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| 天气预报分析型数据模型及生成 | |
| 引用本文： | 谭晓光,罗兵. 天气预报分析型数据模型及生成[J]. 应用气象学报, 2014, 25(1): 120-128 |
| 作者姓名： | 谭晓光 罗兵 |
| 作者单位： | 1.中国气象局北京城市气象研究所，北京 100089 |
| 基金项目： | 资助项目：公益性行业(气象)科研专项（GYHY201206031） |
| 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。 |
| 关 键 词： | 天气预报 数据仓库 分析型数据 天气系统识别 |
| 收稿时间： | 2013-02-18 |
Model and Generation of Weather Forecast Analytic Data |
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| Tan Xiaoguang and Luo Bing. Model and Generation of Weather Forecast Analytic Data[J]. Journal of Applied Meteorological Science, 2014, 25(1): 120-128 | |
| Authors: | Tan Xiaoguang and Luo Bing |
| Affiliation: | 1.Institute of Urban Meteorology, CMA, Beijing 1000892.National Meteorological Center, Beijing 100081 |
| Abstract: | 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 granularity data. Usually the meteorological data got in operational weather forecast is processed, clean and canonical. 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 spatial, 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, forecasters 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 region, statistics for given spatial or temporal partitions, identification of basic weather systems and identification 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 high/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 filtered field, with some arithmetics adopted to tread with multi-scale problems of meteorological field. At last the divided regions are measured to get area, extreme value, length, width, aspect ratio (width/length), geometry center, extreme data location, points of central line, including all attributes of SA, EA, TA and PA. If the aspect ratio is smaller than a threshold, the region will be identified as a trough or ridge, and the central line is the trough or ridge line.A knowledge base system with spatial fuzzy production rule is suggested for identifying weather conceptual models (e.g., cold front), and the rational process of this rule is described. 4 topological relations, several order relations, measure relations and their subjection functions are suggested. The conclusion of the rules is expanded to spatial objects with a result-spatial-object. |
| Keywords: | weather forecast data warehouse weather analytic data weather system identifying |
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