An algorithm to retrieve precipitation with synthetic aperture radar

This paper presents a new type of rainfall retrieval algorithm, called the model-oriented statistical and Volterra integration. It is a combination of the model-oriented statistical (MOS) and Volterra integral equation (VIE) approaches. The steps involved in this new algorithm can be briefly illustrated as follows. Firstly, information such as the start point and width of the rain is obtained through pre-analysis of the data received by synthetic aperture radar (SAR). Secondly, the VIE retrieval algorithm is employed over a short distance to obtain information on the shape of the rain. Finally, the rain rate can be calculated by using the MOS retrieval algorithm. Simulation results show that the proposed algorithm is effective and simple, and can lead to time savings of nearly 50% compared with MOS. An example of application of SAR data is also discussed, involving the retrieval of precipitation information over the South China Sea.     

青岛冷水团的消亡机理研究

本文基于多年月平均水温资料,分析了青岛冷水团的长消过程,并利用气候态月平均大气数据和数值模拟结果,探讨了青岛冷水团的消亡机理。结果表明,青岛冷水团3月出现,4月成型,5月最盛,6月减弱,7月消失;南黄海6-7月间偏南风的增强和温跃层以下反气旋涡的减弱是青岛冷水团消亡的动力机制,而海面净热通量的下传和水平热量的输入则是青岛冷水团消亡的热力机制。     

A multiobjective steepest descent method with applications to optimal well control

Multiobjective optimization deals with mathematical optimization problems where two or more objective functions (cost functions) are to be optimized (maximized or minimized) simultaneously. In most cases of interest, the objective functions are in conflict, i.e., there does not exist a decision (design) vector (vector of optimization variables) at which every objective function takes on its optimal value. The solution of a multiobjective problem is commonly defined as a Pareto front, and any decision vector which maps to a point on the Pareto front is said to be Pareto optimal. We present an original derivation of an analytical expression for the steepest descent direction for multiobjective optimization for the case of two objectives. This leads to an algorithm which can be applied to obtain Pareto optimal points or, equivalently, points on the Pareto front when the problem is the minimization of two conflicting objectives. The method is in effect a generalization of the steepest descent algorithm for minimizing a single objective function. The steepest-descent multiobjective optimization algorithm is applied to obtain optimal well controls for two example problems where the two conflicting objectives are the maximization of the life-cycle (long-term) net-present-value (NPV) and the maximization of the short-term NPV. The results strongly suggest the multiobjective steepest-descent (MOSD) algorithm is more efficient than competing multiobjective optimization algorithms.     

Two-dimensional landslide dynamic simulation based on a velocity-weakening friction law

Surprisingly, hypermobility (high velocity and long run-out) is a remarkable feature of large landslides and is still poorly understood. In this paper, a velocity-weakening friction law is incorporated into a depth-averaged landslide model for explaining the higher mobility mechanism of landslides. In order to improve the precision of the calculation, a coupled numerical method based on the finite volume method is proposed to solve the model equations. Finally, several numerical tests are performed to verify the stability of the algorithm and reliability of the model. The comparison between numerical results and experimental data indicates that the presented model can predict the movement of landslide accurately. Considering the effect of velocity-weakening friction law, the presented model can better reflect the hypermobility of landslide than the conventional Mohr–Coulomb friction model. This work shows that the application of a universal velocity-weakening friction law is effective in describing the hypermobility of landslide and predicting the extent of landslides.     

A public Cloud-based China’s Landslide Inventory Database (CsLID): development,zone, and spatiotemporal analysis for significant historical events, 1949-2011

Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.