Robustness of Precipitation Projections in China:Comparison between CMIP5 and CMIP3 Models

Three sources of uncertainty in model projections of precipitation change in China for the 21st century were separated and quantified: internal variability,inter-model variability,and scenario uncertainty.Simulations from models involved in the third phase and the fifth phase of the Coupled Model Intercomparison Project(CMIP3 and CMIP5) were compared to identify improvements in the robustness of projections from the latest generation of models.No significant differences were found between CMIP3 and CMIP5 in terms of future precipitation projections over China,with the two datasets both showing future increases.The uncertainty can be attributed firstly to internal variability,and then to both inter-model and internal variability.Quantification analysis revealed that the uncertainty in CMIP5 models has increased by about 10%–60% with respect to CMIP3,despite significant improvements in the latest generation of models.The increase is mainly due to the increase of internal variability in the initial decades,and then mainly due to the increase of inter-model variability thereafter,especially by the end of this century.The change in scenario uncertainty shows no major role,but makes a negative contribution to begin with,and then an increase later.     

高斯投影的复变函数表示

借助复变函数理论讨论高斯投影的复变函数表示,与传统的高斯投影实数域表达式相比,本文导出的高斯投影正反解表达式形式紧凑,公式简单,特别是基于复变函数建立的尺度比和子午线收敛角公式能表示为闭合形式;当e=0时,高斯投影正反解公式均为简单准确的闭合表达式。最后,通过算例对导出的新公式进行验算。     

投影寻踪学习网络的遥感影像分类

采用投影寻踪(projection pursuit,PP)学习网络方法建立了一种新的遥感影像分类模型。该方法结合了统计学中投影寻踪算法节点函数灵活的非参数估计特点和人工神经网络的自学习功能,具有简捷的网络结构和良好的鲁棒性能。利用苏州市TM影像进行了分类实验,将分类结果与BP神经网络和最大似然法的分类结果相比较,投影寻踪学习网络的分类精度较高,具有一定的实用性。     

一种非监督快速端元提取方法

正交子空间投影(OSP)方法广泛用于目标与背景的分离之中,对于高光谱影像,OSP可用于目标提取和混合像元分解,但缺点是需要端元的先验知识。针对这一问题,本文基于OSP的原理提出了一种非监督快速端元提取方法。实验使用模拟高光谱数据和由OM ISⅠ获取的真实高光谱数据,结果精度令人满意,证明了本文算法进行端元自动提取的可行性。     

基于支撑向量回归的高光谱混合像元非线性分解

提出了基于支撑向量回归的高光谱混合像元自动分解.首先利用投影迭代的方法自动寻找到影像的典型地物光谱,然后利用Hapke近似函数模拟出非线性的训练和测试数据.支撑向量回归的混合像元分解方法与基于基函数分解方法的不同点是不需要预先确定非线性的映射形式,它通过核函数,把像元矢量从低维空间映射到高维特征空间,使得在特征空间中构造的线性光谱组合对应着原始空间（像元空间）的非线性组合特性,从而揭示了典型地物光谱之间的高阶性质,提高了混合像元的分解精度.实验结果证明,这种方法具有很高的混合像元的分解精度.利用模拟数据作分解精度的评价,表明97%以上的像元分解绝对误差不大于10%,而各类总体平均平方根误差均小于3.5%.     

地图手工数字化对点误差的统计分布

定义了对点误差，导出了统计分布密度和分布函数，给出了矩母函数、特征函数和数字特征，以及对点误差及其投影分量和标准化分布     

常用海图投影平面上大椭圆航线的表象与曲率分析

利用空间矢量方法推导出了椭球面上只与起止点地理坐标有关的大椭圆航线方程,代入4种常用海图投影的正解公式,得到不同投影平面上的大椭圆参数方程;利用上述参数方程进而推导出了不同投影面上大椭圆航线的曲率与曲率半径公式。选取伦敦到纽约的大椭圆航线为例,通过绘制不同投影面上的大椭圆航线并分析其曲率、曲率半径变化曲线可知,大椭圆航线在日晷投影上的表象为曲率处处为0的直线,而在其他投影面上的表象为曲率较小但不断变化的曲线。利用推导的曲率半径公式可以计算各类大椭圆航线上任意位置的“代曲直距”,方便在不同比例尺的海图上对大椭圆航线进行量测和绘制,提高作图效率。     

2018年斐济MW8.2深震:高频辐射过程和发生原因

2018年8月19日,在斐济东部海域563 km深处发生了一次M_W8.2地震.我们首先挑选位于美国阿拉斯加地区的131个宽频带台站构成台阵,选用垂直分量0.5~2 Hz的高频信号,利用广义台阵反投影技术对这次地震的破裂过程进行了成像,然后基于破裂速度对地震的辐射效率进行了估计.结果表明,这次地震总体上呈单侧破裂,破裂方位在3.0°左右,破裂总长度约51 km,持续时间22 s,平均破裂速度为2.5 km·s^-1.但能量释放有2次高峰,形成两次子事件.第一次为前10 s,峰值在7 s左右,破裂速度为2.9 km·s^-1,辐射效率为45%.第二次为10~22 s,峰值在15 s左右,破裂速度为1.6 km·s^-1,辐射效率为26%.结合震源位置、震源机制、破裂速度以及辐射效率,我们认为这次地震是由于俯冲板块前缘受到下部地幔物质上浮阻力引起的剪切失稳所致,起初板块内部的脆性破裂表现突出,致使辐射效率较高,后来震源处高温高压下的熔融耗散特征逐渐凸现,致使辐射效率下降.     

不同气候情景下华北平原蒸发与径流时空变化分析

基于中国气象局国家气候中心生成的IPCC第四次评估报告中23种气候模式的情景集成数据,采用Schreiber公式和Thornthwaite方法计算实际蒸发和径流,分析了2001-2060年SRES A1B、A2和 B1这3种情景下,华北平原气温、降水、蒸发与径流的时空变化。结果表明：未来华北平原气温呈升高趋势,且冬半年升温幅度大于夏半年;降水亦呈增加趋势,而冬半年降水增加幅度小于夏半年;与此相应,华北平原蒸发和年径流呈增加趋势,增幅和空间差异随时间推移而增大,到2041-2060年蒸发将上升7.1%～9.4%,径流将增加8.7%～10.7%。     

Impact of the spatial variability of daily precipitation on hydrological projections: A comparison of GCM‐ and RCM‐driven cases in the Han River basin,Korea

In this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes.