长江流域夏季降水的贝叶斯多模式集合概率预测

A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).     

台风模拟中QuikSCAT风场的非对称加强订正及同化应用

针对QuikSCAT 散射计风场资料对台风级别的强风速区还无法准确刻画这个技术问题,引入台风移速、移向、摩擦等客观因素对该风场产品资料进行非对称加强订正;然后利用四维同化技术,并设计了先同化后预报两个阶段的试验方案,对“云娜”台风进行了数值模拟试验。试验结果表明：通过模式约束调整,四维同化技术可以把QuikSCAT 海面风场资料引入到整层模式大气中,并对整层风场和气压场的强度及路径模拟都有很大的改进,而且该资料还提高了预报阶段台风强度的模拟效果,并对登陆点的预报起到了积极作用。     

NUMERICAL EXPERIMENTS AND ANALYSIS OF DIGITAL FILTER INITIALIZATION FOR WRF MODEL

Initialization and initial imbalance problem were discussed in the context of a three-dimensional variational data assimilation system of the new generation “Weather Research and Forecasting Model”. Several options of digital filter initialization have been tested with a rain storm case. It is shown that digital filter initialization, especially diabatic digital filter initialization and twice digital filter initialization, have effectively removed spurious high frequency noise from initial data for numerical weather prediction and produced balanced initial conditions. For six consecutive intermittent data assimilation cycles covering a 3-day period, mean initialization increments and impact on forecast variables are studied. DFI has been demonstrated to provide better adjustment of the hydrometeors and vertical velocity, reduced spin-up time, and improved forecast variables quantity.     

Analysis of the three-dimensional stellar velocity field using vector spherical functions

We apply vector spherical functions to problems of stellar kinematics. Using these functions allows all of the systematic components in the stellar velocity field to be revealed without being attached to a specific physical model. Comparison of the theoretical decomposition coefficients of the equations for a particular kinematical model with observational data can provide precise information about whether the model is compatible with the observations and can reveal systematic components that are not described by this model. The formalism of vector spherical functions is particularly well suited for analyzing the present and future (e.g., GAIA) catalogs containing all three velocity vector components: the propermotions in both coordinates and the radial velocity. We show that there are systematic components in the proper motions of Hipparcos stars that cannot be interpreted in terms of the linear Ogorodnikov-Milne model. The same result is also confirmed by an analysis of the radial velocities for these stars.     

联合卷积神经网络与集成学习的遥感影像场景分类

针对人工设计的中、低层特征难以实现复杂场景影像的高精度分类以及卷积神经网络依赖大量训练数据等问题，结合迁移学习与集成学习，提出了一种联合卷积神经网络与集成学习的遥感影像场景分类算法。首先基于迁移学习的思想，利用在自然影像数据集上训练好的多个深层卷积神经网络模型作为特征提取器，提取图像多个高度抽象的语义特征；然后构建由Logistic回归和支持向量机组成的Stacking集成模型，对同一图像的多个特征分别训练Logistic模型，将预测概率结果融合构建概率特征；最后利用支持向量机对概率特征训练和预测，得到场景影像的分类结果。利用UCMerced_LandUse和NWPU-RESISC 45两种不同规模的遥感影像数据集进行试验，即使在只有10%的数据作为训练样本情况下，本文方法能够分别达到90.74%和87.21%的分类精度。     

施密特巡天星表的现状和展望

扼要介绍了施密特巡天底片及基于此编制而成的施密特巡天星表的发展历程,并详细介绍了GSC2.3和USNO-B1.0的情况.分析研究了这类星表存在的问题和原因所在,并提出了一个新的计划:利用现有施密特底片资料并增加新的第3期观测,编制一个具有绝对自行、多色测光、系统均匀的高密度全天星表以满足各方面的需要.     

Noah-MP模型中积雪模拟对参数化方案的敏感性评估

针对Noah-MP模型多参数化方案、模拟结果不确定性范围难以确定的特点,选取北疆地区具有代表性的阿勒泰站气象资料作为模型驱动数据,探讨了积雪对多参数化方案的敏感性。在不考虑模型参数和驱动数据不确定性的条件下,设计了集合数为13824的多参数化方案集合模拟试验。选用Natural selection方法对物理过程的敏感性进行分析,并在敏感性分析结果的基础上进一步讨论了模拟结果的不确定性。结果表明:积雪对地表热交换、雨雪分离、土壤温度底层边界条件和第一层积雪或土壤时间方案4个物理过程敏感;在不考虑驱动数据和模型参数不确定性的条件下,多参数化方案集合模拟试验中的不确定性主要来源于敏感物理过程。去除敏感物理过程中能够明显降低模拟性能的参数化方案后,集合模拟结果的不确定性大幅减小。最后,根据分析结果构建了该站雪深和雪水当量模拟的最优参数化方案组合。     

基于集成学习的阶跃型滑坡阶跃点判别分析

针对阶跃型滑坡阶跃点识别和预测难的问题,提出了一种基于聚类分析和集成学习的阶跃型滑坡阶跃点识别和判别模型。以三峡库区八字门滑坡ZG110钻孔2010年4月至2016年12月80个滑坡位移、库水位和降雨数据为例,通过聚类分析方法识别滑坡累积位移-时间曲线中的阶跃点和平稳点,并利用K均值聚类分析检验分类结果的准确性。基于灰色关联确定了滑坡位移的最佳诱发因素,结合随机森林模型建立阶跃型滑坡阶跃点判别模型并利用八字门滑坡ZG111钻孔验证该模型的准确性。模型阶跃点和平稳点的识别准确率均达90%以上,表明该方法在阶跃型滑坡识别中具有较好的适用性,可为阶跃型滑坡的预测提供参考。     

An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

In the present paper, an ensemble approach is proposed to estimate possible modifications caused by climate changes in the extreme precipitation regime, with the rain gauge Napoli Servizio Idrografico (Naples, Italy) chosen as test case. The proposed research, focused on the analysis of extremes on the basis of climate model simulations and rainfall observations, is structured in several consecutive steps. In the first step, all the dynamically downscaled EURO‐CORDEX simulations at about 12 km horizontal resolution are collected for the current period 1971–2000 and the future period 2071–2100, for the RCP4.5 and the RCP8.5 concentration scenarios. In the second step, the significance of climate change effects on extreme precipitation is statistically tested by comparing current and future simulated data and bias‐correction is performed by means of a novel approach based on a combination of simple delta change and quantile delta mapping, in compliance with the storm index method. In the third step, two different ensemble models are proposed, accounting for the variabilities given by the use of different climate models and for their hindcast performances. Finally, the ensemble models are used to build novel intensity–duration–frequency curves, and their effects on the early warning system thresholds for the area of interest are evaluated.     

四元数的基本概念与向量旋转的欧拉公式

为方便理解四元数,首先针对两个相互平行或垂直的向量,定义它们之间的一种不可交换乘积,命名为格拉斯曼乘积,同时约定这一不可交换积满足分配律。由此,进一步给出任意两个向量之间格拉斯曼积的具体表达式,并引出四元数的概念和运算法则。从理论上证明,任意四元数都可表示为两个向量之间的格拉斯曼积,并可以利用单位四元数的正交变换来表示向量旋转的欧拉公式。