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| 地面自动站资料质量控制方案及应用 | |
| 引用本文： | 陶士伟,仲跻芹,徐枝芳,郝民.地面自动站资料质量控制方案及应用[J].高原气象,2009,28(5):1202-1209. |
| 作者姓名： | 陶士伟 仲跻芹 徐枝芳 郝民 |
| 作者单位： | 1. 中国气象局,北京城市气象研究所,北京,100089;中国气象局,国家气象中心,北京,100081 2. 中国气象局,北京城市气象研究所,北京,100089 3. 中国气象局,国家气象中心,北京,100081 |
| 基金项目： | 北京城市气象研究所的城市气象科学研究基金，国家自然科学基金，国际科技合作项目，国家重点基础研究发展规划(973计划) |
| 摘 要： | 为满足精细、 短时天气预报的需求， 我国地面自动站（AWS）观测系统的建设日趋完善。AWS资料在数值预报系统中没得到充分使用的原因之一是AWS观测资料的特殊性及数值预报系统缺少有效针对AWS观测资料的质量控制(QC)方案。本文AWS QC方案在参考国际先进的AWS QC方案基础上， 根据我国地面自动站的特点和数值天气预报模式对地面资料应用的要求设计。其目的是解决AWS观测资料在数值预报中应用的质量问题。本方案采用多种质量控制技术， 其中包括台站气候极值检查、 要素间一致性检查、 时间一致性检查、 持续性检查、 背景场一致性检查、 空间一致性检查、 综合决策算法、 自动统计评估反馈技术。本文从检查方法对错误资料的敏感度和确定性进行理论分析表明, 该方案具有更强的敏感度和确定性。将该方案应用于北京地区2006年8月AWS实际观测资料检验， 结果表明, 该方案能有效地识别观测资料中存在明显错误的资料， 有效地为地面自动站资料在数值模式中的应用提供客观质量依据。 |
| 关 键 词： | 地面自动站 质量控制 敏感度 观测误差 综合决策算法 |
Quality Control Schemes and Its Application to Automatic Surface Weather Observation System |
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| TAO Shi-wei,ZHONG Qi-qin,XU Zhi-fang,HAO Min.Quality Control Schemes and Its Application to Automatic Surface Weather Observation System[J].Plateau Meteorology,2009,28(5):1202-1209. | |
| Authors: | TAO Shi-wei ZHONG Qi-qin XU Zhi-fang HAO Min |
| Abstract: | With the enhancement of the meteorological observation system in China, the Automatic surface Weather observation System (AWS) is coming into the perfect condition. Local AWS network has been setup in many areas and much attention has been paid to the application of AWS data. Somehow, data quality turns to be one of the roadblock in the ways of AWS data application. In order to fulfill the function of AWS data better, a feasible observation data quality control module should be designed to make sure AWS data that used in the meteorological research and operation with a higher quality. The AWS QC module in this paper consult the advantage of other AWS QC module of advance countries and is brought forward consulting the characteristic of AWS and the requirement of the high resolution Numerical Weather Prediction(NWP) operational model. Many quality control techniques are integrated into the QC module, such as the station extreme value check, the interior consistency check, the consistence check with first guess, the temporal consistency check, the space consistency check, the decision-making arithmetic, the statistic and feedback and technique. The advantage of the AWS QC module have been show in theory from sensitivity and independency of the check methods on gross error. The AWS data quality control module has been applied into AWS data in Beijing area. Subjective verification result shows that wrong information in the observation can be distinguished effectively from the AWS data. |
| Keywords: | Intensified automatic surface weather observation station Quality control Sensitivity Observation error Complex decision making |
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