Aerosol Effects on Clouds and Climate

Aerosols affect the climate system by changing cloud characteristics in many ways. They act as cloud condensation and ice nuclei, they may inhibit freezing and they could have an influence on the hydrological cycle. While the cloud albedo enhancement (Twomey effect) of warm clouds received most attention so far and traditionally is the only indirect aerosol forcing considered in transient climate simulations, here I discuss the multitude of effects.     

Water in Space: The Water World of ISO

In this review we present the main results obtained by the ISO satellite on the abundance and spatial distribution of water vapor in the direction of molecular clouds, evolved stars, galaxies, and in the bodies of our Solar System. We also discuss the modeling of H₂O and the difficulties found in the interpretation of the data, the need of collisional rates and the perspectives that future high angular and high spectral resolution observations of H₂O with the Herschel Space Observatory will open.     

Force-free magnetic field in a cylindrical flux rope without a constant alpha

It is generally assumed that magnetic fields inside interplanetary magnetic clouds and flux ropes in the solar photosphere are force-free. In order to model such fields, the solution of rot B = B is commonly used where  = const. But comparisons of this solutions with observations show significant difference. To treat this problem,we examine the solutions with .     

基于运动目标的机载多源传感器数据融合算法

在机载信息融合领域,针对复杂的战场态势环境对机载多源传感器造成的不利影响,提出了一种基于运动目标的机载多源传感器数据融合算法,包括误差估计与补偿、目标关联、估计融合3个部分。采用最小均方误差参数估计方法从数值不变的系统误差中,定量获取量测偏差的确定性部分再通过模糊隶属度函数建立关联矩阵作为目标关联的依据,以最小隶属关系为约束,利用拍卖算法求得多目标关联的最优匹配方案,最后通过IMM 自适应滤波方法,将误差配准与目标航迹估计整合进行,有效提高滤波的精度。仿真结果表明,经过上述算法处理后的融合目标精度高于任意单传感器目标探测精度。     

FY-2C星数传与云图广播转发器分系统

介绍了风云二号(FY-2)气象卫星数传与云图广播转发器分系统的功能、组成,以及频率配置、传输信号特性和增益与功率等主要技术指标.给出了C星分系统转发器的技术改进.阐明了采用的收发多工器、射电天文保护滤波器、固体功率放大器(SSPA)和功能转换开关等关键技术.测试结果和在轨运行状况表明,C星数传与云图广播转发器的性能符合设计任务书要求,在轨运行正常稳定.     

Solar and interplanetary origins of the November 2004 superstorms

During the first half of November 2004, many solar flares and coronal mass ejections (CMEs) were associated with solar active region (AR) 10696. This paper attempts to identify the solar and interplanetary origins of two superstorms which occurred on 8 and 10 November with peak intensities of Dst = −373 nT and −289 nT, respectively. Southward interplanetary magnetic fields within a magnetic cloud (MC), and a sheath + MC were the causes of these two superstorms, respectively. Two different CME propagation models [Gopalswamy, N., Yashiro, S., Kaiser, M.L. et al. Predicting the 1-AU arrival times of coronal mass ejections. J. Geophys. Res. 106, 29207–29219, 2001; Gopalswamy, N.S., Lara, A., Manoharan, P.K. et al. An empirical model to predict the 1-AU arrival of interplanetary shocks. Adv. Space Res. 36, 2289–2294, 2005] were employed to attempt to identify the solar sources. It is found that the models identify several potential CMEs as possible sources for each of the superstorms. The two Gopalswamy et al. models give the possible sources for the first superstorm as CMEs on 2330 UT 4 November 2004 or on 1454 UT 5 November 2004. For the second superstorm, the possible solar source was a CME that on 0754 UT 5 November 2004 or one that occurred on 1206 UT 5 November 2004. We note that other propagation models sometimes agree and other times disagree with the above results. It is concluded that during high solar/interplanetary activity intervals such as this one, the exact solar source is difficult to identify. More refined propagation models are needed.     

Rainfall estimation over a Mediterranean region using a method based on various spectral parameters of SEVIRI-MSG

The ultimate objective of this paper is the estimation of rainfall over an area in Algeria using data from the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager). To achieve this aim, we use a new Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The satellite rainfall retrieval technique is based on various spectral parameters of SEVIRI that express microphysical and optical cloud properties. It uses a multispectral thresholding technique to distinguish between stratiform and convective clouds. This technique (CS-RADT) is applied to the complex situation of the Mediterranean climate of this region. The tests have been conducted during the rainy seasons of 2006/2007 and 2010/2011 where stratiform and convective precipitation is recorded. The developed scheme (CS-RADT) is calibrated by instantaneous meteorological radar data to determine thresholds, and then rain rates are assigned to each cloud type by using radar and rain gauge data. These calibration data are collocated with SEVIRI data in time and space.     

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

A point cloud deep neural network metamodel method for aerodynamic prediction

Aiming to reduce the high expense of 3-Dimensional (3D) aerodynamics numerical simulations and overcome the limitations of the traditional parametric learning methods, a point cloud deep learning non-parametric metamodel method is proposed in this paper. The 3D geometric data, corresponding to the object boundaries, are chosen as point clouds and a deep learning neural network metamodel fed by the point clouds is further established based on the PointNet architecture. This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities. With the proposed aerodynamic metamodel approach, the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain, which can maintain the boundary smoothness and allow the network to detect small changes between geometries. Moreover, the point clouds are easily accessible from 3D sensors. The point cloud deep learning neural network, which employs re-sampling technique, the spatial transformer network and the fully connected layer, is developed to predict the aerodynamic characteristics of 3D geometry. The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing. The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.