Yu Yu, Lei Cao, Zhihua Ren, Yan Xu, Wei Feng, Licheng Zhao
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引用次数: 0
摘要
众包气象数据可为业务观测提供有益补充。然而,各方共享数据的意愿仍不明确。在此,我们开展了一项关于数据应用的调查,以研究参与众包观测的意愿。在21份回复中,71%的人表示仅靠自己的观测数据难以满足数据服务的要求,并表示愿意在某种框架下与其他各方交换数据;此外,90%的人表示愿意参与众包观测。研究结果表明,在某种程度上,众包的社会基础已经在中国建立。此外,还在中国南方广东省省会城市广州开展了降水监测案例研究。在对数据进行质量控制和校准后,将三个来源的每小时降水量测量数据进行合并,并在广州上空进行内插(基于合并数据的网格降水量被称为 COM 网格)。随后,利用累积降水量、降水强度和暴雨时数三个指标,将 COM 网格与仅基于中国气象局观测数据的网格数据进行比较。结果表明,对更多观测数据的需求可以从众包数据中获益,尤其是在不平坦的地形和地面站覆盖稀少的地区。
Crowdsourced meteorological data to supplement limited official sources: A survey and case study of precipitation monitoring in Guangzhou, China
Crowdsourced meteorological data may provide a useful supplement to operational observations. However, the willingness of various parties to share their data remains unclear. Here, a survey on data applications was carried out to investigate the willingness to participate in crowdsourcing observations. Of the 21 responses, 71% expressed difficulty in meeting the requirement of data services using only their own observations and revealed that they would be willing to exchange data with other parties under some framework; moreover, 90% expressed a willingness to participate in crowdsourcing observations. The findings suggest that in a way the social foundation of crowdsourcing has been established in China. Additionally, a case study on precipitation monitoring was performed in Guangzhou, the capital city of Guangdong Province, South China. Three sources of hourly measurements were combined after data quality control and calibration and interpolated over Guangzhou (gridded precipitation was based on combined data, and it is referred to as the COM grid). Subsequently, the COM grid was compared with the grid data based only on observations from the China Meteorological Administration using three indices, namely cumulative precipitation, precipitation intensity, and heavy rain hours. The results indicate that requirement for more observations could benefit from crowdsourced data, especially on uneven terrain and in regions covered by sparse surface stations.