论文摘要
Assimilation of snow cover is an important method to improve the accuracy of snow simulation. However, the effects of snow assimilation are poor because satellite observed snow cover data contain erroneous information, such as cloud contamination. In this paper, an improved approach is proposed to reduce the effects of observational errors during assimilation of snow cover fraction acquired by the Fengyun-3(FY-3) satellite in northeastern China. A snow depth constraint was imposed on quality control of a snow depth product from a microwave radiation imager. The assimilation experiments were carried out before and after quality control(denoted as SCFDA and SCFDAWSD, respectively). The snow cover fraction results were evaluated against the Moderate Resolution Imaging Spectroradiometer(MODIS) snow cover products. When assimilating the snow cover fraction with the snow depth constraint(i.e., SCFDAWSD), substantially larger improvement was obtained than that without such a constraint/quality control(SCFDA), and the deviation and root mean square error of the snow cover fraction were significantly reduced.The assimilation performance was also evaluated against in-situ snow depth observations. The SCFDAWSD also showed greater improvements during the snow accumulation and snowmelt periods than the SCFDA. The SCFDAWSD improvements in woodland and shrubland were the most obvious. At different altitudes, the effects of the SCFDAWSD were basically equivalent, and the deeper the snow depth was, the better the effect. In addition, the SCFDAWSD method was found in close agreement with the observations during a sudden snowfall event.
论文目录
文章来源
类型: 期刊论文
作者: Shuai ZHANG,Chunxiang SHI,Runping SHEN,Jie WU
来源: Journal of Meteorological Research 2019年05期
年度: 2019
分类: 基础科学,工程科技Ⅱ辑
专业: 气象学,航空航天科学与工程
单位: School of Geographic Sciences, Nanjing University of Information Science & Technology,National Meteorological Information Center,China Meteorological Administration,Laboratory for Climate Studies,National Climate Center, China Meteorological Administration
基金: Supported by the National Natural Science Foundation of China(91437220),National Key Research and Development Program of China(2018YFC1506601),China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002)
分类号: P414.4
页码: 960-975
总页数: 16
文件大小: 6057K
下载量: 8
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