张雷,徐宾,廖志宏,师春香,徐梅,孙玫玲. 2021. 全球多源海冰密集度融合资料研制试验[J]. 气象学报, 79(4):703-715, doi:10.11676/qxxb2021.040
全球多源海冰密集度融合资料研制试验
Development experiment of a global multi-source sea ice concentration fusion dataset
投稿时间:2020-09-30  修订日期:2021-04-23
DOI:10.11676/qxxb2021.040
中文关键词:  海冰密集度  融合  全球
英文关键词:Sea ice concentration  Merge  Global
基金项目:国家重点研发计划项目(2018YFC1506601)、国家自然科学基金项目(41806213)、天津市科技局科技重大专项与工程“雾霾天气对海事交通影响预报预警技术研究”(18ZXAQSF00130)
作者单位E-mail
张雷 国家气象信息中心北京100081
天津市气象信息中心天津300074
天津市海洋气象重点实验室天津300074 
 
徐宾 国家气象信息中心北京100081 xubin@cma.gov.cn 
廖志宏 国家气象信息中心北京100081  
师春香 国家气象信息中心北京100081  
徐梅 天津市气象信息中心天津300074
天津市海洋气象重点实验室天津300074 
 
孙玫玲 天津市海洋气象重点实验室天津300074
天津市气象服务中心天津300074 
 
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中文摘要:
      为了发展一套全球多源海冰密集度逐日融合资料,以欧洲气象卫星应用组织(EUMETSAT)海洋海冰应用中心(OSI SAF)海冰密集度数据、中国国家卫星气象中心(NSMC)的MWRI和VIRR全球海冰密集度数据、美国国家冰雪数据中心(NSIDC)的NISE海冰密集度数据、美国国家冰中心(NIC)的IMS北半球海冰数据为观测数据源,以ERA-Interim模式数据为背景场,采用以下方案开展融合试验。首先,对各数据源资料进行质量控制;其次,以OSI SAF海冰密集度数据为基准,采用概率密度函数(PDF)匹配方法订正其他卫星资料的系统误差;然后,根据订正后的误差生成超级观测场;最后,利用STMAS方法将超级观测场和作为背景场的ERA-Interim海冰密集度数据进行融合,生成全球逐日0.25°分辨率海冰密集度融合试验数据。通过与国际广泛使用的OISST、OSTIA海冰密集度数据对比,评估融合试验产品的质量。结果表明:融合方案中的PDF方法通过调整非基准资料的概率密度分布,实现非基准资料和基准资料概率密度分布一致,从而使3种海冰密集度卫星资料系统误差均显著减小;STMAS方法能够将超级观测场和背景场进行有效融合,生成融合试验产品;风云卫星数据的使用提高了融合数据生产的自主可控能力;同时,融合方案考虑了卫星数据源的时效性、获取的稳定性等因素。融合产品与OISST和OSTIA海冰密集度数据的空间分布在南、北极均高度吻合,相关系数均超过0.985,与OISST和OSTIA的偏差分别为-1.170%和0.276%,融合试验产品整体偏差介于两种资料之间,反映了试验产品系统误差较小的良好特性。可见,融合方案能够满足实时业务需要,融合试验产品具有较高的质量。
英文摘要:
      In order to develop a fusion dataset of global multi-source daily sea ice concentration, the following scheme is adopted to combine observations from various sources and model background field data. The observational data sources include the daily sea ice concentration analysis from OSI SAF (Ocean Sea Ice Application Center) EUMETSAT (European Meteorological Satellite Application Organization), the global sea ice concentration data of MWRI (Micro Wave Radiation Imager) and VIRR (Visible and Infrared Radiometer) from NSMC (National Satellite Meteorological Center) of China, the NISE (Near-Real-Time SSM/I EASE Grid Daily Global Ice Concentration and Snow Extent)sea ice concentration data of U.S. NSIDC (National Snow and Ice Data Center), the IMS (Interactive Multisensor Snow and Ice Mapping System) Northern Hemisphere sea ice data of U.S. NIC (National Ice Center). The model background field is the ERA-Interim sea ice concentration data. First, complete quality control is performed on all the datasets from various sources. Next, the sea ice concentration data of the OSI SAF is used as a reference, and the probability density function (PDF) matching method is used to correct systematic errors in other satellite data. The super observation is then generated based on adjusted error. Finally, the super observation and ERA-Interim sea ice concentration data (the background field) are combined using the STMAS (Space-Time Multiscale Analysis System) method to generate 1-year merged global daily sea ice concentration data with the resolution of 0.25°. The quality of the merged product is evaluated by comparing with the sea ice concentration data widely used internationally, e.g., the OISST (Optimum Interpolation Sea Surface Temperature) and OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis). The results show that the PDF method can make the probability density distribution of non-reference data consistent with that of reference data by adjusting the probability density distribution of non-reference data, and the adjusted systematic errors of the three sea ice concentration satellite datasets are significantly reduced in the merged solution. The STMAS method can effectively combine the super observation and the background to generate a merged product. The use of FY(FengYun ) Satellite data improves the autonomous and controllable ability of the fusion data. In addition, the timeliness of satellite data sources, the stability of acquisition and other factors are considered in the fusion scheme. The spatial distribution of the merged product is highly consistent with the OISST and OSTIA sea ice concentration data in the Arctic and Antarctic, and the correlation coefficients between them exceed 0.985. The deviations of the merged product from the OISST and OSTIA are -1.170% and 0.276%, respectively. The overall bias of the fusion experiment product is between those of the OISST and OSTIA, which reflects the good quality of the fusion product that shows small systematic errors. It can be seen that the fusion scheme can meet the operational need and the fusion experiment product has a high quality.
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