Study On Gansu Meteorological Scientific Data Sharing Platform And Application

The construction of scientific data sharing platform has a strong scientificsignificance and practical significance to maximize the effectiveness of data resourcesand provide to local governments in the decision-making services, meteorologicaloperation self-development, scientific research in earth science-related fields, localeconomic construction, and so on. The platform has a complete strategy for datastorage, which greatly enhancing the shared data resources accessibility by thecombination of distribution and orderly access to meet the multi-user concurrentaccess and traffic flow. At the same time, the platform follow data own internalrelationship to stress data links between the various types data and provide real datareliability for users at all levels.Based on meteorological data in Gansu Province as an example, the platformachieved the following results: Following regional and non-regional areas combined,integrated and dominant principle of combining factors, developed a number ofmeteorological data sets, including the northwest ground, agrological, meteorologicaldisasters, air radiation, the history climate proxy data. All five categories, contains atotal of 31 data sets, apply data quality control technology to ensure data quality. Theplatform largely used metadata technology and GIS technology to establish a basicplatform, which based on meteorological thematic data sets and built onbusiness-based distributed database platforms, to provide users with transparentaccess for structured data access and unstructured data access. Based on unifiedmetadata standard, the metadata system established by the platform provided anavigation mechanism for different sub-node and benefits the deployment ofmeta-data, which is rarely reported in China. The Platform used distributed datastorage, which bridge the contradictions of data distributed storage and concentrateaccess by proposed to unify metadata description for the Entity Data and established a coordination mechanism of various systems sub-node. The Technology and methodsof platform development has been introduced to the Hubei, Jiangxi, Xinjiang, Ningxiaand other provinces to promote the application of meteorological services. Theplatform is an important part of the scientific and technological base platform ofGansu Province and will provide rich content and reliable data quality for theearth-related fields of scientific research, government decision-making meteorologicalservices, public weather services, the national economic construction and localresearch and project construction. The paper's results, developed platform andsystems can be widely used in the construction of other resource sharing system forGansu Province and can play a leading role model.In this paper, a variety of related research is held by the sharing platform: First ofall, climatic regionalization of Gansu Province is carried out; and then a spatialinterpolation method (ISTDW) is applied to radar data for dealing with abnormal dataand absence data; At the same time, in-depth study of the multi-radar time-seriessimilarity matching problem and gives a similarity matching algorithm; Finally, someartificial intelligence algorithms, such as bionic optimization algorithm (PSO (PSO))algorithm, fish-swarm algorithm (AFSA) and the prediction algorithm (BP neuralnetwork, least squares support vector machine)), is studied. By the research, thefollowing results are made: a climatic regionalization program in Gansu Province isprovided; a well-designed ETL processes for space-time particle size consistency ofradar data and ground observation data is held out and perform good; the DIRE(dynamic incremental rule engine) to detect abnormal data and the ISTDWinterpolation method for data cleansing ensured the data for meteorological datawarehouse is "clean"; the multi-radar time-series similarity matching algorithm(SBMDTSM), deeply considered the abnormal data, sequence of deformation and theseason in terms of the impact of distance measure, given a formal model of thedistance measure and has been verified that the similarity SBMDTSM matchingalgorithm in false dismissals rate, the false alarms rate and accuracy rate are betterthan APCA and DWT algorithm. Artificial Fish Swarm Algorithm (AFSA) isproposed to choose the parameters of least squares support vector machine (LS-SVM) automatically in time series prediction. A novel hybrid evolutionary algorithm basedon AFSA and PSO, also referred to as AFSA-PSO-parallel-hybrid evolutionary(APPHE) algorithm, has been used in FNN training. Compared to FNN trained byLMBP algorithm, FNN training by the novel hybrid evolutionary algorithm showsatisfactory performance, converges quickly towards the optimal position, convergentaccuracy, high stability and can avoid overfitting in some extent. FNN training bythe novel method has been testified by using in Iris data classification and the resultsare much more accurate and stable than by Levenberg-Marquardt back-propagationalgorithm.