为3.23%。经验证,本文方法预测深基坑开挖过程中的变形具有较高的可信度。     

浅析基于GIS矿产资源评价的方法与模式

文章简要介绍了GIS进行矿产资料评价的方法及途径,并建立了成矿信息综合评价的空间分析模式,以期为地质工作者提供有益的启示.     

基于时间序列建模的城市热岛时间尺度成分分离方法与应用

城市热岛效应是全球与区域气候变化研究中的焦点问题。基于2001—2012年较长时间序列的北京市MODIS地表温度产品及相关NDVI和反射率产品,给出地表温度时间序列构建方法。基于站点气象观测资料进行的精度验证表明地表温度时间序列构建方法可行,并最终给出城市热岛强度的量化方案。研究选取统计学中X-11-ARIMA时间序列建模方法,分离并分析城市热岛强度时间序列的结构性成分。分析发现,以平均城乡温差为指标的北京城市热岛强度季节性特征明显,与城乡土地利用状况、季节性地表覆盖、地物热特性以及气候因子等联系密切。趋势—循环特征与城市扩张速度及入选城市区域面积相关。以已发生城市热岛区域城乡平均温差为指标的北京城市热岛强度趋势—循环特性在12年间表现平稳。时间序列建模分析提取出不规则变动成分,为定量研究偶然因素对城市热岛的影响提供了可能。     

层次分析-多级模糊评判法在贵州普晴锑(金)矿区综合信息成矿预测中的应用

因综合信息成矿预测涉及的控矿因子较多,各度量指标间具有一定的层次性,有些定性指标还具有模糊性特征,高精度和定量化一直是制约其发展的难题。为了实现对研究程度较低的新矿区的成矿预测,并解决上述难题,本文试图将专家的主观判断与精确的数学运算巧妙地融合,将复杂问题及定性指标定量化,使评价过程和评判结果更具客观性,建立一种层次分析-多级模糊评价的成矿预测模型。以贵州大厂锑(金)矿田普晴矿区为例,选取地质、化探和遥感多元致矿信息,基于GIS平台,建立了层次分析-多级模糊评判的综合信息成矿预测模型,最终圈定出锑(金)矿远景区18个,其中Ⅰ、Ⅱ和III级远景区分别有4个、5个和9个。根据勘查验证,预测结果较为客观有效。     

黔东南天锦黎地区矿田级金矿定位预测——兼论勘查及研究程度较低地区的矿产预测方法

黔东南天柱-锦屏-黎平地区金矿勘查及研究程度较低,在进行矿产预测时必须有针对性地进行成矿地质背景、成矿规律、物化遥信息、找矿标志、预测要素等专题研究,更多关注成矿地质背景、成矿规律对矿产预测的主导作用,找准影响矿产预测的主要因素,客观适度地赋予物化遥信息对矿产预测的权重。同时,在条件不成熟时可将预测精度降至矿田-矿床级的定位预测,从整体上评价每个金矿田的找矿远景,并对某些部位找到矿床的可能性作进一步评述。采用了多种预测要素权重的简单相加法进行定位预测,而未具体开展基于GIS的定量预测,意在强调地质勘查及研究程度较低地区开展各项专题研究的重要性,并探讨简化矿产预测程序的可行性。经尝试,适宜于该类地区矿田级定位预测的简化流程为：a.相关专题研究;b.预测要素研究及赋值;c.各矿田成矿概率的计算及找矿远景分类;d.各矿田资源潜力评价。通过该方法尝试,该区获得找矿远景区A类4个、B类3个、C类3个。     

结合实测光谱的ASTER TIR数据岩性划分与构造样式分析:以新疆阿克苏蓝片岩为例

常见造岩矿物的特征谱带位于热红外区域,因此热红外遥感在岩石地层划分与构造样式分析方面有巨大的应用潜力,并且精细岩石地层划分是构造样式分析的基础。新疆阿克苏蓝片岩被认为是目前世界范围内保存最好的前寒武纪蓝片岩之一。本文对该地质体中采集的不同类型岩石样品进行了室内热红外发射光谱测试,发现白云母石英片岩类残余辐射特征（restrahlen features,RF）位于波长8.55、9.16和9.60 μm处,绿泥绿帘片岩类RF位于波长9.60、10.50和11.66 μm处。按照ASTER TIR波段位置将样品的发射率光谱曲线重采样,利用ASTER TIR数据计算基性指数I_b和白云母指数I_m。依据I_b和I_m得到不同岩性段分布图,该图能反映蓝片岩地质体中不同基性程度岩石、后期侵入的基性岩墙群以及白云母石英片岩单元的空间分布。从岩性区域分布特征看,我们认为新疆阿克苏蓝片岩地质体平面构造样式表现为褶皱,其轴迹线为NE—SW向,这一结果与前人对其划分的单斜地层结构有较大的差异。另外,褶皱出现在地质体南部,说明其北部所受压力较南部小,与前人认识一致。研究结果体现了热红外遥感结合实测光谱可有效探测矿物岩石信息的优势,该方法可为相关研究提供参考。