摘要
将原始数据转换为分析型数据,增强用户对海量数据的分析能力,是数据仓库技术最核心、最有价值的思想,也是数据仓库在气象领域应用的基础。该文针对天气预报领域数据空间性、瞬变性、物理性和多尺度性等特点,提出了五元组描述的天气预报分析型数据概念模型;总结了生成分析型数据的固定区域统计、划分区域统计、基本天气系统识别和天气学概念模型识别4种聚集变换,并对其关键技术进行了讨论。提出了基本天气系统自动识别的滤波-划分-测量算法,探讨了针对气象数据特点的模糊空间关系,定义了进行天气学概念模型识别的空间模糊产生式规则,并针对空间数据给出了定位条件等扩展。
To solve the problem of ＂information exploration＂ in operational weather forecast, building a data warehouse to help forecaster＇s analysis is necessary. The key and most valuable idea is to change raw data to analytic data, include extracting useful data, making data clean, and aggregating data to rough granu- larity data. Usually the meteorological data got in operational weather forecast is processed, clean and ca- nonical. So the main process is ＂aggregation＂ to concentrate the weather information to fewer data which have clear physical meaning. A conceptual model of weather analytic data is suggested with a pentagon tuple considering the spa- tial, transitional, physical and multi-scale natures of meteorological data. The pentagon tuple refers to ID （identification）, SA （spatial attributes）, EA （entity attributes）, TA （time attributes） and PA （physical attributes）, including several detailed attributes set each. Although meteorological data is field data, fore- casters usually use spatial object data to analyze the weather systems. So the main work of changing raw data to analytic data is identifying spatial objects from field data. Four aggregations arithmetics to change raw data to analytic data are suggested. Statistics for fixed re- gion, statistics for given spatial or temporal partitions, identification of basic weather systems and identifi- cation of weather conceptual models. The former two are relatively simple statistics, while the latter two are complex for mutative spatial object and they are discussed in detail. Basic weather systems include region of high/low, center of l^igh/low and trough/ridge in a data field. A filtering-dividing-measuring arithmetic is suggested. Filtered with a Mexican-hat function, the trough/ ridge become high/low region and easier to identify, and then the high/low region are divided from the fil- tered field, with some arithmetics adopted to tread with multi-scale problems of meteorological field. At last the divided regions are measure
出处
《应用气象学报》
CSCD
北大核心
2014年第1期120-128,共9页
Journal of Applied Meteorological Science
基金
公益性行业(气象)科研专项(GYHY201206031)
关键词
天气预报
数据仓库
分析型数据
天气系统识别
weather forecast~ data warehouse~ weather analytic data~ weather system identifying
