In recent years, meteorological data increases dramatically, and the amount of data has been TB-per-hour-level. The traditional relational database and file storage system have troubles in the massive data storage and management, thus large-scale and heterogeneous meteorological data cannot also be used effectively in meteorological business. Furthermore, it would be also difficult for scientific researchers to efficiently explore the huge amount of heterogeneous meteorological data. In order to tackle these problems, researchers have developed many types of distributed computing frameworks based on MapReduce and HBase, etc., which provide an effective way to exploit large-scale meteorological data. The distributed computing and storing techniques have been tested separately in applications of meteorology field. However, to our best knowledge, these techniques have not been carefully studied jointly. Therefore, a new 3D storm tracking method based on the combination of MapReduce and Hbase was studied by using a large amount of weather radar data accumulated in recent years. Moreover, based on the original Rest interface, a series of distributed service interfaces were implemented for exploring a variety of point, line and surface data. Compared with the performance of the standard single data storage and access interface based on Rest, the proposed method has better comprehensive performance, and the efficiency is improved about 100%. A practical application for tracking 3D storm in Zhujiang River urban agglomeration from 2007 to 2009 was used to further validate the performance of the proposed method.

When civil aviation passengers check in, various uncertainty problems exist in the baggage tag readers' number, position and angle. To solve the problems, the Dynamic Population-Double Fitness Particle Swarm Optimization (DPDF-PSO) algorithm was proposed. Firstly, the mathematical model of baggage tag detector was established, then it was transformed into an optimization problem; secondly, the optimization problem was solved by standard Particle Swarm Optimization (PSO) algorithm; finally, the standard PSO algorithm was improved in accordance with the model features. The simulation results show that compared with standard PSO algorithm, the simulation time of the DPDF-PSO algorithm reduced by 23.6%, the objective function value increased by 3.7%. DPDF-PSO algorithm overcomes the shortage of long simulation time and troublesome problem of optimal boundary solutions existed in standard PSO algorithm. Identity information can be read quickly and accurately by readers layout at a lower cost.